/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_7030.py:464: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( 1.22.4 1.4.1 aaindex_df contains non-numerical data Total no. of non-numerial columns: 2 Selecting numerical data only PASS: successfully selected numerical columns only for aaindex_df Now checking for NA in the remaining aaindex_cols Counting aaindex_df cols with NA ncols with NA: 4 columns Dropping these... Original ncols: 127 Revised df ncols: 123 Checking NA in revised df... PASS: cols with NA successfully dropped from aaindex_df Proceeding with combining aa_df with other features_df PASS: ncols match Expected ncols: 123 Got: 123 Total no. of columns in clean aa_df: 123 Proceeding to merge, expected nrows in merged_df: 424 PASS: my_features_df and aa_df successfully combined nrows: 424 ncols: 265 count of NULL values before imputation or_mychisq 102 log10_or_mychisq 102 dtype: int64 count of NULL values AFTER imputation mutationinformation 0 or_rawI 0 logorI 0 dtype: int64 PASS: OR values imputed, data ready for ML Total no. of features for aaindex: 123 PASS: x_features has no target variable No. of columns for x_features: 172 PASS: ML data with input features, training and test generated... Total no. of input features: 172 --------No. of numerical features: 166 --------No. of categorical features: 6 Total no. of evolutionary features: 3 Total no. of stability features: 28 --------Common stabilty cols: 5 --------Foldx cols: 23 Total no. of affinity features: 3 --------Common affinity cols: 3 --------Gene specific affinity cols: 0 Total no. of residue level features: 132 --------AA index cols: 123 --------Residue Prop cols: 3 --------AA change Prop cols: 6 Total no. of genomic features: 6 --------MAF+OR cols: 2 --------Lineage cols: 4 --------Other cols: 0 ------------------------------------------------------------- Successfully split data: ALL features actual values: training set imputed values: blind test set Total data size: 185 Train data size: (123, 172) y_train numbers: Counter({1: 76, 0: 47}) Test data size: (62, 172) y_test_numbers: Counter({1: 38, 0: 24}) y_train ratio: 0.618421052631579 y_test ratio: 0.631578947368421 ------------------------------------------------------------- index: 0 ind: 1 Mask count check: True Original Data Counter({1: 76, 0: 47}) Data dim: (123, 172) Simple Random OverSampling Counter({0: 76, 1: 76}) (152, 172) Simple Random UnderSampling Counter({0: 47, 1: 47}) (94, 172) Simple Combined Over and UnderSampling Counter({0: 76, 1: 76}) (152, 172) SMOTE_NC OverSampling Counter({0: 76, 1: 76}) (152, 172) ##################################################################### Running ML analysis: feature groups Gene name: pncA Drug name: pyrazinamide Output directory: /home/tanu/git/Data/pyrazinamide/output/ml/tts_7030/ ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( key: fit_time value: [0.02845812 0.03014469 0.02878141 0.0267849 0.02630472 0.02681899 0.02665186 0.06223321 0.04837632 0.02695227] mean value: 0.033150649070739745 key: score_time value: [0.01202369 0.0115416 0.01154542 0.01147985 0.01147604 0.01139736 0.0114212 0.01349187 0.012362 0.01165318] mean value: 0.01183922290802002 key: test_mcc value: [ 0.35 0.50069396 -0.05773503 0.29277002 0.16903085 -0.09759001 0.29277002 0.40824829 0.25 0.47809144] mean value: 0.25862795575586206 key: train_mcc value: [0.8456883 0.84516165 0.84761434 0.82751593 0.86547483 0.84833359 0.86551251 0.81075196 0.84998189 0.88578234] mean value: 0.849181735147331 key: test_fscore value: [0.75 0.82352941 0.66666667 0.75 0.61538462 0.625 0.75 0.82352941 0.75 0.8 ] mean value: 0.7354110105580693 key: train_fscore value: [0.94285714 0.94202899 0.94366197 0.93706294 0.94964029 0.94444444 0.95035461 0.92957746 0.94366197 0.95652174] mean value: 0.9439811555151773 key: test_precision value: [0.75 0.77777778 0.6 0.66666667 0.66666667 0.55555556 0.66666667 0.77777778 0.75 0.85714286] mean value: 0.7068253968253968 key: train_precision value: [0.91666667 0.92857143 0.90540541 0.90540541 0.94285714 0.90666667 0.93055556 0.89189189 0.90540541 0.94285714] mean value: 0.9176282711282712 key: test_recall value: [0.75 0.875 0.75 0.85714286 0.57142857 0.71428571 0.85714286 0.875 0.75 0.75 ] mean value: 0.775 key: train_recall value: [0.97058824 0.95588235 0.98529412 0.97101449 0.95652174 0.98550725 0.97101449 0.97058824 0.98529412 0.97058824] mean value: 0.9722293265132139 key: test_accuracy value: [0.69230769 0.76923077 0.53846154 0.66666667 0.58333333 0.5 0.66666667 0.75 0.66666667 0.75 ] mean value: 0.6583333333333334 key: train_accuracy value: [0.92727273 0.92727273 0.92727273 0.91891892 0.93693694 0.92792793 0.93693694 0.90990991 0.92792793 0.94594595] mean value: 0.9286322686322686 key: test_roc_auc value: [0.675 0.7375 0.475 0.62857143 0.58571429 0.45714286 0.62857143 0.6875 0.625 0.75 ] mean value: 0.625 key: train_roc_auc value: [0.91386555 0.91841737 0.90931373 0.90217391 0.93064182 0.90942029 0.92598344 0.89227086 0.91125171 0.93878249] mean value: 0.9152121161912694 key: test_jcc value: [0.6 0.7 0.5 0.6 0.44444444 0.45454545 0.6 0.7 0.6 0.66666667] mean value: 0.5865656565656565 key: train_jcc value: [0.89189189 0.89041096 0.89333333 0.88157895 0.90410959 0.89473684 0.90540541 0.86842105 0.89333333 0.91666667] mean value: 0.8939888020681099 key: TN value: 22 mean value: 22.0 key: FP value: 17 mean value: 17.0 key: FN value: 25 mean value: 25.0 key: TP value: 59 mean value: 59.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.66 Accuracy on Blind test: 0.84 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.78238106 0.58068442 0.60166001 0.70356202 0.56532955 0.60268497 0.59112096 0.69404554 0.58551407 0.58897471] mean value: 0.6295957326889038 key: score_time value: [0.01313138 0.01312971 0.01340914 0.01305056 0.01307988 0.0131216 0.01315808 0.01324868 0.01174593 0.01343203] mean value: 0.013050699234008789 key: test_mcc value: [0.35 0.84327404 0.41475753 0.83666003 0.31428571 0.52915026 0.47809144 0. 0.70710678 0.625 ] mean value: 0.5098325801667707 key: train_mcc value: [0.98100984 1. 0.98100984 0.98111593 1. 1. 1. 1. 0.92552101 1. ] mean value: 0.9868656621244465 key: test_fscore value: [0.75 0.94117647 0.71428571 0.93333333 0.71428571 0.82352941 0.8 0.70588235 0.85714286 0.875 ] mean value: 0.8114635854341735 key: train_fscore value: [0.99259259 1. 0.99259259 0.99270073 1. 1. 1. 1. 0.97142857 1. ] mean value: 0.9949314486540765 key: test_precision value: [0.75 0.88888889 0.83333333 0.875 0.71428571 0.7 0.75 0.66666667 1. 0.875 ] mean value: 0.8053174603174604 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 0.94444444 1. ] mean value: 0.9944444444444445 key: test_recall value: [0.75 1. 0.625 1. 0.71428571 1. 0.85714286 0.75 0.75 0.875 ] mean value: 0.8321428571428571 key: train_recall value: [0.98529412 1. 0.98529412 0.98550725 1. 1. 1. 1. 1. 1. ] mean value: 0.9956095481670928 key: test_accuracy value: [0.69230769 0.92307692 0.69230769 0.91666667 0.66666667 0.75 0.75 0.58333333 0.83333333 0.83333333] mean value: 0.764102564102564 key: train_accuracy value: [0.99090909 1. 0.99090909 0.99099099 1. 1. 1. 1. 0.96396396 1. ] mean value: 0.9936773136773137 key: test_roc_auc value: [0.675 0.9 0.7125 0.9 0.65714286 0.7 0.72857143 0.5 0.875 0.8125 ] mean value: 0.7460714285714285 key: train_roc_auc value: [0.99264706 1. 0.99264706 0.99275362 1. 1. 1. 1. 0.95348837 1. ] mean value: 0.9931536112928487 key: test_jcc value: [0.6 0.88888889 0.55555556 0.875 0.55555556 0.7 0.66666667 0.54545455 0.75 0.77777778] mean value: 0.691489898989899 key: train_jcc value: [0.98529412 1. 0.98529412 0.98550725 1. 1. 1. 1. 0.94444444 1. ] mean value: 0.9900539926115373 key: TN value: 31 mean value: 31.0 key: FP value: 13 mean value: 13.0 key: FN value: 16 mean value: 16.0 key: TP value: 63 mean value: 63.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.76 Accuracy on Blind test: 0.89 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01180434 0.01156044 0.00869417 0.00840425 0.00925708 0.00883102 0.00905108 0.00831056 0.00855541 0.00827074] mean value: 0.009273910522460937 key: score_time value: [0.01157045 0.01013303 0.00877929 0.0085907 0.00855446 0.00904465 0.00895548 0.00870943 0.00923038 0.00833988] mean value: 0.009190773963928223 key: test_mcc value: [ 0.53674504 0.21957752 0.05773503 0.50709255 0.02857143 0.02857143 -0.2548236 0. -0.23904572 0.15811388] mean value: 0.10425375588567705 key: train_mcc value: [0.46345451 0.44053439 0.32281987 0.44236993 0.48145494 0.51886112 0.46689953 0.46947169 0.49865546 0.55055954] mean value: 0.46550809707958835 key: test_fscore value: [0.8 0.66666667 0.36363636 0.76923077 0.5 0.5 0.66666667 0.36363636 0.53333333 0.77777778] mean value: 0.594094794094794 key: train_fscore value: [0.75806452 0.752 0.63063063 0.69026549 0.77165354 0.8030303 0.82352941 0.7751938 0.77777778 0.80314961] mean value: 0.7585295074114025 key: test_precision value: [0.85714286 0.71428571 0.66666667 0.83333333 0.6 0.6 0.54545455 0.66666667 0.57142857 0.7 ] mean value: 0.6754978354978355 key: train_precision value: [0.83928571 0.8245614 0.81395349 0.88636364 0.84482759 0.84126984 0.75 0.81967213 0.84482759 0.86440678] mean value: 0.8329168167022407 key: test_recall value: [0.75 0.625 0.25 0.71428571 0.42857143 0.42857143 0.85714286 0.25 0.5 0.875 ] mean value: 0.5678571428571428 key: train_recall value: [0.69117647 0.69117647 0.51470588 0.56521739 0.71014493 0.76811594 0.91304348 0.73529412 0.72058824 0.75 ] mean value: 0.7059462915601022 key: test_accuracy value: [0.76923077 0.61538462 0.46153846 0.75 0.5 0.5 0.5 0.41666667 0.41666667 0.66666667] mean value: 0.5596153846153846 key: train_accuracy value: [0.72727273 0.71818182 0.62727273 0.68468468 0.73873874 0.76576577 0.75675676 0.73873874 0.74774775 0.77477477] mean value: 0.727993447993448 key: test_roc_auc value: [0.775 0.6125 0.525 0.75714286 0.51428571 0.51428571 0.42857143 0.5 0.375 0.5625 ] mean value: 0.5564285714285715 key: train_roc_auc value: [0.73844538 0.72654062 0.66211485 0.72308489 0.74792961 0.76501035 0.70652174 0.73974008 0.75564295 0.78197674] mean value: 0.7347007205308816 key: test_jcc value: [0.66666667 0.5 0.22222222 0.625 0.33333333 0.33333333 0.5 0.22222222 0.36363636 0.63636364] mean value: 0.44027777777777777 key: train_jcc value: [0.61038961 0.6025641 0.46052632 0.52702703 0.62820513 0.67088608 0.7 0.63291139 0.63636364 0.67105263] mean value: 0.6139925920272356 key: TN value: 26 mean value: 26.0 key: FP value: 33 mean value: 33.0 key: FN value: 21 mean value: 21.0 key: TP value: 43 mean value: 43.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.32 Accuracy on Blind test: 0.66 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00860023 0.00946689 0.00877619 0.00849962 0.0084548 0.00853848 0.00842428 0.00853515 0.00877523 0.00882339] mean value: 0.008689427375793457 key: score_time value: [0.0086565 0.00921583 0.00864005 0.00907683 0.00884461 0.00913835 0.00865912 0.00881648 0.00896597 0.00869155] mean value: 0.008870530128479003 key: test_mcc value: [ 0.73192505 -0.05773503 -0.1844662 0.29277002 0.11952286 -0.37142857 -0.65714286 -0.125 -0.23904572 0. ] mean value: -0.04906004366710262 key: train_mcc value: [0.32909498 0.36744292 0.47177844 0.40914183 0.38716356 0.47639996 0.38168559 0.46853625 0.45724863 0.46429355] mean value: 0.4212785715783386 key: test_fscore value: [0.85714286 0.66666667 0.58823529 0.75 0.66666667 0.42857143 0.16666667 0.625 0.53333333 0.70588235] mean value: 0.5988165266106443 key: train_fscore value: [0.74074074 0.75555556 0.78787879 0.77372263 0.76811594 0.80851064 0.77142857 0.79411765 0.78195489 0.79710145] mean value: 0.777912684721997 key: test_precision value: [1. 0.6 0.55555556 0.66666667 0.625 0.42857143 0.2 0.625 0.57142857 0.66666667] mean value: 0.5938888888888889 key: train_precision value: [0.74626866 0.76119403 0.8125 0.77941176 0.76811594 0.79166667 0.76056338 0.79411765 0.8 0.78571429] mean value: 0.7799552373023497 key: test_recall value: [0.75 0.75 0.625 0.85714286 0.71428571 0.42857143 0.14285714 0.625 0.5 0.75 ] mean value: 0.6142857142857142 key: train_recall value: [0.73529412 0.75 0.76470588 0.76811594 0.76811594 0.82608696 0.7826087 0.79411765 0.76470588 0.80882353] mean value: 0.7762574595055413 key: test_accuracy value: [0.84615385 0.53846154 0.46153846 0.66666667 0.58333333 0.33333333 0.16666667 0.5 0.41666667 0.58333333] mean value: 0.5096153846153847 key: train_accuracy value: [0.68181818 0.7 0.74545455 0.72072072 0.71171171 0.75675676 0.71171171 0.74774775 0.73873874 0.74774775] mean value: 0.7262407862407863 key: test_roc_auc value: [0.875 0.475 0.4125 0.62857143 0.55714286 0.31428571 0.17142857 0.4375 0.375 0.5 ] mean value: 0.4746428571428572 key: train_roc_auc value: [0.66526611 0.68452381 0.7394958 0.70548654 0.69358178 0.73447205 0.6889234 0.73426813 0.73119015 0.72999316] mean value: 0.7107200918790164 key: test_jcc value: [0.75 0.5 0.41666667 0.6 0.5 0.27272727 0.09090909 0.45454545 0.36363636 0.54545455] mean value: 0.4493939393939394 key: train_jcc value: [0.58823529 0.60714286 0.65 0.63095238 0.62352941 0.67857143 0.62790698 0.65853659 0.64197531 0.6626506 ] mean value: 0.6369500845710674 key: TN value: 16 mean value: 16.0 key: FP value: 29 mean value: 29.0 key: FN value: 31 mean value: 31.0 key: TP value: 47 mean value: 47.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.38 Accuracy on Blind test: 0.71 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00872731 0.0109899 0.00829268 0.00792122 0.00795484 0.00792432 0.00792336 0.00785565 0.00792909 0.00805664] mean value: 0.008357501029968262 key: score_time value: [0.04392147 0.0374155 0.0092802 0.00911021 0.00910616 0.00900841 0.00935888 0.00906873 0.00905585 0.00903249] mean value: 0.015435791015625 key: test_mcc value: [ 0.21957752 0. -0.05773503 0.07559289 -0.37796447 -0.23904572 -0.2548236 -0.125 -0.31622777 0.40824829] mean value: -0.06673778820515909 key: train_mcc value: [0.26021759 0.24010945 0.28480141 0.31220998 0.29084942 0.31220998 0.40186078 0.32350485 0.30458868 0.27692451] mean value: 0.30072766582878036 key: test_fscore value: [0.66666667 0.76190476 0.66666667 0.70588235 0.58823529 0.53333333 0.66666667 0.625 0.66666667 0.82352941] mean value: 0.6704551820728291 key: train_fscore value: [0.7721519 0.76129032 0.77707006 0.78205128 0.77419355 0.78205128 0.80519481 0.78205128 0.76821192 0.7721519 ] mean value: 0.7776418304008816 key: test_precision value: [0.71428571 0.61538462 0.6 0.6 0.5 0.5 0.54545455 0.625 0.6 0.77777778] mean value: 0.6077902652902651 key: train_precision value: [0.67777778 0.67816092 0.68539326 0.70114943 0.69767442 0.70114943 0.72941176 0.69318182 0.69879518 0.67777778] mean value: 0.6940471766312707 key: test_recall value: [0.625 1. 0.75 0.85714286 0.71428571 0.57142857 0.85714286 0.625 0.75 0.875 ] mean value: 0.7625 key: train_recall value: [0.89705882 0.86764706 0.89705882 0.88405797 0.86956522 0.88405797 0.89855072 0.89705882 0.85294118 0.89705882] mean value: 0.8845055413469736 key: test_accuracy value: [0.61538462 0.61538462 0.53846154 0.58333333 0.41666667 0.41666667 0.5 0.5 0.5 0.75 ] mean value: 0.5435897435897437 key: train_accuracy value: [0.67272727 0.66363636 0.68181818 0.69369369 0.68468468 0.69369369 0.72972973 0.69369369 0.68468468 0.67567568] mean value: 0.6874037674037674 key: test_roc_auc value: [0.6125 0.5 0.475 0.52857143 0.35714286 0.38571429 0.42857143 0.4375 0.375 0.6875 ] mean value: 0.47875000000000006 key: train_roc_auc value: [0.60329132 0.6004902 0.61519608 0.63250518 0.6252588 0.63250518 0.67546584 0.63457592 0.63577291 0.61132011] mean value: 0.6266381527332874 key: test_jcc value: [0.5 0.61538462 0.5 0.54545455 0.41666667 0.36363636 0.5 0.45454545 0.5 0.7 ] mean value: 0.5095687645687647 key: train_jcc value: [0.62886598 0.61458333 0.63541667 0.64210526 0.63157895 0.64210526 0.67391304 0.64210526 0.62365591 0.62886598] mean value: 0.6363195653061748 key: TN value: 9 mean value: 9.0 key: FP value: 18 mean value: 18.0 key: FN value: 38 mean value: 38.0 key: TP value: 58 mean value: 58.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.14 Accuracy on Blind test: 0.61 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.00947833 0.00908899 0.00898314 0.0089705 0.00936913 0.00897479 0.00902367 0.00896096 0.0091517 0.00906444] mean value: 0.00910656452178955 key: score_time value: [0.00871348 0.00849056 0.00855875 0.00846887 0.00866127 0.00845981 0.00851274 0.00849438 0.0084672 0.00849605] mean value: 0.008532309532165527 key: test_mcc value: [ 0.53935989 0.36514837 -0.05773503 0. -0.2548236 -0.2548236 -0.2548236 -0.21320072 -0.21320072 0. ] mean value: -0.03440989851459174 key: train_mcc value: [0.40237391 0.44525356 0.56282341 0.44624455 0.46682976 0.60088893 0.48694291 0.52238476 0.5347869 0.41707732] mean value: 0.4885606008648235 key: test_fscore value: [0.84210526 0.8 0.66666667 0.73684211 0.66666667 0.66666667 0.66666667 0.73684211 0.73684211 0.8 ] mean value: 0.7319298245614034 key: train_fscore value: [0.80952381 0.81927711 0.85 0.82142857 0.82634731 0.8625 0.8313253 0.8375 0.83950617 0.80952381] mean value: 0.8306932078343474 key: test_precision value: [0.72727273 0.66666667 0.6 0.58333333 0.54545455 0.54545455 0.54545455 0.63636364 0.63636364 0.66666667] mean value: 0.6153030303030304 key: train_precision value: [0.68 0.69387755 0.73913043 0.6969697 0.70408163 0.75824176 0.71134021 0.72826087 0.72340426 0.68 ] mean value: 0.7115306404737467 key: test_recall value: [1. 1. 0.75 1. 0.85714286 0.85714286 0.85714286 0.875 0.875 1. ] mean value: 0.9071428571428571 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 0.98529412 1. 1. ] mean value: 0.9985294117647058 key: test_accuracy value: [0.76923077 0.69230769 0.53846154 0.58333333 0.5 0.5 0.5 0.58333333 0.58333333 0.66666667] mean value: 0.5916666666666667 key: train_accuracy value: [0.70909091 0.72727273 0.78181818 0.72972973 0.73873874 0.8018018 0.74774775 0.76576577 0.76576577 0.71171171] mean value: 0.747944307944308 key: test_roc_auc value: [0.7 0.6 0.475 0.5 0.42857143 0.42857143 0.42857143 0.4375 0.4375 0.5 ] mean value: 0.4935714285714285 key: train_roc_auc value: [0.61904762 0.64285714 0.71428571 0.64285714 0.6547619 0.73809524 0.66666667 0.70194938 0.69767442 0.62790698] mean value: 0.6706102208325191 key: test_jcc value: [0.72727273 0.66666667 0.5 0.58333333 0.5 0.5 0.5 0.58333333 0.58333333 0.66666667] mean value: 0.581060606060606 key: train_jcc value: [0.68 0.69387755 0.73913043 0.6969697 0.70408163 0.75824176 0.71134021 0.72043011 0.72340426 0.68 ] mean value: 0.7107475642699131 key: TN value: 4 mean value: 4.0 key: FP value: 7 mean value: 7.0 key: FN value: 43 mean value: 43.0 key: TP value: 69 mean value: 69.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.2 Accuracy on Blind test: 0.65 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.49486423 0.51623654 0.47505784 0.67788649 0.49271965 0.52207398 0.52219319 0.61589813 0.51179814 0.54275203] mean value: 0.5371480226516724 key: score_time value: [0.01192236 0.01202393 0.01187253 0.01178312 0.01180911 0.01181746 0.01180387 0.01184916 0.01177979 0.01179743] mean value: 0.011845874786376952 key: test_mcc value: [ 0.15811388 0.21957752 0.35 0.52915026 -0.37142857 -0.09759001 0.47809144 0.40824829 -0.125 0.35355339] mean value: 0.19027162077022267 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.70588235 0.66666667 0.75 0.82352941 0.42857143 0.625 0.8 0.82352941 0.625 0.71428571] mean value: 0.6962464985994398 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.71428571 0.75 0.7 0.42857143 0.55555556 0.75 0.77777778 0.625 0.83333333] mean value: 0.6801190476190475 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.625 0.75 1. 0.42857143 0.71428571 0.85714286 0.875 0.625 0.625 ] mean value: 0.725 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.61538462 0.61538462 0.69230769 0.75 0.33333333 0.5 0.75 0.75 0.5 0.66666667] mean value: 0.6173076923076923 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.575 0.6125 0.675 0.7 0.31428571 0.45714286 0.72857143 0.6875 0.4375 0.6875 ] mean value: 0.5875 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.54545455 0.5 0.6 0.7 0.27272727 0.45454545 0.66666667 0.7 0.45454545 0.55555556] mean value: 0.5449494949494949 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 21 mean value: 21.0 key: FP value: 21 mean value: 21.0 key: FN value: 26 mean value: 26.0 key: TP value: 55 mean value: 55.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.55 Accuracy on Blind test: 0.79 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01489496 0.01408672 0.01056743 0.01056862 0.01049972 0.01067162 0.01093578 0.01076221 0.01055264 0.01062989] mean value: 0.011416959762573241 key: score_time value: [0.01156378 0.00994086 0.00843978 0.00833082 0.00827193 0.0083282 0.00828743 0.00835609 0.00836277 0.00829649] mean value: 0.008817815780639648 key: test_mcc value: [0.85391256 0.675 0.85391256 0.84515425 0.65714286 0.83666003 0.65714286 0.625 1. 0.83666003] mean value: 0.7840585149742315 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.93333333 0.875 0.93333333 0.92307692 0.85714286 0.93333333 0.85714286 0.875 1. 0.93333333] mean value: 0.9120695970695971 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.875 1. 1. 0.85714286 0.875 0.85714286 0.875 1. 1. ] mean value: 0.9339285714285716 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.875 0.875 0.875 0.85714286 0.85714286 1. 0.85714286 0.875 1. 0.875 ] mean value: 0.8946428571428571 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.92307692 0.84615385 0.92307692 0.91666667 0.83333333 0.91666667 0.83333333 0.83333333 1. 0.91666667] mean value: 0.8942307692307692 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9375 0.8375 0.9375 0.92857143 0.82857143 0.9 0.82857143 0.8125 1. 0.9375 ] mean value: 0.8948214285714287 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.875 0.77777778 0.875 0.85714286 0.75 0.875 0.75 0.77777778 1. 0.875 ] mean value: 0.8412698412698413 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 42 mean value: 42.0 key: FP value: 8 mean value: 8.0 key: FN value: 5 mean value: 5.0 key: TP value: 68 mean value: 68.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.79 Accuracy on Blind test: 0.9 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.08687162 0.08488417 0.08471727 0.08456445 0.08452892 0.08550549 0.08529496 0.08598661 0.08636308 0.08553123] mean value: 0.08542478084564209 key: score_time value: [0.01668167 0.01676798 0.01683354 0.01664472 0.01674986 0.01668262 0.01673627 0.01670599 0.0166409 0.01674151] mean value: 0.016718506813049316 key: test_mcc value: [ 0.31754265 0.21957752 0.21957752 0.29277002 -0.48795004 -0.09759001 0.07559289 0.25 0.15811388 0.47809144] mean value: 0.1425725880340596 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.77777778 0.66666667 0.66666667 0.75 0.5 0.625 0.70588235 0.75 0.77777778 0.8 ] mean value: 0.7019771241830065 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.7 0.71428571 0.71428571 0.66666667 0.44444444 0.55555556 0.6 0.75 0.7 0.85714286] mean value: 0.6702380952380953 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.875 0.625 0.625 0.85714286 0.57142857 0.71428571 0.85714286 0.75 0.875 0.75 ] mean value: 0.75 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.69230769 0.61538462 0.61538462 0.66666667 0.33333333 0.5 0.58333333 0.66666667 0.66666667 0.75 ] mean value: 0.6089743589743589 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.6375 0.6125 0.6125 0.62857143 0.28571429 0.45714286 0.52857143 0.625 0.5625 0.75 ] mean value: 0.5700000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.63636364 0.5 0.5 0.6 0.33333333 0.45454545 0.54545455 0.6 0.63636364 0.66666667] mean value: 0.5472727272727274 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 18 mean value: 18.0 key: FP value: 19 mean value: 19.0 key: FN value: 29 mean value: 29.0 key: TP value: 57 mean value: 57.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.45 Accuracy on Blind test: 0.74 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00829029 0.00823355 0.00817823 0.00814819 0.00815415 0.00818539 0.00818944 0.00816822 0.00817585 0.00820303] mean value: 0.008192634582519532 key: score_time value: [0.00814843 0.00815177 0.00821733 0.00820231 0.00818133 0.00818706 0.0082128 0.0082233 0.00823975 0.00820589] mean value: 0.00819699764251709 key: test_mcc value: [-0.21957752 -0.05773503 0.35 0.37142857 0.11952286 -0.5976143 -0.02857143 0.47809144 0.15811388 0. ] mean value: 0.05736584825331793 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.42857143 0.66666667 0.75 0.66666667 0.66666667 0.4 0.57142857 0.8 0.77777778 0.36363636] mean value: 0.6091414141414141 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.6 0.75 0.8 0.625 0.375 0.57142857 0.85714286 0.7 0.66666667] mean value: 0.6445238095238096 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.375 0.75 0.75 0.57142857 0.71428571 0.42857143 0.57142857 0.75 0.875 0.25 ] mean value: 0.6035714285714285 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.38461538 0.53846154 0.69230769 0.66666667 0.58333333 0.25 0.5 0.75 0.66666667 0.41666667] mean value: 0.5448717948717949 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.3875 0.475 0.675 0.68571429 0.55714286 0.21428571 0.48571429 0.75 0.5625 0.5 ] mean value: 0.5292857142857142 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.27272727 0.5 0.6 0.5 0.5 0.25 0.4 0.66666667 0.63636364 0.22222222] mean value: 0.45479797979797987 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 21 mean value: 21.0 key: FP value: 30 mean value: 30.0 key: FN value: 26 mean value: 26.0 key: TP value: 46 mean value: 46.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.05 Accuracy on Blind test: 0.55 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.09546304 1.08743787 1.08374643 1.14095879 1.08425879 1.08838081 1.08451486 1.08428717 1.08518529 1.09441781]/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( mean value: 1.0928650856018067 key: score_time value: [0.09162807 0.09519958 0.08684635 0.08672667 0.08648539 0.08686352 0.09133315 0.08665919 0.08701229 0.08668303] mean value: 0.08854372501373291 key: test_mcc value: [0.50069396 0.35 0.53674504 0.68313005 0.47809144 0.29277002 0.29277002 0.47809144 0.625 0.40824829] mean value: 0.4645540275746158 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.82352941 0.75 0.8 0.875 0.8 0.75 0.75 0.8 0.875 0.82352941] mean value: 0.8047058823529412 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.77777778 0.75 0.85714286 0.77777778 0.75 0.66666667 0.66666667 0.85714286 0.875 0.77777778] mean value: 0.775595238095238 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.875 0.75 0.75 1. 0.85714286 0.85714286 0.85714286 0.75 0.875 0.875 ] mean value: 0.8446428571428571 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.76923077 0.69230769 0.76923077 0.83333333 0.75 0.66666667 0.66666667 0.75 0.83333333 0.75 ] mean value: 0.7480769230769231 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.7375 0.675 0.775 0.8 0.72857143 0.62857143 0.62857143 0.75 0.8125 0.6875 ] mean value: 0.7223214285714287 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.7 0.6 0.66666667 0.77777778 0.66666667 0.6 0.6 0.66666667 0.77777778 0.7 ] mean value: 0.6755555555555556 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 28 mean value: 28.0 key: FP value: 12 mean value: 12.0 key: FN value: 19 mean value: 19.0 key: TP value: 64 mean value: 64.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.69 Accuracy on Blind test: 0.85 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.81459498 0.85142756 0.83559656 0.90151811 0.86936331 0.8753283 0.84840512 0.85588837 0.8444097 0.84958768] mean value: 0.8546119689941406 key: score_time value: [0.19565916 0.17850089 0.1850121 0.1829555 0.17323041 0.14045858 0.14689827 0.17946649 0.17438769 0.16761422] mean value: 0.17241833209991456 key: test_mcc value: [0.50069396 0.69282032 0.35 0.68313005 0.47809144 0.52915026 0.07559289 0.625 0.15811388 0.63245553] mean value: 0.47250483525421333 key: train_mcc value: [0.82905263 0.84761434 0.86922699 0.86692145 0.86692145 0.86692145 0.82983637 0.8683248 0.88678314 0.88678314] mean value: 0.8618385753871097 key: test_fscore value: [0.82352941 0.88888889 0.75 0.875 0.8 0.82352941 0.70588235 0.875 0.77777778 0.88888889] mean value: 0.8208496732026143 key: train_fscore value: [0.93706294 0.94366197 0.95104895 0.95104895 0.95104895 0.95104895 0.93793103 0.95035461 0.95714286 0.95714286] mean value: 0.9487492071787278 key: test_precision value: [0.77777778 0.8 0.75 0.77777778 0.75 0.7 0.6 0.875 0.7 0.8 ] mean value: 0.7530555555555555 key: train_precision value: [0.89333333 0.90540541 0.90666667 0.91891892 0.91891892 0.91891892 0.89473684 0.91780822 0.93055556 0.93055556] mean value: 0.9135818334556619 key: test_recall value: [0.875 1. 0.75 1. 0.85714286 1. 0.85714286 0.875 0.875 1. ] mean value: 0.9089285714285715 key: train_recall value: [0.98529412 0.98529412 1. 0.98550725 0.98550725 0.98550725 0.98550725 0.98529412 0.98529412 0.98529412] mean value: 0.986849957374254 key: test_accuracy value: [0.76923077 0.84615385 0.69230769 0.83333333 0.75 0.75 0.58333333 0.83333333 0.66666667 0.83333333] mean value: 0.7557692307692309 key: train_accuracy value: [0.91818182 0.92727273 0.93636364 0.93693694 0.93693694 0.93693694 0.91891892 0.93693694 0.94594595 0.94594595] mean value: 0.934037674037674 key: test_roc_auc value: [0.7375 0.8 0.675 0.8 0.72857143 0.7 0.52857143 0.8125 0.5625 0.75 ] mean value: 0.7094642857142858 key: train_roc_auc value: [0.89740896 0.90931373 0.91666667 0.92132505 0.92132505 0.92132505 0.89751553 0.92287962 0.93450752 0.93450752] mean value: 0.9176774703814793 key: test_jcc value: [0.7 0.8 0.6 0.77777778 0.66666667 0.7 0.54545455 0.77777778 0.63636364 0.8 ] mean value: 0.7004040404040404 key: train_jcc value: [0.88157895 0.89333333 0.90666667 0.90666667 0.90666667 0.90666667 0.88311688 0.90540541 0.91780822 0.91780822] mean value: 0.9025717674246874 key: TN value: 24 mean value: 24.0 key: FP value: 7 mean value: 7.0 key: FN value: 23 mean value: 23.0 key: TP value: 69 mean value: 69.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.73 Accuracy on Blind test: 0.87 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.09464025 0.04113626 0.04390836 0.04294944 0.18263888 0.03982615 0.0420804 0.04239774 0.04127336 0.04275012] mean value: 0.06136009693145752 key: score_time value: [0.01062655 0.01001501 0.01005936 0.00997829 0.01059461 0.01022339 0.01007247 0.0101881 0.01008344 0.01005411] mean value: 0.010189533233642578 key: test_mcc value: [1. 0.50069396 0.53674504 0.84515425 0.65714286 0.83666003 0.83666003 0.625 0.625 0.81649658] mean value: 0.7279552748848938 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.82352941 0.8 0.92307692 0.85714286 0.93333333 0.93333333 0.875 0.875 0.94117647] mean value: 0.8961592329239387 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.77777778 0.85714286 1. 0.85714286 0.875 0.875 0.875 0.875 0.88888889] mean value: 0.8880952380952382 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.875 0.75 0.85714286 0.85714286 1. 1. 0.875 0.875 1. ] mean value: 0.9089285714285715 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.76923077 0.76923077 0.91666667 0.83333333 0.91666667 0.91666667 0.83333333 0.83333333 0.91666667] mean value: 0.8705128205128204 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.7375 0.775 0.92857143 0.82857143 0.9 0.9 0.8125 0.8125 0.875 ] mean value: 0.8569642857142858 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.7 0.66666667 0.85714286 0.75 0.875 0.875 0.77777778 0.77777778 0.88888889] mean value: 0.8168253968253968 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 38 mean value: 38.0 key: FP value: 7 mean value: 7.0 key: FN value: 9 mean value: 9.0 key: TP value: 69 mean value: 69.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.83 Accuracy on Blind test: 0.92 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03773665 0.03072977 0.01915598 0.01995564 0.01927376 0.04759216 0.04356813 0.06778336 0.06304049 0.04747105] mean value: 0.039630699157714847 key: score_time value: [0.01988006 0.0116334 0.01142693 0.01161718 0.01167965 0.02221656 0.01711965 0.01969266 0.02830029 0.0115099 ] mean value: 0.016507625579833984 key: test_mcc value: [ 0.35 0.41475753 0.625 0.23904572 -0.29277002 -0.23904572 0.16903085 0.625 0.23904572 0.47809144] mean value: 0.2608155525664906 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.75 0.71428571 0.76923077 0.54545455 0.2 0.53333333 0.61538462 0.875 0.61538462 0.8 ] mean value: 0.6418073593073592 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.83333333 1. 0.75 0.33333333 0.5 0.66666667 0.875 0.8 0.85714286] mean value: 0.736547619047619 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.625 0.625 0.42857143 0.14285714 0.57142857 0.57142857 0.875 0.5 0.75 ] mean value: 0.5839285714285715 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.69230769 0.69230769 0.76923077 0.58333333 0.33333333 0.41666667 0.58333333 0.83333333 0.58333333 0.75 ] mean value: 0.6237179487179487 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.675 0.7125 0.8125 0.61428571 0.37142857 0.38571429 0.58571429 0.8125 0.625 0.75 ] mean value: 0.6344642857142857 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.6 0.55555556 0.625 0.375 0.11111111 0.36363636 0.44444444 0.77777778 0.44444444 0.66666667] mean value: 0.4963636363636364 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 32 mean value: 32.0 key: FP value: 31 mean value: 31.0 key: FN value: 15 mean value: 15.0 key: TP value: 45 mean value: 45.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.46 Accuracy on Blind test: 0.73 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.02157807 0.00869703 0.00844336 0.00849652 0.00846434 0.008219 0.00820565 0.00873017 0.00826836 0.00818968] mean value: 0.009729218482971192 key: score_time value: [0.0115695 0.00851297 0.00859666 0.00849891 0.00861049 0.00815916 0.00822258 0.0087266 0.00830817 0.0088129 ] mean value: 0.008801794052124024 key: test_mcc value: [ 0.50069396 0.50069396 0.21957752 0.35675303 -0.23904572 -0.09759001 0.07559289 -0.21320072 0.15811388 0.40824829] mean value: 0.16698370986965294 key: train_mcc value: [0.38383017 0.3811422 0.44420485 0.34242672 0.40566227 0.42728475 0.42728475 0.31408433 0.33243128 0.38975075] mean value: 0.38481020774814817 key: test_fscore value: [0.82352941 0.82352941 0.66666667 0.77777778 0.53333333 0.625 0.70588235 0.73684211 0.77777778 0.82352941] mean value: 0.7293868249054007 key: train_fscore value: [0.7862069 0.78911565 0.80821918 0.77852349 0.7972973 0.80272109 0.80272109 0.75862069 0.76712329 0.79470199] mean value: 0.7885250649074724 key: test_precision value: [0.77777778 0.77777778 0.71428571 0.63636364 0.5 0.55555556 0.6 0.63636364 0.7 0.77777778] mean value: 0.6675901875901876 key: train_precision value: [0.74025974 0.73417722 0.75641026 0.725 0.74683544 0.75641026 0.75641026 0.71428571 0.71794872 0.72289157] mean value: 0.737062916621785 key: test_recall value: [0.875 0.875 0.625 1. 0.57142857 0.71428571 0.85714286 0.875 0.875 0.875 ] mean value: 0.8142857142857143 key: train_recall value: [0.83823529 0.85294118 0.86764706 0.84057971 0.85507246 0.85507246 0.85507246 0.80882353 0.82352941 0.88235294] mean value: 0.8479326513213981 key: test_accuracy value: [0.76923077 0.76923077 0.61538462 0.66666667 0.41666667 0.5 0.58333333 0.58333333 0.66666667 0.75 ] mean value: 0.632051282051282 key: train_accuracy value: [0.71818182 0.71818182 0.74545455 0.7027027 0.72972973 0.73873874 0.73873874 0.68468468 0.69369369 0.72072072] mean value: 0.7190827190827191 key: test_roc_auc value: [0.7375 0.7375 0.6125 0.6 0.38571429 0.45714286 0.52857143 0.4375 0.5625 0.6875 ] mean value: 0.5746428571428572 key: train_roc_auc value: [0.68102241 0.67647059 0.70763305 0.65838509 0.68944099 0.70134576 0.70134576 0.64859781 0.65595075 0.67373461] mean value: 0.6793926822498463 key: test_jcc value: [0.7 0.7 0.5 0.63636364 0.36363636 0.45454545 0.54545455 0.58333333 0.63636364 0.7 ] mean value: 0.581969696969697 key: train_jcc value: [0.64772727 0.65168539 0.67816092 0.63736264 0.66292135 0.67045455 0.67045455 0.61111111 0.62222222 0.65934066] mean value: 0.6511440654786257 key: TN value: 16 mean value: 16.0 key: FP value: 14 mean value: 14.0 key: FN value: 31 mean value: 31.0 key: TP value: 62 mean value: 62.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.33 Accuracy on Blind test: 0.69 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.00946546 0.01361704 0.01318765 0.01225996 0.01225066 0.01285219 0.01456738 0.0147326 0.01424146 0.01396871] mean value: 0.013114309310913086 key: score_time value: [0.00867009 0.01097059 0.0109055 0.01113343 0.01116776 0.01117802 0.01155496 0.01120424 0.01120448 0.01112294] mean value: 0.010911202430725098 key: test_mcc value: [ 0.53674504 0.53935989 0.10112998 0.23904572 -0.09759001 0.31428571 0.68313005 0.47809144 0.40824829 0.35355339] mean value: 0.35559995141743805 key: train_mcc value: [0.90393035 0.81499979 0.6542355 0.57420392 0.73821371 0.81429778 0.76224042 0.86252225 0.94388927 0.92537599] mean value: 0.7993908985251966 key: test_fscore value: [0.8 0.84210526 0.73684211 0.54545455 0.625 0.71428571 0.875 0.8 0.82352941 0.71428571] mean value: 0.7476502754211732 key: train_fscore value: [0.96402878 0.93150685 0.87741935 0.72222222 0.90666667 0.92537313 0.91390728 0.9375 0.97841727 0.97014925] mean value: 0.9127190809036048 key: test_precision value: [0.85714286 0.72727273 0.63636364 0.75 0.55555556 0.71428571 0.77777778 0.85714286 0.77777778 0.83333333] mean value: 0.7486652236652236 key: train_precision value: [0.94366197 0.87179487 0.7816092 1. 0.83950617 0.95384615 0.84146341 1. 0.95774648 0.98484848] mean value: 0.9174476744069686 key: test_recall value: [0.75 1. 0.875 0.42857143 0.71428571 0.71428571 1. 0.75 0.875 0.625 ] mean value: 0.7732142857142857 key: train_recall value: [0.98529412 1. 1. 0.56521739 0.98550725 0.89855072 1. 0.88235294 1. 0.95588235] mean value: 0.9272804774083545 key: test_accuracy value: [0.76923077 0.76923077 0.61538462 0.58333333 0.5 0.66666667 0.83333333 0.75 0.75 0.66666667] mean value: 0.6903846153846154 key: train_accuracy value: [0.95454545 0.90909091 0.82727273 0.72972973 0.87387387 0.90990991 0.88288288 0.92792793 0.97297297 0.96396396] mean value: 0.8952170352170352 key: test_roc_auc value: [0.775 0.7 0.5375 0.61428571 0.45714286 0.65714286 0.8 0.75 0.6875 0.6875 ] mean value: 0.666607142857143 key: train_roc_auc value: [0.94502801 0.88095238 0.77380952 0.7826087 0.83799172 0.91356108 0.8452381 0.94117647 0.96511628 0.96631327] mean value: 0.8851795521039559 key: test_jcc value: [0.66666667 0.72727273 0.58333333 0.375 0.45454545 0.55555556 0.77777778 0.66666667 0.7 0.55555556] mean value: 0.6062373737373737 key: train_jcc value: [0.93055556 0.87179487 0.7816092 0.56521739 0.82926829 0.86111111 0.84146341 0.88235294 0.95774648 0.94202899] mean value: 0.8463148238042214 key: TN value: 26 mean value: 26.0 key: FP value: 17 mean value: 17.0 key: FN value: 21 mean value: 21.0 key: TP value: 59 mean value: 59.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.65 Accuracy on Blind test: 0.81 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.0125587 0.01264143 0.01278758 0.01199651 0.01312399 0.01274061 0.01246476 0.01312518 0.01257873 0.01276875] mean value: 0.01267862319946289 key: score_time value: [0.01114702 0.01114774 0.01115823 0.01123714 0.01115966 0.01120853 0.0119431 0.01129484 0.01114678 0.01116776] mean value: 0.011261081695556641 key: test_mcc value: [0.35 0.69282032 0.36514837 0.35675303 0.11952286 0.07559289 0. 0.625 0.5 0.15811388] mean value: 0.3242951367247703 key: train_mcc value: [0.8510645 0.88503928 0.67218096 0.52601495 0.69106783 0.90396349 0.38789701 0.80998026 0.92794199 0.80035317] mean value: 0.7455503461752209 key: test_fscore value: [0.75 0.88888889 0.8 0.77777778 0.66666667 0.70588235 0. 0.875 0.66666667 0.77777778] mean value: 0.6908660130718955 key: train_fscore value: [0.94444444 0.95714286 0.88311688 0.84146341 0.89032258 0.96402878 0.48351648 0.91338583 0.96969697 0.92517007] mean value: 0.8772288304974227 key: test_precision value: [0.75 0.8 0.66666667 0.63636364 0.625 0.6 0. 0.875 1. 0.7 ] mean value: 0.6653030303030303 key: train_precision value: [0.89473684 0.93055556 0.79069767 0.72631579 0.80232558 0.95714286 1. 0.98305085 1. 0.86075949] mean value: 0.8945584641219826 key: test_recall value: [0.75 1. 1. 1. 0.71428571 0.85714286 0. 0.875 0.5 0.875 ] mean value: 0.7571428571428571 key: train_recall value: [1. 0.98529412 1. 1. 1. 0.97101449 0.31884058 0.85294118 0.94117647 1. ] mean value: 0.9069266837169649 key: test_accuracy value: [0.69230769 0.84615385 0.69230769 0.66666667 0.58333333 0.58333333 0.41666667 0.83333333 0.66666667 0.66666667] mean value: 0.6647435897435898 key: train_accuracy value: [0.92727273 0.94545455 0.83636364 0.76576577 0.84684685 0.95495495 0.57657658 0.9009009 0.96396396 0.9009009 ] mean value: 0.8619000819000819 key: test_roc_auc value: [0.675 0.8 0.6 0.6 0.55714286 0.52857143 0.5 0.8125 0.75 0.5625 ] mean value: 0.6385714285714286 key: train_roc_auc value: [0.9047619 0.93312325 0.78571429 0.69047619 0.79761905 0.94979296 0.65942029 0.91484268 0.97058824 0.87209302] mean value: 0.8478431868197228 key: test_jcc value: [0.6 0.8 0.66666667 0.63636364 0.5 0.54545455 0. 0.77777778 0.5 0.63636364] mean value: 0.5662626262626264 key: train_jcc value: [0.89473684 0.91780822 0.79069767 0.72631579 0.80232558 0.93055556 0.31884058 0.84057971 0.94117647 0.86075949] mean value: 0.8023795916240731 key: TN value: 24 mean value: 24.0 key: FP value: 18 mean value: 18.0 key: FN value: 23 mean value: 23.0 key: TP value: 58 mean value: 58.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.67 Accuracy on Blind test: 0.84 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.10628152 0.08904624 0.089674 0.0911634 0.09197474 0.09089756 0.0883739 0.08940411 0.08909869 0.08851576] mean value: 0.09144299030303955 key: score_time value: [0.01433253 0.0145371 0.01444006 0.01587653 0.01531577 0.01451087 0.01486683 0.01439857 0.01451421 0.01443386] mean value: 0.014722633361816406 key: test_mcc value: [0.73192505 0.69282032 0.53674504 0.71428571 0.65714286 0.83666003 0.65714286 0.81649658 0.625 0.63245553] mean value: 0.690067398592755 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.85714286 0.88888889 0.8 0.83333333 0.85714286 0.93333333 0.85714286 0.94117647 0.875 0.88888889] mean value: 0.8732049486461252 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.8 0.85714286 1. 0.85714286 0.875 0.85714286 0.88888889 0.875 0.8 ] mean value: 0.8810317460317462 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 1. 0.75 0.71428571 0.85714286 1. 0.85714286 1. 0.875 1. ] mean value: 0.8803571428571428 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.84615385 0.84615385 0.76923077 0.83333333 0.83333333 0.91666667 0.83333333 0.91666667 0.83333333 0.83333333] mean value: 0.8461538461538461 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.875 0.8 0.775 0.85714286 0.82857143 0.9 0.82857143 0.875 0.8125 0.75 ] mean value: 0.8301785714285714 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.75 0.8 0.66666667 0.71428571 0.75 0.875 0.75 0.88888889 0.77777778 0.8 ] mean value: 0.7772619047619047 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 37 mean value: 37.0 key: FP value: 9 mean value: 9.0 key: FN value: 10 mean value: 10.0 key: TP value: 67 mean value: 67.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.83 Accuracy on Blind test: 0.92 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.03100371 0.03074861 0.04495049 0.04121494 0.0335474 0.0405786 0.04685497 0.0346806 0.04328895 0.03637171] mean value: 0.03832399845123291 key: score_time value: [0.01783442 0.01984453 0.02553606 0.02212143 0.02536345 0.03404522 0.01622009 0.02240157 0.02442431 0.02128291] mean value: 0.022907400131225587 key: test_mcc value: [0.85391256 0.50069396 0.675 0.84515425 0.65714286 0.83666003 0.65714286 0.47809144 0.625 0.625 ] mean value: 0.6753797965972024 key: train_mcc value: [0.96253342 1. 0.96148459 0.98111593 0.98094082 0.98094082 0.96169772 1. 0.98111593 0.92537599] mean value: 0.9735205243744222 key: test_fscore value: [0.93333333 0.82352941 0.875 0.92307692 0.85714286 0.93333333 0.85714286 0.8 0.875 0.875 ] mean value: 0.875255871579401 key: train_fscore value: [0.98507463 1. 0.98529412 0.99270073 0.99280576 0.99280576 0.98550725 1. 0.99270073 0.97014925] mean value: 0.9897038215266267 key: test_precision value: [1. 0.77777778 0.875 1. 0.85714286 0.875 0.85714286 0.85714286 0.875 0.875 ] mean value: 0.8849206349206348 key: train_precision value: [1. 1. 0.98529412 1. 0.98571429 0.98571429 0.98550725 1. 0.98550725 0.98484848] mean value: 0.9912585666677739 key: test_recall value: [0.875 0.875 0.875 0.85714286 0.85714286 1. 0.85714286 0.75 0.875 0.875 ] mean value: 0.8696428571428572 key: train_recall value: [0.97058824 1. 0.98529412 0.98550725 1. 1. 0.98550725 1. 1. 0.95588235] mean value: 0.9882779198635975 key: test_accuracy value: [0.92307692 0.76923077 0.84615385 0.91666667 0.83333333 0.91666667 0.83333333 0.75 0.83333333 0.83333333] mean value: 0.8455128205128204 key: train_accuracy value: [0.98181818 1. 0.98181818 0.99099099 0.99099099 0.99099099 0.98198198 1. 0.99099099 0.96396396] mean value: 0.9873546273546273 key: test_roc_auc value: [0.9375 0.7375 0.8375 0.92857143 0.82857143 0.9 0.82857143 0.75 0.8125 0.8125 ] mean value: 0.8373214285714287 key: train_roc_auc value: [0.98529412 1. 0.9807423 0.99275362 0.98809524 0.98809524 0.98084886 1. 0.98837209 0.96631327] mean value: 0.987051473774545 key: test_jcc value: [0.875 0.7 0.77777778 0.85714286 0.75 0.875 0.75 0.66666667 0.77777778 0.77777778] mean value: 0.7807142857142857 key: train_jcc value: [0.97058824 1. 0.97101449 0.98550725 0.98571429 0.98571429 0.97142857 1. 0.98550725 0.94202899] mean value: 0.9797503349165753 key: TN value: 38 mean value: 38.0 key: FP value: 10 mean value: 10.0 key: FN value: 9 mean value: 9.0 key: TP value: 66 mean value: 66.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.83 Accuracy on Blind test: 0.92 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.0334475 0.04036093 0.03545952 0.03805256 0.0399847 0.04043794 0.04135227 0.04058337 0.04076147 0.03606129] mean value: 0.03865015506744385 key: score_time value: [0.02255535 0.02062464 0.0196178 0.01720858 0.024055 0.01250672 0.02217317 0.02099752 0.02161336 0.0183444 ] mean value: 0.019969654083251954 key: test_mcc value: [ 0.025 0.15811388 -0.09759001 0.11952286 -0.48795004 -0.09759001 -0.48795004 -0.125 -0.31622777 0.25 ] mean value: -0.10596711096132197 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.625 0.70588235 0.53333333 0.66666667 0.5 0.625 0.5 0.625 0.66666667 0.75 ] mean value: 0.6197549019607843 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.625 0.66666667 0.57142857 0.625 0.44444444 0.55555556 0.44444444 0.625 0.6 0.75 ] mean value: 0.5907539682539682 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.625 0.75 0.5 0.71428571 0.57142857 0.71428571 0.57142857 0.625 0.75 0.75 ] mean value: 0.6571428571428571 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.53846154 0.61538462 0.46153846 0.58333333 0.33333333 0.5 0.33333333 0.5 0.5 0.66666667] mean value: 0.5032051282051282 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5125 0.575 0.45 0.55714286 0.28571429 0.45714286 0.28571429 0.4375 0.375 0.625 ] mean value: 0.4560714285714285 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.45454545 0.54545455 0.36363636 0.5 0.33333333 0.45454545 0.33333333 0.45454545 0.5 0.6 ] mean value: 0.4539393939393939 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 12 mean value: 12.0 key: FP value: 26 mean value: 26.0 key: FN value: 35 mean value: 35.0 key: TP value: 50 mean value: 50.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.08 Accuracy on Blind test: 0.58 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.25328517 0.22863054 0.22370648 0.22962642 0.22986698 0.22808623 0.2266736 0.22830272 0.23198032 0.22613072] mean value: 0.23062891960144044 key: score_time value: [0.00900102 0.00898838 0.00899267 0.00883532 0.00903273 0.00897455 0.00888777 0.00897026 0.00880384 0.00959563] mean value: 0.009008216857910156 key: test_mcc value: [0.85391256 0.50069396 0.85391256 1. 0.65714286 0.83666003 0.83666003 0.47809144 0.81649658 0.81649658] mean value: 0.7650066606320145 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.93333333 0.82352941 0.93333333 1. 0.85714286 0.93333333 0.93333333 0.8 0.94117647 0.94117647] mean value: 0.9096358543417367 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.77777778 1. 1. 0.85714286 0.875 0.875 0.85714286 0.88888889 0.88888889] mean value: 0.9019841269841271 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.875 0.875 0.875 1. 0.85714286 1. 1. 0.75 1. 1. ] mean value: 0.9232142857142858 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.92307692 0.76923077 0.92307692 1. 0.83333333 0.91666667 0.91666667 0.75 0.91666667 0.91666667] mean value: 0.8865384615384617 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9375 0.7375 0.9375 1. 0.82857143 0.9 0.9 0.75 0.875 0.875 ] mean value: 0.8741071428571429 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.875 0.7 0.875 1. 0.75 0.875 0.875 0.66666667 0.88888889 0.88888889] mean value: 0.8394444444444445 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 39 mean value: 39.0 key: FP value: 6 mean value: 6.0 key: FN value: 8 mean value: 8.0 key: TP value: 70 mean value: 70.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.76 Accuracy on Blind test: 0.89 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01242208 0.01676083 0.02331352 0.01530957 0.01565695 0.01562834 0.01548266 0.01578426 0.01571488 0.01702189] mean value: 0.016309499740600586 key: score_time value: [0.0116725 0.01181579 0.01174021 0.0117867 0.01330113 0.01283383 0.01169133 0.01326752 0.0130136 0.01296425] mean value: 0.012408685684204102 key: test_mcc value: [ 0.36514837 0.10112998 0.025 0.29277002 -0.50709255 0.47809144 -0.09759001 -0.125 0.25 0.47809144] mean value: 0.12605487002597085 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.73684211 0.625 0.75 0.30769231 0.8 0.625 0.625 0.75 0.8 ] mean value: 0.6819534412955466 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.63636364 0.625 0.66666667 0.33333333 0.75 0.55555556 0.625 0.75 0.85714286] mean value: 0.6465728715728715 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.875 0.625 0.85714286 0.28571429 0.85714286 0.71428571 0.625 0.75 0.75 ] mean value: 0.7339285714285715 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.69230769 0.61538462 0.53846154 0.66666667 0.25 0.75 0.5 0.5 0.66666667 0.75 ] mean value: 0.592948717948718 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.6 0.5375 0.5125 0.62857143 0.24285714 0.72857143 0.45714286 0.4375 0.625 0.75 ] mean value: 0.5519642857142857 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.58333333 0.45454545 0.6 0.18181818 0.66666667 0.45454545 0.45454545 0.6 0.66666667] mean value: 0.532878787878788 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 18 mean value: 18.0 key: FP value: 19 mean value: 19.0 key: FN value: 29 mean value: 29.0 key: TP value: 57 mean value: 57.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: -0.08 Accuracy on Blind test: 0.5 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.04491043 0.01445651 0.029953 0.02912736 0.0282557 0.02754736 0.02681708 0.02655959 0.02590156 0.03225064] mean value: 0.02857792377471924 key: score_time value: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:130: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:131: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_CV['Resampling'] = rs_none /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:136: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:137: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_BT['Resampling'] = rs_none [0.0209837 0.02096486 0.02190661 0.02278996 0.02107406 0.02101135 0.02097893 0.02094507 0.02091599 0.02093077] mean value: 0.021250128746032715 key: test_mcc value: [0.35 0.675 0.53674504 0.65714286 0.37142857 0.29277002 0.65714286 0.81649658 0.5976143 0.47809144] mean value: 0.5432431677049219 key: train_mcc value: [0.90344027 0.90393035 0.94213903 0.96169772 0.98094082 0.94333502 0.94333502 0.94298433 0.96241667 0.94346837] mean value: 0.9427687593329607 key: test_fscore value: [0.75 0.875 0.8 0.85714286 0.66666667 0.75 0.85714286 0.94117647 0.76923077 0.8 ] mean value: 0.8066359620771385 key: train_fscore value: [0.96350365 0.96402878 0.97810219 0.98550725 0.99280576 0.9787234 0.9787234 0.97810219 0.98550725 0.97777778] mean value: 0.978278164061322 key: test_precision value: [0.75 0.875 0.85714286 0.85714286 0.8 0.66666667 0.85714286 0.88888889 1. 0.85714286] mean value: 0.8409126984126984 key: train_precision value: [0.95652174 0.94366197 0.97101449 0.98550725 0.98571429 0.95833333 0.95833333 0.97101449 0.97142857 0.98507463] mean value: 0.9686604093520673 key: test_recall value: [0.75 0.875 0.75 0.85714286 0.57142857 0.85714286 0.85714286 1. 0.625 0.75 ] mean value: 0.7892857142857143 key: train_recall value: [0.97058824 0.98529412 0.98529412 0.98550725 1. 1. 1. 0.98529412 1. 0.97058824] mean value: 0.9882566069906223 key: test_accuracy value: [0.69230769 0.84615385 0.76923077 0.83333333 0.66666667 0.66666667 0.83333333 0.91666667 0.75 0.75 ] mean value: 0.7724358974358975 key: train_accuracy value: [0.95454545 0.95454545 0.97272727 0.98198198 0.99099099 0.97297297 0.97297297 0.97297297 0.98198198 0.97297297] mean value: 0.9728665028665029 key: test_roc_auc value: [0.675 0.8375 0.775 0.82857143 0.68571429 0.62857143 0.82857143 0.875 0.8125 0.75 ] mean value: 0.7696428571428572 key: train_roc_auc value: [0.94957983 0.94502801 0.96883754 0.98084886 0.98809524 0.96428571 0.96428571 0.96939124 0.97674419 0.97366621] mean value: 0.9680762547688438 key: test_jcc value: [0.6 0.77777778 0.66666667 0.75 0.5 0.6 0.75 0.88888889 0.625 0.66666667] mean value: 0.6825 key: train_jcc value: [0.92957746 0.93055556 0.95714286 0.97142857 0.98571429 0.95833333 0.95833333 0.95714286 0.97142857 0.95652174] mean value: 0.9576178568998532 key: TN value: 35 mean value: 35.0 key: FP value: 16 mean value: 16.0 key: FN value: 12 mean value: 12.0 key: TP value: 60 mean value: 60.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.66 Accuracy on Blind test: 0.84 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.21082497 0.18535781 0.18963909 0.25137472 0.18987632 0.18474865 0.18461823 0.18464971 0.1901927 0.18669987] mean value: 0.1957982063293457 key: score_time value: [0.02206898 0.01987553 0.02346182 0.02101779 0.02000642 0.02148461 0.02291036 0.02201867 0.02046514 0.01848912] mean value: 0.021179842948913574 key: test_mcc value: [0.35 1. 0.53674504 0.83666003 0.37142857 0.29277002 1. 0.81649658 0.5976143 0.625 ] mean value: 0.6426714545563822 key: train_mcc value: [0.98100984 0.96148459 0.94213903 0.98111593 0.98094082 0.94333502 0.98094082 0.94298433 1. 1. ] mean value: 0.9713950385172282 key: test_fscore value: [0.75 1. 0.8 0.93333333 0.66666667 0.75 1. 0.94117647 0.76923077 0.875 ] mean value: 0.8485407239819004 key: train_fscore value: [0.99259259 0.98529412 0.97810219 0.99270073 0.99280576 0.9787234 0.99280576 0.97810219 1. 1. ] mean value: 0.9891126734775388 key: test_precision value: [0.75 1. 0.85714286 0.875 0.8 0.66666667 1. 0.88888889 1. 0.875 ] mean value: 0.8712698412698412 key: train_precision value: [1. 0.98529412 0.97101449 1. 0.98571429 0.95833333 0.98571429 0.97101449 1. 1. ] mean value: 0.985708500791621 key: test_recall value: [0.75 1. 0.75 1. 0.57142857 0.85714286 1. 1. 0.625 0.875 ] mean value: 0.8428571428571429 key: train_recall value: [0.98529412 0.98529412 0.98529412 0.98550725 1. 1. 1. 0.98529412 1. 1. ] mean value: 0.9926683716965048 key: test_accuracy value: [0.69230769 1. 0.76923077 0.91666667 0.66666667 0.66666667 1. 0.91666667 0.75 0.83333333] mean value: 0.8211538461538461 key: train_accuracy value: [0.99090909 0.98181818 0.97272727 0.99099099 0.99099099 0.97297297 0.99099099 0.97297297 1. 1. ] mean value: 0.9864373464373465 key: test_roc_auc value: [0.675 1. 0.775 0.9 0.68571429 0.62857143 1. 0.875 0.8125 0.8125 ] mean value: 0.8164285714285715 key: train_roc_auc value: [0.99264706 0.9807423 0.96883754 0.99275362 0.98809524 0.96428571 0.98809524 0.96939124 1. 1. ] mean value: 0.984484794929094 key: test_jcc value: [0.6 1. 0.66666667 0.875 0.5 0.6 1. 0.88888889 0.625 0.77777778] mean value: 0.7533333333333333 key: train_jcc value: [0.98529412 0.97101449 0.95714286 0.98550725 0.98571429 0.95833333 0.98571429 0.95714286 1. 1. ] mean value: 0.9785863475825114 key: TN value: 37 mean value: 37.0 key: FP value: 12 mean value: 12.0 key: FN value: 10 mean value: 10.0 key: TP value: 64 mean value: 64.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.65 Accuracy on Blind test: 0.82 PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02417588 0.02643847 0.02383518 0.02756286 0.04330134 0.04405832 0.02802396 0.05000472 0.04649234 0.02764344] mean value: 0.03415365219116211 key: score_time value: [0.01154518 0.01132321 0.01134753 0.01136136 0.011343 0.01144052 0.02787018 0.01187468 0.01160598 0.01137733] mean value: 0.01310889720916748 key: test_mcc value: [0.51639778 0.37796447 0.60714286 0.26189246 0.07142857 0.6000992 1. 0.76376262 1. 0.73214286] mean value: 0.5930830815501035 key: train_mcc value: [0.84271225 0.88273483 0.84026462 0.82629176 0.8555278 0.87099729 0.82788248 0.84156943 0.85540562 0.812277 ] mean value: 0.8455663091863196 key: test_fscore value: [0.71428571 0.66666667 0.8 0.66666667 0.53333333 0.76923077 1. 0.85714286 1. 0.875 ] mean value: 0.7882326007326007 key: train_fscore value: [0.91472868 0.94029851 0.91851852 0.91044776 0.92537313 0.93233083 0.90769231 0.91603053 0.92424242 0.90076336] mean value: 0.9190426055806308 key: test_precision value: [0.83333333 0.71428571 0.75 0.54545455 0.5 0.83333333 1. 1. 1. 0.875 ] mean value: 0.8051406926406927 key: train_precision value: [0.96721311 0.95454545 0.93939394 0.93846154 0.95384615 0.96875 0.9516129 0.95238095 0.953125 0.93650794] mean value: 0.9515836993115879 key: test_recall value: [0.625 0.625 0.85714286 0.85714286 0.57142857 0.71428571 1. 0.75 1. 0.875 ] mean value: 0.7875 key: train_recall value: [0.86764706 0.92647059 0.89855072 0.88405797 0.89855072 0.89855072 0.86764706 0.88235294 0.89705882 0.86764706] mean value: 0.88885336743393 key: test_accuracy value: [0.75 0.6875 0.8 0.6 0.53333333 0.8 1. 0.86666667 1. 0.86666667] mean value: 0.7904166666666667 key: train_accuracy value: [0.91911765 0.94117647 0.91970803 0.91240876 0.9270073 0.93430657 0.91240876 0.91970803 0.9270073 0.90510949] mean value: 0.9217958351223701 key: test_roc_auc value: [0.75 0.6875 0.80357143 0.61607143 0.53571429 0.79464286 1. 0.875 1. 0.86607143] mean value: 0.7928571428571429 key: train_roc_auc value: [0.91911765 0.94117647 0.9198636 0.91261722 0.92721654 0.93456948 0.9120844 0.91943734 0.92679028 0.90483802] mean value: 0.9217710997442456 key: test_jcc value: [0.55555556 0.5 0.66666667 0.5 0.36363636 0.625 1. 0.75 1. 0.77777778] mean value: 0.6738636363636364 key: train_jcc value: [0.84285714 0.88732394 0.84931507 0.83561644 0.86111111 0.87323944 0.83098592 0.84507042 0.85915493 0.81944444] mean value: 0.8504118853149338 key: TN value: 60 mean value: 60.0 key: FP value: 16 mean value: 16.0 key: FN value: 16 mean value: 16.0 key: TP value: 60 mean value: 60.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.66 Accuracy on Blind test: 0.84 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.61739421 0.60889077 0.72704625 0.63255167 0.60625839 0.61627579 0.74641204 0.62438607 0.62583709 0.99010992] mean value: 0.6795162200927735 key: score_time value: [0.01502943 0.01426482 0.01314068 0.01309299 0.01303506 0.01354313 0.01315236 0.01328683 0.01351619 0.01387477] mean value: 0.013593626022338868 key: test_mcc value: [0.62994079 0.5 0.6000992 0.60714286 0.07142857 0.46428571 1. 0.87287156 0.875 0.60714286] mean value: 0.622791154744166 key: train_mcc value: [1. 1. 1. 1. 1. 1. 0.98550418 1. 1. 1. ] mean value: 0.9985504179288428 key: test_fscore value: [0.8 0.75 0.76923077 0.8 0.53333333 0.71428571 1. 0.94117647 0.93333333 0.8 ] mean value: 0.8041359620771387 key: train_fscore value: [1. 1. 1. 1. 1. 1. 0.99259259 1. 1. 1. ] mean value: 0.9992592592592592 key: test_precision value: [0.85714286 0.75 0.83333333 0.75 0.5 0.71428571 1. 0.88888889 1. 0.85714286] mean value: 0.8150793650793652 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.75 0.71428571 0.85714286 0.57142857 0.71428571 1. 1. 0.875 0.75 ] mean value: 0.7982142857142857 key: train_recall value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [1. 1. 1. 1. 1. 1. 0.98529412 1. 1. 1. ] mean value: 0.9985294117647058 key: test_accuracy value: [0.8125 0.75 0.8 0.8 0.53333333 0.73333333 1. 0.93333333 0.93333333 0.8 ] mean value: 0.8095833333333335 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 0.99270073 1. 1. 1. ] mean value: 0.9992700729927007 key: test_roc_auc value: [0.8125 0.75 0.79464286 0.80357143 0.53571429 0.73214286 1. 0.92857143 0.9375 0.80357143] mean value: 0.8098214285714285 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 0.99264706 1. 1. 1. ] mean value: 0.9992647058823529 key: test_jcc value: [0.66666667 0.6 0.625 0.66666667 0.36363636 0.55555556 1. 0.88888889 0.875 0.66666667] mean value: 0.6908080808080809 key: train_jcc value: [1. 1. 1. 1. 1. 1. 0.98529412 1. 1. 1. ] mean value: 0.9985294117647058 key: TN value: 62 mean value: 62.0 key: FP value: 15 mean value: 15.0 key: FN value: 14 mean value: 14.0 key: TP value: 61 mean value: 61.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.73 Accuracy on Blind test: 0.87 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01203299 0.0114522 0.00865173 0.00863814 0.00827718 0.00840831 0.0082643 0.00831389 0.00874424 0.00846148] mean value: 0.009124445915222167 key: score_time value: [0.01137662 0.0107646 0.00874591 0.0085361 0.00905752 0.00831676 0.00850463 0.00908828 0.00901866 0.00841832] mean value: 0.0091827392578125 key: test_mcc value: [-0.12598816 0.5 0.47245559 0.33928571 0.19642857 0.49099025 0.49099025 0.66143783 0.60714286 0.49099025] mean value: 0.4123733163308031 key: train_mcc value: [0.50349655 0.53158595 0.60764769 0.52321152 0.56363445 0.5360985 0.49281415 0.54996703 0.50855421 0.52059257] mean value: 0.5337602617879365 key: test_fscore value: [0.4 0.75 0.66666667 0.66666667 0.57142857 0.75 0.71428571 0.76923077 0.8 0.71428571] mean value: 0.6802564102564103 key: train_fscore value: [0.734375 0.72268908 0.79699248 0.74418605 0.72413793 0.78082192 0.72440945 0.75968992 0.73015873 0.74418605] mean value: 0.7461646600157464 key: test_precision value: [0.42857143 0.75 0.8 0.625 0.57142857 0.66666667 0.83333333 1. 0.85714286 0.83333333] mean value: 0.736547619047619 key: train_precision value: [0.78333333 0.84313725 0.828125 0.8 0.89361702 0.74025974 0.77966102 0.80327869 0.79310345 0.78688525] mean value: 0.8051400749422875 key: test_recall value: [0.375 0.75 0.57142857 0.71428571 0.57142857 0.85714286 0.625 0.625 0.75 0.625 ] mean value: 0.6464285714285714 key: train_recall value: [0.69117647 0.63235294 0.76811594 0.69565217 0.60869565 0.82608696 0.67647059 0.72058824 0.67647059 0.70588235] mean value: 0.700149190110827 key: test_accuracy value: [0.4375 0.75 0.73333333 0.66666667 0.6 0.73333333 0.73333333 0.8 0.8 0.73333333] mean value: 0.6987499999999999 key: train_accuracy value: [0.75 0.75735294 0.80291971 0.75912409 0.76642336 0.76642336 0.74452555 0.77372263 0.75182482 0.75912409] mean value: 0.7631440532417345 key: test_roc_auc value: [0.4375 0.75 0.72321429 0.66964286 0.59821429 0.74107143 0.74107143 0.8125 0.80357143 0.74107143] mean value: 0.7017857142857145 key: train_roc_auc value: [0.75 0.75735294 0.80317562 0.75959079 0.76758312 0.76598465 0.7440324 0.7733376 0.75127877 0.75873828] mean value: 0.7631074168797956 key: test_jcc value: [0.25 0.6 0.5 0.5 0.4 0.6 0.55555556 0.625 0.66666667 0.55555556] mean value: 0.5252777777777777 key: train_jcc value: [0.58024691 0.56578947 0.6625 0.59259259 0.56756757 0.64044944 0.56790123 0.6125 0.575 0.59259259] mean value: 0.5957139812787359 key: TN value: 57 mean value: 57.0 key: FP value: 27 mean value: 27.0 key: FN value: 19 mean value: 19.0 key: TP value: 49 mean value: 49.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.32 Accuracy on Blind test: 0.68 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00864267 0.00851321 0.00859857 0.00837684 0.00854611 0.00839543 0.00837374 0.00850129 0.00829649 0.00852156] mean value: 0.008476591110229493 key: score_time value: [0.00828528 0.00844479 0.00827122 0.0084486 0.00835276 0.00851703 0.00856376 0.00837302 0.00849414 0.00842476] mean value: 0.008417534828186034 key: test_mcc value: [ 0.28867513 0.40451992 -0.13363062 -0.07142857 0.18898224 -0.13363062 0.26189246 0.46770717 0.26189246 0. ] mean value: 0.1534979575199616 key: train_mcc value: [0.47149797 0.43898087 0.47009486 0.51721526 0.49317391 0.45199806 0.42270073 0.48120986 0.41808005 0.42843418] mean value: 0.45933857270699335 key: test_fscore value: [0.5 0.61538462 0.2 0.42857143 0.5 0.2 0.5 0.54545455 0.5 0. ] mean value: 0.3989410589410589 key: train_fscore value: [0.63551402 0.61682243 0.64864865 0.65420561 0.62857143 0.61682243 0.58252427 0.62857143 0.56 0.60377358] mean value: 0.6175453848523136 key: test_precision value: [0.75 0.8 0.33333333 0.42857143 0.6 0.33333333 0.75 1. 0.75 0. ] mean value: 0.5745238095238095 key: train_precision value: [0.87179487 0.84615385 0.85714286 0.92105263 0.91666667 0.86842105 0.85714286 0.89189189 0.875 0.84210526] mean value: 0.8747371938161411 key: test_recall value: [0.375 0.5 0.14285714 0.42857143 0.42857143 0.14285714 0.375 0.375 0.375 0. ] mean value: 0.3142857142857143 key: train_recall value: [0.5 0.48529412 0.52173913 0.50724638 0.47826087 0.47826087 0.44117647 0.48529412 0.41176471 0.47058824] mean value: 0.47796248934356356 key: test_accuracy value: [0.625 0.6875 0.46666667 0.46666667 0.6 0.46666667 0.6 0.66666667 0.6 0.46666667] mean value: 0.5645833333333333 key: train_accuracy value: [0.71323529 0.69852941 0.71532847 0.72992701 0.71532847 0.70072993 0.68613139 0.71532847 0.67883212 0.69343066] mean value: 0.7046801202232718 key: test_roc_auc value: [0.625 0.6875 0.44642857 0.46428571 0.58928571 0.44642857 0.61607143 0.6875 0.61607143 0.5 ] mean value: 0.5678571428571428 key: train_roc_auc value: [0.71323529 0.69852941 0.71675192 0.73156436 0.71707161 0.70236573 0.68435635 0.71366155 0.67689685 0.69181586] mean value: 0.7046248934356352 key: test_jcc value: [0.33333333 0.44444444 0.11111111 0.27272727 0.33333333 0.11111111 0.33333333 0.375 0.33333333 0. ] mean value: 0.26477272727272727 key: train_jcc value: [0.46575342 0.44594595 0.48 0.48611111 0.45833333 0.44594595 0.4109589 0.45833333 0.38888889 0.43243243] mean value: 0.44727033197581145 key: TN value: 62 mean value: 62.0 key: FP value: 52 mean value: 52.0 key: FN value: 14 mean value: 14.0 key: TP value: 24 mean value: 24.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.25 Accuracy on Blind test: 0.6 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00809407 0.00833535 0.00804353 0.00800586 0.00811768 0.00831366 0.00817037 0.00811768 0.00806737 0.00804043] mean value: 0.008130598068237304 key: score_time value: [0.00945091 0.00934529 0.01366639 0.00925326 0.00923061 0.00968885 0.00921607 0.00923228 0.00930452 0.00933623] mean value: 0.009772443771362304 key: test_mcc value: [ 0. 0.13483997 0.33928571 0.19642857 -0.56407607 0.04029115 0.36689969 0.09449112 0.19642857 0.13363062] mean value: 0.09382193350801989 key: train_mcc value: [0.47809144 0.45083482 0.45881948 0.48879481 0.51877014 0.4190125 0.4870476 0.45582775 0.37256446 0.46759853] mean value: 0.4597361547538289 key: test_fscore value: [0.33333333 0.46153846 0.66666667 0.57142857 0. 0.36363636 0.4 0.46153846 0.625 0.36363636] mean value: 0.42467782217782213 key: train_fscore value: [0.70967742 0.68852459 0.68852459 0.70491803 0.72131148 0.65546218 0.7 0.65486726 0.62068966 0.69918699] mean value: 0.6843162196432879 key: test_precision value: [0.5 0.6 0.625 0.57142857 0. 0.5 1. 0.6 0.625 0.66666667] mean value: 0.5688095238095239 key: train_precision value: [0.78571429 0.77777778 0.79245283 0.81132075 0.83018868 0.78 0.80769231 0.82222222 0.75 0.78181818] mean value: 0.7939187039375719 key: test_recall value: [0.25 0.375 0.71428571 0.57142857 0. 0.28571429 0.25 0.375 0.625 0.25 ] mean value: 0.3696428571428571 key: train_recall value: [0.64705882 0.61764706 0.60869565 0.62318841 0.63768116 0.56521739 0.61764706 0.54411765 0.52941176 0.63235294] mean value: 0.6023017902813299 key: test_accuracy value: [0.5 0.5625 0.66666667 0.6 0.26666667 0.53333333 0.6 0.53333333 0.6 0.53333333] mean value: 0.5395833333333333 key: train_accuracy value: [0.73529412 0.72058824 0.72262774 0.73722628 0.75182482 0.70072993 0.73722628 0.71532847 0.67883212 0.72992701] mean value: 0.7229604980678402 key: test_roc_auc value: [0.5 0.5625 0.66964286 0.59821429 0.25 0.51785714 0.625 0.54464286 0.59821429 0.55357143] mean value: 0.5419642857142857 key: train_roc_auc value: [0.73529412 0.72058824 0.72346547 0.73806479 0.75266411 0.70172634 0.73635976 0.71408781 0.67774936 0.72921995] mean value: 0.7229219948849105 key: test_jcc value: [0.2 0.3 0.5 0.4 0. 0.22222222 0.25 0.3 0.45454545 0.22222222] mean value: 0.2848989898989899 key: train_jcc value: [0.55 0.525 0.525 0.5443038 0.56410256 0.4875 0.53846154 0.48684211 0.45 0.5375 ] mean value: 0.5208710005295616 key: TN value: 54 mean value: 54.0 key: FP value: 48 mean value: 48.0 key: FN value: 22 mean value: 22.0 key: TP value: 28 mean value: 28.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.04 Accuracy on Blind test: 0.52 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.0097568 0.00961161 0.00942612 0.00953341 0.00947309 0.00964904 0.00985408 0.00970864 0.00972199 0.00953579] mean value: 0.009627056121826173 key: score_time value: [0.00866508 0.00860143 0.00880051 0.00888371 0.00882936 0.00949597 0.00867581 0.00866055 0.00863814 0.00874805] mean value: 0.008799862861633301 key: test_mcc value: [0.37796447 0.51639778 0.6000992 0.09449112 0.07142857 0.6000992 0.60714286 0.33928571 0.6000992 0.6000992 ] mean value: 0.4407107306208915 key: train_mcc value: [0.72066617 0.78152732 0.72339533 0.66616982 0.75261265 0.73758262 0.70910029 0.78298457 0.76762243 0.72266043] mean value: 0.7364321631017087 key: test_fscore value: [0.66666667 0.71428571 0.76923077 0.58823529 0.53333333 0.76923077 0.8 0.66666667 0.82352941 0.82352941] mean value: 0.7154708037060977 key: train_fscore value: [0.85925926 0.88549618 0.85925926 0.82706767 0.87407407 0.86764706 0.84848485 0.88549618 0.87878788 0.85925926] mean value: 0.8644831673533255 key: test_precision value: [0.71428571 0.83333333 0.83333333 0.5 0.5 0.83333333 0.85714286 0.71428571 0.77777778 0.77777778] mean value: 0.7341269841269842 key: train_precision value: [0.86567164 0.92063492 0.87878788 0.859375 0.89393939 0.88059701 0.875 0.92063492 0.90625 0.86567164] mean value: 0.8866562412504576 key: test_recall value: [0.625 0.625 0.71428571 0.71428571 0.57142857 0.71428571 0.75 0.625 0.875 0.875 ] mean value: 0.7089285714285715 key: train_recall value: [0.85294118 0.85294118 0.84057971 0.79710145 0.85507246 0.85507246 0.82352941 0.85294118 0.85294118 0.85294118] mean value: 0.8436061381074168 key: test_accuracy value: [0.6875 0.75 0.8 0.53333333 0.53333333 0.8 0.8 0.66666667 0.8 0.8 ] mean value: 0.7170833333333333 key: train_accuracy value: [0.86029412 0.88970588 0.86131387 0.83211679 0.87591241 0.86861314 0.8540146 0.89051095 0.88321168 0.86131387] mean value: 0.8677007299270073 key: test_roc_auc value: [0.6875 0.75 0.79464286 0.54464286 0.53571429 0.79464286 0.80357143 0.66964286 0.79464286 0.79464286] mean value: 0.7169642857142857 key: train_roc_auc value: [0.86029412 0.88970588 0.86146633 0.83237425 0.87606564 0.8687127 0.85379369 0.8902387 0.88299233 0.8612532 ] mean value: 0.8676896845694799 key: test_jcc value: [0.5 0.55555556 0.625 0.41666667 0.36363636 0.625 0.66666667 0.5 0.7 0.7 ] mean value: 0.5652525252525253 key: train_jcc value: [0.75324675 0.79452055 0.75324675 0.70512821 0.77631579 0.76623377 0.73684211 0.79452055 0.78378378 0.75324675] mean value: 0.7617085005513268 key: TN value: 55 mean value: 55.0 key: FP value: 22 mean value: 22.0 key: FN value: 21 mean value: 21.0 key: TP value: 54 mean value: 54.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.37 Accuracy on Blind test: 0.69 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.54033351 0.71709991 0.54379225 0.56754971 0.61910152 0.5835712 0.58603239 0.52859211 0.69978833 0.53617334] mean value: 0.592203426361084 key: score_time value: [0.01193166 0.01185679 0.01190352 0.01194334 0.01216698 0.01190925 0.01196671 0.01193714 0.01922536 0.01193929] mean value: 0.012678003311157227 key: test_mcc value: [0.25819889 0.12598816 0.75592895 0.46428571 0.09449112 0.18898224 0.875 0.33928571 0.6000992 0.49099025] mean value: 0.4193250227943669 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.57142857 0.53333333 0.83333333 0.71428571 0.58823529 0.5 0.93333333 0.66666667 0.82352941 0.71428571] mean value: 0.687843137254902 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.57142857 1. 0.71428571 0.5 0.6 1. 0.71428571 0.77777778 0.83333333] mean value: 0.7377777777777779 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 0.71428571 0.71428571 0.71428571 0.42857143 0.875 0.625 0.875 0.625 ] mean value: 0.6571428571428571 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.625 0.5625 0.86666667 0.73333333 0.53333333 0.6 0.93333333 0.66666667 0.8 0.73333333] mean value: 0.7054166666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.625 0.5625 0.85714286 0.73214286 0.54464286 0.58928571 0.9375 0.66964286 0.79464286 0.74107143] mean value: 0.7053571428571429 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.4 0.36363636 0.71428571 0.55555556 0.41666667 0.33333333 0.875 0.5 0.7 0.55555556] mean value: 0.5414033189033189 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 57 mean value: 57.0 key: FP value: 26 mean value: 26.0 key: FN value: 19 mean value: 19.0 key: TP value: 50 mean value: 50.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.51 Accuracy on Blind test: 0.77 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01494861 0.01541853 0.01285434 0.0118711 0.01083112 0.01113248 0.01136684 0.01161194 0.01165771 0.01203823] mean value: 0.012373089790344238 key: score_time value: [0.01136184 0.00968146 0.00878263 0.00828695 0.00837064 0.0083673 0.00827742 0.00834441 0.00832987 0.00868535] mean value: 0.008848786354064941 key: test_mcc value: [0.62994079 0.75 0.56407607 0.60714286 0.75592895 0.875 0.87287156 0.76376262 1. 0.73214286] mean value: 0.755086570024059 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.875 0.77777778 0.8 0.83333333 0.93333333 0.94117647 0.85714286 1. 0.875 ] mean value: 0.8692763772175536 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.875 0.63636364 0.75 1. 0.875 0.88888889 1. 1. 0.875 ] mean value: 0.8757395382395382 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.875 1. 0.85714286 0.71428571 1. 1. 0.75 1. 0.875 ] mean value: 0.8821428571428571 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.875 0.73333333 0.8 0.86666667 0.93333333 0.93333333 0.86666667 1. 0.86666667] mean value: 0.86875 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.875 0.75 0.80357143 0.85714286 0.9375 0.92857143 0.875 1. 0.86607143] mean value: 0.8705357142857142 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.77777778 0.63636364 0.66666667 0.71428571 0.875 0.88888889 0.75 1. 0.77777778] mean value: 0.7753427128427128 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 65 mean value: 65.0 key: FP value: 9 mean value: 9.0 key: FN value: 11 mean value: 11.0 key: TP value: 67 mean value: 67.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.8 Accuracy on Blind test: 0.9 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.09008145 0.08893013 0.08802629 0.08733678 0.08760571 0.09081817 0.08841467 0.08839107 0.0875423 0.08779526] mean value: 0.0884941816329956 key: score_time value: [0.01682448 0.01687717 0.01692629 0.01681376 0.01705241 0.07369518 0.01688719 0.01667404 0.01678467 0.0176034 ] mean value: 0.02261385917663574 key: test_mcc value: [0.62994079 0.5 0.60714286 0.33928571 0.07142857 0.6000992 0.73214286 0.33928571 0.6000992 0.47245559] mean value: 0.4891880490193918 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.75 0.8 0.66666667 0.53333333 0.76923077 0.875 0.66666667 0.82352941 0.77777778] mean value: 0.7462204625439919 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.75 0.75 0.625 0.5 0.83333333 0.875 0.71428571 0.77777778 0.7 ] mean value: 0.7382539682539683 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.75 0.85714286 0.71428571 0.57142857 0.71428571 0.875 0.625 0.875 0.875 ] mean value: 0.7607142857142857 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.75 0.8 0.66666667 0.53333333 0.8 0.86666667 0.66666667 0.8 0.73333333] mean value: 0.7429166666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.75 0.80357143 0.66964286 0.53571429 0.79464286 0.86607143 0.66964286 0.79464286 0.72321429] mean value: 0.7419642857142856 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.6 0.66666667 0.5 0.36363636 0.625 0.77777778 0.5 0.7 0.63636364] mean value: 0.6036111111111111 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 55 mean value: 55.0 key: FP value: 18 mean value: 18.0 key: FN value: 21 mean value: 21.0 key: TP value: 58 mean value: 58.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.44 Accuracy on Blind test: 0.74 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00934315 0.00889921 0.00850725 0.00913143 0.0084269 0.00897956 0.0086236 0.00859261 0.00827956 0.00843573] mean value: 0.00872189998626709 key: score_time value: [0.0093441 0.00848818 0.00834203 0.00868416 0.00830793 0.00901246 0.00880361 0.00855732 0.00837278 0.00827336] mean value: 0.008618593215942383 key: test_mcc value: [ 0.37796447 0.5 -0.05455447 0.6000992 -0.18898224 -0.19642857 -0.05455447 0.46770717 -0.21821789 -0.26189246] mean value: 0.09711407379095267 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.75 0.5 0.76923077 0.47058824 0.4 0.42857143 0.54545455 0.47058824 0.52631579] mean value: 0.5527415669985329 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.71428571 0.75 0.44444444 0.83333333 0.4 0.375 0.5 1. 0.44444444 0.45454545] mean value: 0.5916053391053391 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.625 0.75 0.57142857 0.71428571 0.57142857 0.42857143 0.375 0.375 0.5 0.625 ] mean value: 0.5535714285714286 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6875 0.75 0.46666667 0.8 0.4 0.4 0.46666667 0.66666667 0.4 0.4 ] mean value: 0.5437500000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.6875 0.75 0.47321429 0.79464286 0.41071429 0.40178571 0.47321429 0.6875 0.39285714 0.38392857] mean value: 0.5455357142857143 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.6 0.33333333 0.625 0.30769231 0.25 0.27272727 0.375 0.30769231 0.35714286] mean value: 0.3928588078588079 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 41 mean value: 41.0 key: FP value: 34 mean value: 34.0 key: FN value: 35 mean value: 35.0 key: TP value: 42 mean value: 42.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.32 Accuracy on Blind test: 0.66 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.17358732 1.15520883 1.15909719 1.16517234 1.1578052 1.15858912 1.16316414 1.17745185 1.1814363 1.18203735] mean value: 1.1673549652099608 key: score_time value: [0.09102702 0.0880537 0.08931446 0.09419155 0.09115577 0.08767653 0.08834195 0.08730507 0.09308815 0.09478593] mean value: 0.09049401283264161 key: test_mcc value: [0.62994079 0.37796447 0.49099025 0.33928571 0.33928571 0.75592895 0.87287156 0.6000992 0.60714286 0.75592895] mean value: 0.5769438451233067 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.70588235 0.75 0.66666667 0.66666667 0.83333333 0.94117647 0.82352941 0.8 0.88888889] mean value: 0.7876143790849673 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.66666667 0.66666667 0.625 0.625 1. 0.88888889 0.77777778 0.85714286 0.8 ] mean value: 0.7764285714285714 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.75 0.85714286 0.71428571 0.71428571 0.71428571 1. 0.875 0.75 1. ] mean value: 0.8125 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.6875 0.73333333 0.66666667 0.66666667 0.86666667 0.93333333 0.8 0.8 0.86666667] mean value: 0.7833333333333332 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.6875 0.74107143 0.66964286 0.66964286 0.85714286 0.92857143 0.79464286 0.80357143 0.85714286] mean value: 0.7821428571428571 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.54545455 0.6 0.5 0.5 0.71428571 0.88888889 0.7 0.66666667 0.8 ] mean value: 0.6581962481962481 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 57 mean value: 57.0 key: FP value: 14 mean value: 14.0 key: FN value: 19 mean value: 19.0 key: TP value: 62 mean value: 62.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.73 Accuracy on Blind test: 0.87 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.87456441 0.8517611 0.87047458 0.90091586 0.85709262 0.82753348 0.91082954 0.8257854 0.9030807 0.85180211] mean value: 0.8673839807510376 key: score_time value: [0.1779182 0.14194345 0.18658233 0.14119387 0.20161152 0.18412566 0.13825274 0.17037749 0.23327112 0.18102789] mean value: 0.17563042640686036 key: test_mcc value: [0.62994079 0.5 0.60714286 0.33928571 0.33928571 0.75592895 0.87287156 0.73214286 0.73214286 0.75592895] mean value: 0.626467024132959 key: train_mcc value: [0.91215932 0.95598573 0.89863497 0.94160273 0.94160273 0.92787101 0.92791659 0.92710997 0.92791659 0.92791659] mean value: 0.928871623370142 key: test_fscore value: [0.8 0.75 0.8 0.66666667 0.66666667 0.83333333 0.94117647 0.875 0.875 0.88888889] mean value: 0.809673202614379 key: train_fscore value: [0.95652174 0.97810219 0.95035461 0.97101449 0.97101449 0.96453901 0.96402878 0.96350365 0.96402878 0.96402878] mean value: 0.9647136512010267 key: test_precision value: [0.85714286 0.75 0.75 0.625 0.625 1. 0.88888889 0.875 0.875 0.8 ] mean value: 0.8046031746031745 key: train_precision value: [0.94285714 0.97101449 0.93055556 0.97101449 0.97101449 0.94444444 0.94366197 0.95652174 0.94366197 0.94366197] mean value: 0.9518408275741406 key: test_recall value: [0.75 0.75 0.85714286 0.71428571 0.71428571 0.71428571 1. 0.875 0.875 1. ] mean value: 0.825 key: train_recall value: [0.97058824 0.98529412 0.97101449 0.97101449 0.97101449 0.98550725 0.98529412 0.97058824 0.98529412 0.98529412] mean value: 0.9780903665814151 key: test_accuracy value: [0.8125 0.75 0.8 0.66666667 0.66666667 0.86666667 0.93333333 0.86666667 0.86666667 0.86666667] mean value: 0.8095833333333333 key: train_accuracy value: [0.95588235 0.97794118 0.94890511 0.97080292 0.97080292 0.96350365 0.96350365 0.96350365 0.96350365 0.96350365] mean value: 0.9641852726492056 key: test_roc_auc value: [0.8125 0.75 0.80357143 0.66964286 0.66964286 0.85714286 0.92857143 0.86607143 0.86607143 0.85714286] mean value: 0.8080357142857144 key: train_roc_auc value: [0.95588235 0.97794118 0.94874254 0.97080136 0.97080136 0.96334186 0.96366155 0.96355499 0.96366155 0.96366155] mean value: 0.9642050298380221 key: test_jcc value: [0.66666667 0.6 0.66666667 0.5 0.5 0.71428571 0.88888889 0.77777778 0.77777778 0.8 ] mean value: 0.6892063492063492 key: train_jcc value: [0.91666667 0.95714286 0.90540541 0.94366197 0.94366197 0.93150685 0.93055556 0.92957746 0.93055556 0.93055556] mean value: 0.9319289853647368 key: TN value: 60 mean value: 60.0 key: FP value: 13 mean value: 13.0 key: FN value: 16 mean value: 16.0 key: TP value: 63 mean value: 63.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.73 Accuracy on Blind test: 0.87 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.04664946 0.04418659 0.04323983 0.04263473 0.04212356 0.04343629 0.04374647 0.04389095 0.04435182 0.04395628] mean value: 0.0438215970993042 key: score_time value: [0.01153922 0.01023006 0.01027131 0.0100522 0.01002264 0.01016068 0.01012945 0.01014566 0.01025224 0.01023006] mean value: 0.010303354263305664 key: test_mcc value: [0.62994079 0.8819171 0.49099025 0.46428571 0.6000992 0.87287156 0.87287156 0.875 1. 0.87287156] mean value: 0.7560847740334494 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.94117647 0.75 0.71428571 0.76923077 0.92307692 0.94117647 0.93333333 1. 0.94117647] mean value: 0.8713456151691445 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.88888889 0.66666667 0.71428571 0.83333333 1. 0.88888889 1. 1. 0.88888889] mean value: 0.8738095238095239 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 1. 0.85714286 0.71428571 0.71428571 0.85714286 1. 0.875 1. 1. ] mean value: 0.8767857142857143 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.9375 0.73333333 0.73333333 0.8 0.93333333 0.93333333 0.93333333 1. 0.93333333] mean value: 0.875 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.9375 0.74107143 0.73214286 0.79464286 0.92857143 0.92857143 0.9375 1. 0.92857143] mean value: 0.8741071428571429 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.88888889 0.6 0.55555556 0.625 0.85714286 0.88888889 0.875 1. 0.88888889] mean value: 0.7846031746031746 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 66 mean value: 66.0 key: FP value: 9 mean value: 9.0 key: FN value: 10 mean value: 10.0 key: TP value: 67 mean value: 67.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.9 Accuracy on Blind test: 0.95 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02408957 0.04614139 0.04688525 0.05445194 0.05765557 0.05993319 0.04817176 0.04807353 0.04673409 0.04642868] mean value: 0.047856497764587405 key: score_time value: [0.01161027 0.02342272 0.01675224 0.02035213 0.02013803 0.01784778 0.02019763 0.02121401 0.01608253 0.02314377] mean value: 0.019076108932495117 key: test_mcc value: [ 0.51639778 0.25 0.6000992 0.73214286 0.19642857 0.32732684 0.56407607 0.875 0.32732684 -0.04029115] mean value: 0.4348507003539204 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 0.98550725 1. 1. ] mean value: 0.9985507246376812 key: test_fscore value: [0.71428571 0.625 0.76923077 0.85714286 0.57142857 0.61538462 0.66666667 0.93333333 0.70588235 0.33333333] mean value: 0.6791688213747037 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 0.99270073 1. 1. ] mean value: 0.9992700729927007 key: test_precision value: [0.83333333 0.625 0.83333333 0.85714286 0.57142857 0.66666667 1. 1. 0.66666667 0.5 ] mean value: 0.7553571428571428 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 0.98550725 1. 1. ] mean value: 0.9985507246376812 key: test_recall value: [0.625 0.625 0.71428571 0.85714286 0.57142857 0.57142857 0.5 0.875 0.75 0.25 ] mean value: 0.6339285714285714 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.75 0.625 0.8 0.86666667 0.6 0.66666667 0.73333333 0.93333333 0.66666667 0.46666667] mean value: 0.7108333333333333 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 0.99270073 1. 1. ] mean value: 0.9992700729927007 key: test_roc_auc value: [0.75 0.625 0.79464286 0.86607143 0.59821429 0.66071429 0.75 0.9375 0.66071429 0.48214286] mean value: 0.7124999999999999 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 0.99275362 1. 1. ] mean value: 0.9992753623188406 key: test_jcc value: [0.55555556 0.45454545 0.625 0.75 0.4 0.44444444 0.5 0.875 0.54545455 0.2 ] mean value: 0.535 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 0.98550725 1. 1. ] mean value: 0.9985507246376812 key: TN value: 60 mean value: 60.0 key: FP value: 28 mean value: 28.0 key: FN value: 16 mean value: 16.0 key: TP value: 48 mean value: 48.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.48 Accuracy on Blind test: 0.73 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01173639 0.01161218 0.00877666 0.00845313 0.00894594 0.00844526 0.00887132 0.00845528 0.00926495 0.00925374] mean value: 0.009381484985351563 key: score_time value: [0.01118565 0.00959563 0.00870705 0.00857997 0.00914383 0.00842357 0.00890827 0.00826287 0.00876474 0.00837183] mean value: 0.008994340896606445 key: test_mcc value: [ 0.25819889 0.5 0.32732684 0.26189246 -0.07142857 0.18898224 0.32732684 0.49099025 0.49099025 0.6000992 ] mean value: 0.33743783930503735 key: train_mcc value: [0.45593166 0.42762816 0.37278745 0.48902154 0.49060343 0.38686295 0.47659001 0.46009427 0.40162218 0.46134236] mean value: 0.44224840030981083 key: test_fscore value: [0.57142857 0.75 0.61538462 0.66666667 0.42857143 0.5 0.70588235 0.71428571 0.71428571 0.82352941] mean value: 0.6490034475328593 key: train_fscore value: [0.72592593 0.72340426 0.68148148 0.74820144 0.73684211 0.7 0.74647887 0.72180451 0.69172932 0.73758865] mean value: 0.7213456567146808 key: test_precision value: [0.66666667 0.75 0.66666667 0.54545455 0.42857143 0.6 0.66666667 0.83333333 0.83333333 0.77777778] mean value: 0.6768470418470418 key: train_precision value: [0.73134328 0.69863014 0.6969697 0.74285714 0.765625 0.69014085 0.71621622 0.73846154 0.70769231 0.71232877] mean value: 0.7200264934959003 key: test_recall value: [0.5 0.75 0.57142857 0.85714286 0.42857143 0.42857143 0.75 0.625 0.625 0.875 ] mean value: 0.6410714285714285 key: train_recall value: [0.72058824 0.75 0.66666667 0.75362319 0.71014493 0.71014493 0.77941176 0.70588235 0.67647059 0.76470588] mean value: 0.723763853367434 key: test_accuracy value: [0.625 0.75 0.66666667 0.6 0.46666667 0.6 0.66666667 0.73333333 0.73333333 0.8 ] mean value: 0.6641666666666667 key: train_accuracy value: [0.72794118 0.71323529 0.68613139 0.74452555 0.74452555 0.69343066 0.73722628 0.72992701 0.70072993 0.72992701] mean value: 0.7207599828252468 key: test_roc_auc value: [0.625 0.75 0.66071429 0.61607143 0.46428571 0.58928571 0.66071429 0.74107143 0.74107143 0.79464286] mean value: 0.6642857142857144 key: train_roc_auc value: [0.72794118 0.71323529 0.68627451 0.74445865 0.74477835 0.69330776 0.73753197 0.72975277 0.70055413 0.73017903] mean value: 0.7208013640238706 key: test_jcc value: [0.4 0.6 0.44444444 0.5 0.27272727 0.33333333 0.54545455 0.55555556 0.55555556 0.7 ] mean value: 0.4907070707070707 key: train_jcc value: [0.56976744 0.56666667 0.51685393 0.59770115 0.58333333 0.53846154 0.59550562 0.56470588 0.52873563 0.58426966] mean value: 0.5646000857767286 key: TN value: 52 mean value: 52.0 key: FP value: 27 mean value: 27.0 key: FN value: 24 mean value: 24.0 key: TP value: 49 mean value: 49.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.46 Accuracy on Blind test: 0.71 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01394415 0.01133895 0.0137763 0.01342082 0.01502132 0.01487899 0.01419282 0.01436758 0.01505518 0.01504755] mean value: 0.014104366302490234 key: score_time value: [0.00836349 0.00870824 0.01120782 0.01121211 0.01124024 0.01129532 0.01127934 0.01132226 0.01124644 0.0112648 ] mean value: 0.010714006423950196 key: test_mcc value: [0.37796447 0.51639778 0. 0.26189246 0.19642857 0.41931393 1. 0.46770717 0.87287156 0.75592895] mean value: 0.4868504902238303 key: train_mcc value: [0.78679579 0.92657079 0.41647809 0.83357169 0.98550418 0.77646542 0.91240409 0.60139616 0.88938138 0.76447938] mean value: 0.7893046959824239 key: test_fscore value: [0.70588235 0.71428571 0.63636364 0.66666667 0.57142857 0.44444444 1. 0.54545455 0.94117647 0.88888889] mean value: 0.7114591291061879 key: train_fscore value: [0.89473684 0.96296296 0.74193548 0.91891892 0.99280576 0.85950413 0.95588235 0.69230769 0.94444444 0.88311688] mean value: 0.8846615468295399 key: test_precision value: [0.66666667 0.83333333 0.46666667 0.54545455 0.57142857 1. 1. 1. 0.88888889 0.8 ] mean value: 0.7772438672438672 key: train_precision value: [0.80952381 0.97014925 0.58974359 0.86075949 0.98571429 1. 0.95588235 1. 0.89473684 0.79069767] mean value: 0.8857207301848959 key: test_recall value: [0.75 0.625 1. 0.85714286 0.57142857 0.28571429 1. 0.375 1. 1. ] mean value: 0.7464285714285714 key: train_recall value: [1. 0.95588235 1. 0.98550725 1. 0.75362319 0.95588235 0.52941176 1. 1. ] mean value: 0.9180306905370845 key: test_accuracy value: [0.6875 0.75 0.46666667 0.6 0.6 0.66666667 1. 0.66666667 0.93333333 0.86666667] mean value: 0.7237500000000001 key: train_accuracy value: [0.88235294 0.96323529 0.64963504 0.91240876 0.99270073 0.87591241 0.95620438 0.76642336 0.94160584 0.86861314] mean value: 0.8809091884929154 key: test_roc_auc value: [0.6875 0.75 0.5 0.61607143 0.59821429 0.64285714 1. 0.6875 0.92857143 0.85714286] mean value: 0.7267857142857144 key: train_roc_auc value: [0.88235294 0.96323529 0.64705882 0.91187127 0.99264706 0.87681159 0.95620205 0.76470588 0.94202899 0.86956522] mean value: 0.8806479113384483 key: test_jcc value: [0.54545455 0.55555556 0.46666667 0.5 0.4 0.28571429 1. 0.375 0.88888889 0.8 ] mean value: 0.5817279942279943 key: train_jcc value: [0.80952381 0.92857143 0.58974359 0.85 0.98571429 0.75362319 0.91549296 0.52941176 0.89473684 0.79069767] mean value: 0.804751554093514 key: TN value: 53 mean value: 53.0 key: FP value: 19 mean value: 19.0 key: FN value: 23 mean value: 23.0 key: TP value: 57 mean value: 57.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.7 Accuracy on Blind test: 0.85 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01267743 0.01273584 0.01261234 0.01352763 0.01351833 0.01287937 0.01380897 0.01409888 0.01330662 0.01315832] mean value: 0.01323237419128418 key: score_time value: [0.01120472 0.01123285 0.0112319 0.0111711 0.01120114 0.0111866 0.01113701 0.01134038 0.01117802 0.0111804 ] mean value: 0.011206412315368652 key: test_mcc value: [0.51639778 0.40451992 0.75592895 0.49099025 0.09449112 0.26189246 0.64465837 0.875 0.87287156 0.56407607] mean value: 0.5480826484564301 key: train_mcc value: [0.85442069 0.69156407 0.82543222 0.92709446 0.82498207 0.73332722 0.48045763 0.89863497 0.92709446 0.72794365] mean value: 0.7890951434474077 key: test_fscore value: [0.71428571 0.73684211 0.83333333 0.75 0.58823529 0.66666667 0.84210526 0.93333333 0.94117647 0.66666667] mean value: 0.767264484741265 key: train_fscore value: [0.92857143 0.85 0.896 0.96402878 0.91390728 0.87179487 0.75977654 0.94736842 0.96296296 0.8173913 ] mean value: 0.8911801586789199 key: test_precision value: [0.83333333 0.63636364 1. 0.66666667 0.5 0.54545455 0.72727273 1. 0.88888889 1. ] mean value: 0.7797979797979797 key: train_precision value: [0.90277778 0.73913043 1. 0.95714286 0.84146341 0.7816092 0.61261261 0.96923077 0.97014925 1. ] mean value: 0.8774116315314415 key: test_recall value: [0.625 0.875 0.71428571 0.85714286 0.71428571 0.85714286 1. 0.875 1. 0.5 ] mean value: 0.8017857142857142 key: train_recall value: [0.95588235 1. 0.8115942 0.97101449 1. 0.98550725 1. 0.92647059 0.95588235 0.69117647] mean value: 0.9297527706734868 key: test_accuracy value: [0.75 0.6875 0.86666667 0.73333333 0.53333333 0.6 0.8 0.93333333 0.93333333 0.73333333] mean value: 0.7570833333333333 key: train_accuracy value: [0.92647059 0.82352941 0.90510949 0.96350365 0.90510949 0.8540146 0.68613139 0.94890511 0.96350365 0.84671533] mean value: 0.8822992700729927 key: test_roc_auc value: [0.75 0.6875 0.85714286 0.74107143 0.54464286 0.61607143 0.78571429 0.9375 0.92857143 0.75 ] mean value: 0.7598214285714285 key: train_roc_auc value: [0.92647059 0.82352941 0.9057971 0.96344842 0.90441176 0.85304774 0.6884058 0.94874254 0.96344842 0.84558824] mean value: 0.8822890025575447 key: test_jcc value: [0.55555556 0.58333333 0.71428571 0.6 0.41666667 0.5 0.72727273 0.875 0.88888889 0.5 ] mean value: 0.6361002886002887 key: train_jcc value: [0.86666667 0.73913043 0.8115942 0.93055556 0.84146341 0.77272727 0.61261261 0.9 0.92857143 0.69117647] mean value: 0.8094498059037077 key: TN value: 54 mean value: 54.0 key: FP value: 15 mean value: 15.0 key: FN value: 22 mean value: 22.0 key: TP value: 61 mean value: 61.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.45 Accuracy on Blind test: 0.63 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.12404799 0.10369802 0.10437322 0.10355759 0.10415196 0.10292411 0.10479069 0.10424972 0.10479093 0.10378075] mean value: 0.10603649616241455 key: score_time value: [0.01572347 0.01438046 0.01443005 0.01451278 0.01436782 0.01480412 0.01446128 0.01449037 0.01445508 0.01472044] mean value: 0.0146345853805542 key: test_mcc value: [0.62994079 0.8819171 0.66143783 0.76376262 0.73214286 0.75592895 1. 0.76376262 1. 0.87287156] mean value: 0.8061764315560284 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.94117647 0.82352941 0.875 0.85714286 0.83333333 1. 0.85714286 1. 0.94117647] mean value: 0.8928501400560224 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.88888889 0.7 0.77777778 0.85714286 1. 1. 1. 1. 0.88888889] mean value: 0.896984126984127 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 1. 1. 1. 0.85714286 0.71428571 1. 0.75 1. 1. ] mean value: 0.9071428571428571 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.9375 0.8 0.86666667 0.86666667 0.86666667 1. 0.86666667 1. 0.93333333] mean value: 0.8950000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.9375 0.8125 0.875 0.86607143 0.85714286 1. 0.875 1. 0.92857143] mean value: 0.8964285714285714 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.88888889 0.7 0.77777778 0.75 0.71428571 1. 0.75 1. 0.88888889] mean value: 0.8136507936507936 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 67 mean value: 67.0 key: FP value: 7 mean value: 7.0 key: FN value: 9 mean value: 9.0 key: TP value: 69 mean value: 69.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.76 Accuracy on Blind test: 0.89 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.02649498 0.04828382 0.0344305 0.04434419 0.03334451 0.03308582 0.03616285 0.03474283 0.02912116 0.03139949] mean value: 0.03514101505279541 key: score_time value: [0.01983929 0.02085829 0.02037263 0.0226109 0.03466535 0.02397752 0.01654959 0.02532196 0.01791954 0.01661181] mean value: 0.021872687339782714 key: test_mcc value: [0.62994079 0.77459667 0.56407607 0.6000992 0.87287156 0.64465837 0.87287156 0.875 1. 0.73214286] mean value: 0.7566257080808041 key: train_mcc value: [0.98540068 0.98540068 0.98550725 0.98550725 0.98550418 1. 0.97080136 1. 0.97120941 1. ] mean value: 0.9869330800393877 key: test_fscore value: [0.8 0.88888889 0.77777778 0.76923077 0.92307692 0.72727273 0.94117647 0.93333333 1. 0.875 ] mean value: 0.8635756890168654 key: train_fscore value: [0.99259259 0.99270073 0.99270073 0.99270073 0.99280576 1. 0.98529412 1. 0.98507463 1. ] mean value: 0.9933869282282028 key: test_precision value: [0.85714286 0.8 0.63636364 0.83333333 1. 1. 0.88888889 1. 1. 0.875 ] mean value: 0.8890728715728716 key: train_precision value: [1. 0.98550725 1. 1. 0.98571429 1. 0.98529412 1. 1. 1. ] mean value: 0.9956515649738156 key: test_recall value: [0.75 1. 1. 0.71428571 0.85714286 0.57142857 1. 0.875 1. 0.875 ] mean value: 0.8642857142857142 key: train_recall value: [0.98529412 1. 0.98550725 0.98550725 1. 1. 0.98529412 1. 0.97058824 1. ] mean value: 0.9912190963341858 key: test_accuracy value: [0.8125 0.875 0.73333333 0.8 0.93333333 0.8 0.93333333 0.93333333 1. 0.86666667] mean value: 0.86875 key: train_accuracy value: [0.99264706 0.99264706 0.99270073 0.99270073 0.99270073 1. 0.98540146 1. 0.98540146 1. ] mean value: 0.993419922713611 key: test_roc_auc value: [0.8125 0.875 0.75 0.79464286 0.92857143 0.78571429 0.92857143 0.9375 1. 0.86607143] mean value: 0.8678571428571429 key: train_roc_auc value: [0.99264706 0.99264706 0.99275362 0.99275362 0.99264706 1. 0.98540068 1. 0.98529412 1. ] mean value: 0.9934143222506393 key: test_jcc value: [0.66666667 0.8 0.63636364 0.625 0.85714286 0.57142857 0.88888889 0.875 1. 0.77777778] mean value: 0.7698268398268397 key: train_jcc value: [0.98529412 0.98550725 0.98550725 0.98550725 0.98571429 1. 0.97101449 1. 0.97058824 1. ] mean value: 0.986913287053952 key: TN value: 66 mean value: 66.0 key: FP value: 10 mean value: 10.0 key: FN value: 10 mean value: 10.0 key: TP value: 66 mean value: 66.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.83 Accuracy on Blind test: 0.92 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.01622009 0.01859188 0.01931643 0.01900101 0.0448873 0.04830527 0.04416037 0.04434729 0.04451132 0.04420733] mean value: 0.03435482978820801 key: score_time value: [0.01259899 0.01189089 0.01194811 0.0118506 0.02117038 0.02203035 0.02284622 0.02364111 0.01663518 0.02374792] mean value: 0.01783597469329834 key: test_mcc value: [ 0.28867513 0.40451992 0.46428571 0.19642857 -0.33928571 0.07142857 0.46770717 0.09449112 0.33928571 0.07142857] mean value: 0.20589647722432364 key: train_mcc value: [1. 1. 0.98550725 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9985507246376812 key: test_fscore value: [0.5 0.61538462 0.71428571 0.57142857 0.28571429 0.53333333 0.54545455 0.46153846 0.66666667 0.53333333] mean value: 0.5427139527139527 key: train_fscore value: [1. 1. 0.99270073 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9992700729927007 key: test_precision value: [0.75 0.8 0.71428571 0.57142857 0.28571429 0.5 1. 0.6 0.71428571 0.57142857] mean value: 0.6507142857142857 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.375 0.5 0.71428571 0.57142857 0.28571429 0.57142857 0.375 0.375 0.625 0.5 ] mean value: 0.4892857142857142 key: train_recall value: [1. 1. 0.98550725 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9985507246376812 key: test_accuracy value: [0.625 0.6875 0.73333333 0.6 0.33333333 0.53333333 0.66666667 0.53333333 0.66666667 0.53333333] mean value: 0.5912499999999999 key: train_accuracy value: [1. 1. 0.99270073 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9992700729927007 key: test_roc_auc value: [0.625 0.6875 0.73214286 0.59821429 0.33035714 0.53571429 0.6875 0.54464286 0.66964286 0.53571429] mean value: 0.5946428571428573 key: train_roc_auc value: [1. 1. 0.99275362 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9992753623188406 key: test_jcc value: [0.33333333 0.44444444 0.55555556 0.4 0.16666667 0.36363636 0.375 0.3 0.5 0.36363636] mean value: 0.38022727272727275 key: train_jcc value: [1. 1. 0.98550725 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9985507246376812 key: TN value: 53 mean value: 53.0 key: FP value: 39 mean value: 39.0 key: FN value: 23 mean value: 23.0 key: TP value: 37 mean value: 37.0 key: trainingY_neg /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.22 Accuracy on Blind test: 0.61 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.32300711 0.29841375 0.2976191 0.29853249 0.30251598 0.29544401 0.29952025 0.30380344 0.30649114 0.30230904] mean value: 0.30276563167572024 key: score_time value: [0.00929809 0.00880408 0.00938535 0.00878072 0.00879741 0.00877094 0.00892997 0.00889921 0.00881052 0.00874591] mean value: 0.008922219276428223 key: test_mcc value: [0.62994079 0.77459667 0.56407607 0.32732684 0.6000992 0.87287156 0.87287156 0.875 1. 0.87287156] mean value: 0.7389654248742837 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.88888889 0.77777778 0.61538462 0.76923077 0.92307692 0.94117647 0.93333333 1. 0.94117647] mean value: 0.8590045248868778 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.8 0.63636364 0.66666667 0.83333333 1. 0.88888889 1. 1. 0.88888889] mean value: 0.8571284271284272 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 1. 1. 0.57142857 0.71428571 0.85714286 1. 0.875 1. 1. ] mean value: 0.8767857142857143 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.875 0.73333333 0.66666667 0.8 0.93333333 0.93333333 0.93333333 1. 0.93333333] mean value: 0.8620833333333333 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.875 0.75 0.66071429 0.79464286 0.92857143 0.92857143 0.9375 1. 0.92857143] mean value: 0.8616071428571429 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.8 0.63636364 0.44444444 0.625 0.85714286 0.88888889 0.875 1. 0.88888889] mean value: 0.7682395382395383 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 64 mean value: 64.0 key: FP value: 9 mean value: 9.0 key: FN value: 12 mean value: 12.0 key: TP value: 67 mean value: 67.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.83 Accuracy on Blind test: 0.92 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01370049 0.0166471 0.01660919 0.01670527 0.01712155 0.01718688 0.01706386 0.01711583 0.01714063 0.0173409 ] mean value: 0.016663169860839842 key: score_time value: [0.01170731 0.01148653 0.01205492 0.01254296 0.014395 0.01411581 0.01300907 0.01320004 0.01338458 0.01304603] mean value: 0.012894225120544434 key: test_mcc value: [0.67419986 0.25819889 0.6000992 0.53452248 0.47245559 0.34247476 0.66143783 0.46770717 0.66143783 0.49099025] mean value: 0.5163523867066572 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.76923077 0.57142857 0.76923077 0.6 0.66666667 0.54545455 0.76923077 0.54545455 0.76923077 0.71428571] mean value: 0.6720213120213121 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.66666667 0.83333333 1. 0.8 0.75 1. 1. 1. 0.83333333] mean value: 0.8883333333333334 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.625 0.5 0.71428571 0.42857143 0.57142857 0.42857143 0.625 0.375 0.625 0.625 ] mean value: 0.5517857142857142 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.625 0.8 0.73333333 0.73333333 0.66666667 0.8 0.66666667 0.8 0.73333333] mean value: 0.7370833333333333 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.625 0.79464286 0.71428571 0.72321429 0.65178571 0.8125 0.6875 0.8125 0.74107143] mean value: 0.7375 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.625 0.4 0.625 0.42857143 0.5 0.375 0.625 0.375 0.625 0.55555556] mean value: 0.5134126984126983 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 71 mean value: 71.0 key: FP value: 33 mean value: 33.0 key: FN value: 5 mean value: 5.0 key: TP value: 43 mean value: 43.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.61 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:206: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:207: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_CV['Resampling'] = rs_smnc /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:212: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:213: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_BT['Resampling'] = rs_smnc Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.04506564 0.0397656 0.03775096 0.02856636 0.02827263 0.03218317 0.03213382 0.03201056 0.03272009 0.03276658] mean value: 0.03412353992462158 key: score_time value: [0.03038287 0.02150035 0.02133131 0.02151036 0.02205992 0.0203793 0.02163863 0.01988029 0.01317859 0.02244115] mean value: 0.021430277824401857 key: test_mcc value: [0.51639778 0.51639778 0.6000992 0.60714286 0.6000992 0.6000992 1. 0.76376262 0.875 0.60714286] mean value: 0.6686141483547269 key: train_mcc value: [0.98540068 0.94158382 0.97122151 0.95630861 0.97080136 0.98550418 0.94160273 0.92944673 0.94199209 0.95629932] mean value: 0.9580161023288083 key: test_fscore value: [0.71428571 0.71428571 0.76923077 0.8 0.76923077 0.76923077 1. 0.85714286 0.93333333 0.8 ] mean value: 0.8126739926739928 key: train_fscore value: [0.99259259 0.97014925 0.98529412 0.97810219 0.98550725 0.99280576 0.97058824 0.96183206 0.97014925 0.97777778] mean value: 0.9784798483396452 key: test_precision value: [0.83333333 0.83333333 0.83333333 0.75 0.83333333 0.83333333 1. 1. 1. 0.85714286] mean value: 0.8773809523809524 key: train_precision value: [1. 0.98484848 1. 0.98529412 0.98550725 0.98571429 0.97058824 1. 0.98484848 0.98507463] mean value: 0.9881875481594914 key: test_recall value: [0.625 0.625 0.71428571 0.85714286 0.71428571 0.71428571 1. 0.75 0.875 0.75 ] mean value: 0.7625 key: train_recall value: [0.98529412 0.95588235 0.97101449 0.97101449 0.98550725 1. 0.97058824 0.92647059 0.95588235 0.97058824] mean value: 0.9692242114236999 key: test_accuracy value: [0.75 0.75 0.8 0.8 0.8 0.8 1. 0.86666667 0.93333333 0.8 ] mean value: 0.8300000000000001 key: train_accuracy value: [0.99264706 0.97058824 0.98540146 0.97810219 0.98540146 0.99270073 0.97080292 0.96350365 0.97080292 0.97810219] mean value: 0.9788052812365823 key: test_roc_auc value: [0.75 0.75 0.79464286 0.80357143 0.79464286 0.79464286 1. 0.875 0.9375 0.80357143] mean value: 0.8303571428571429 key: train_roc_auc value: [0.99264706 0.97058824 0.98550725 0.97815431 0.98540068 0.99264706 0.97080136 0.96323529 0.9706948 0.97804774] mean value: 0.9787723785166241 key: test_jcc value: [0.55555556 0.55555556 0.625 0.66666667 0.625 0.625 1. 0.75 0.875 0.66666667] mean value: 0.6944444444444444 key: train_jcc value: [0.98529412 0.94202899 0.97101449 0.95714286 0.97142857 0.98571429 0.94285714 0.92647059 0.94202899 0.95652174] mean value: 0.9580501765923761 key: TN value: 68 mean value: 68.0 key: FP value: 18 mean value: 18.0 key: FN value: 8 mean value: 8.0 key: TP value: 58 mean value: 58.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.7 Accuracy on Blind test: 0.85 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.09967899 0.17394996 0.11551142 0.1964004 0.19705701 0.19939947 0.19976878 0.33203363 0.41508412 0.18141294] mean value: 0.21102967262268066 key: score_time value: [0.01223731 0.01162314 0.02037287 0.02161789 0.0222702 0.02059126 0.02248669 0.02017426 0.03288436 0.01790738] mean value: 0.020216536521911622 key: test_mcc value: [0.77459667 0.37796447 0.6000992 0.87287156 0.6000992 0.73214286 1. 1. 0.46428571 0.73214286] mean value: 0.7154202528064066 key: train_mcc value: [1. 0.98540068 0.98550725 0.98550725 0.97080136 1. 0.97080136 0.98550418 1. 0.98550418] mean value: 0.9869026255628051 key: test_fscore value: [0.85714286 0.66666667 0.76923077 0.92307692 0.76923077 0.85714286 1. 1. 0.75 0.875 ] mean value: 0.8467490842490843 key: train_fscore value: [1. 0.99259259 0.99270073 0.99270073 0.98550725 1. 0.98529412 0.99259259 1. 0.99259259] mean value: 0.9933980601655662 key: test_precision value: [1. 0.71428571 0.83333333 1. 0.83333333 0.85714286 1. 1. 0.75 0.875 ] mean value: 0.8863095238095239 key: train_precision value: [1. 1. 1. 1. 0.98550725 1. 0.98529412 1. 1. 1. ] mean value: 0.997080136402387 key: test_recall value: [0.75 0.625 0.71428571 0.85714286 0.71428571 0.85714286 1. 1. 0.75 0.875 ] mean value: 0.8142857142857143 key: train_recall value: [1. 0.98529412 0.98550725 0.98550725 0.98550725 1. 0.98529412 0.98529412 1. 0.98529412] mean value: 0.9897698209718669 key: test_accuracy value: [0.875 0.6875 0.8 0.93333333 0.8 0.86666667 1. 1. 0.73333333 0.86666667] mean value: 0.85625 key: train_accuracy value: [1. 0.99264706 0.99270073 0.99270073 0.98540146 1. 0.98540146 0.99270073 1. 0.99270073] mean value: 0.9934252898239586 key: test_roc_auc value: [0.875 0.6875 0.79464286 0.92857143 0.79464286 0.86607143 1. 1. 0.73214286 0.86607143] mean value: 0.8544642857142856 key: train_roc_auc value: [1. 0.99264706 0.99275362 0.99275362 0.98540068 1. 0.98540068 0.99264706 1. 0.99264706] mean value: 0.993424978687127 key: test_jcc value: [0.75 0.5 0.625 0.85714286 0.625 0.75 1. 1. 0.6 0.77777778] mean value: 0.7484920634920635 key: train_jcc value: [1. 0.98529412 0.98550725 0.98550725 0.97142857 1. 0.97101449 0.98529412 1. 0.98529412] mean value: 0.9869339909876995 key: TN value: 68 mean value: 68.0 key: FP value: 14 mean value: 14.0 key: FN value: 8 mean value: 8.0 key: TP value: 62 mean value: 62.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.65 Accuracy on Blind test: 0.82 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02371693 0.03020382 0.02565432 0.02507687 0.0290494 0.03059435 0.02941561 0.02647495 0.02864885 0.02442336] mean value: 0.02732584476470947 key: score_time value: [0.01136851 0.01143813 0.01154709 0.01141238 0.01138926 0.01261711 0.01146507 0.01139593 0.01141238 0.01151299] mean value: 0.011555886268615723 key: test_mcc value: [0.37796447 0.51639778 0.66143783 0.60714286 0.19642857 0.53452248 1. 0.76376262 1. 0.73214286] mean value: 0.6389799465634804 key: train_mcc value: [0.85331034 0.86774089 0.8687127 0.82480818 0.83951407 0.81031543 0.81027501 0.81092683 0.81027501 0.83951407] mean value: 0.8335392533265381 key: test_fscore value: [0.66666667 0.71428571 0.82352941 0.8 0.57142857 0.6 1. 0.85714286 1. 0.875 ] mean value: 0.7908053221288516 key: train_fscore value: [0.92753623 0.93430657 0.93430657 0.91304348 0.91970803 0.90510949 0.9037037 0.90225564 0.9037037 0.91970803] mean value: 0.9163381442781466 key: test_precision value: [0.71428571 0.83333333 0.7 0.75 0.57142857 1. 1. 1. 1. 0.875 ] mean value: 0.844404761904762 key: train_precision value: [0.91428571 0.92753623 0.94117647 0.91304348 0.92647059 0.91176471 0.91044776 0.92307692 0.91044776 0.91304348] mean value: 0.9191293112862375 key: test_recall value: [0.625 0.625 1. 0.85714286 0.57142857 0.42857143 1. 0.75 1. 0.875 ] mean value: 0.7732142857142857 key: train_recall value: [0.94117647 0.94117647 0.92753623 0.91304348 0.91304348 0.89855072 0.89705882 0.88235294 0.89705882 0.92647059] mean value: 0.9137468030690536 key: test_accuracy value: [0.6875 0.75 0.8 0.8 0.6 0.73333333 1. 0.86666667 1. 0.86666667] mean value: 0.8104166666666668 key: train_accuracy value: [0.92647059 0.93382353 0.93430657 0.91240876 0.91970803 0.90510949 0.90510949 0.90510949 0.90510949 0.91970803] mean value: 0.9166863460712751 key: test_roc_auc value: [0.6875 0.75 0.8125 0.80357143 0.59821429 0.71428571 1. 0.875 1. 0.86607143] mean value: 0.8107142857142857 key: train_roc_auc value: [0.92647059 0.93382353 0.93435635 0.91240409 0.91975703 0.90515772 0.90505115 0.90494459 0.90505115 0.91975703] mean value: 0.9166773231031543 key: test_jcc value: [0.5 0.55555556 0.7 0.66666667 0.4 0.42857143 1. 0.75 1. 0.77777778] mean value: 0.6778571428571428 key: train_jcc value: [0.86486486 0.87671233 0.87671233 0.84 0.85135135 0.82666667 0.82432432 0.82191781 0.82432432 0.85135135] mean value: 0.8458225348636308 key: TN value: 64 mean value: 64.0 key: FP value: 17 mean value: 17.0 key: FN value: 12 mean value: 12.0 key: TP value: 59 mean value: 59.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.66 Accuracy on Blind test: 0.84 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.69967294 0.60888672 0.60500884 0.60262251 0.75237751 0.60629177 0.61903977 0.69859052 0.62684703 0.60913682] mean value: 0.6428474426269531 key: score_time value: [0.01302028 0.014256 0.01426744 0.01292682 0.01295495 0.013134 0.01429248 0.01316833 0.01415896 0.01430464] mean value: 0.013648390769958496 key: test_mcc value: [0.62994079 0.37796447 0.6000992 0.60714286 0.19642857 0.64465837 1. 1. 0.875 0.60714286] mean value: 0.6538377116441508 key: train_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [1. 1. 1. 0.89863497 1. 1. 0.97120941 1. 1. 1. ] mean value: 0.9869844378081876 key: test_fscore value: [0.8 0.66666667 0.76923077 0.8 0.57142857 0.72727273 1. 1. 0.93333333 0.8 ] mean value: 0.8067932067932068 key: train_fscore value: [1. 1. 1. 0.95035461 1. 1. 0.98507463 1. 1. 1. ] mean value: 0.993542923679475 key: test_precision value: [0.85714286 0.71428571 0.83333333 0.75 0.57142857 1. 1. 1. 1. 0.85714286] mean value: 0.8583333333333334 key: train_precision value: [1. 1. 1. 0.93055556 1. 1. 1. 1. 1. 1. ] mean value: 0.9930555555555556 key: test_recall value: [0.75 0.625 0.71428571 0.85714286 0.57142857 0.57142857 1. 1. 0.875 0.75 ] mean value: 0.7714285714285714 key: train_recall value: [1. 1. 1. 0.97101449 1. 1. 0.97058824 1. 1. 1. ] mean value: 0.994160272804774 key: test_accuracy value: [0.8125 0.6875 0.8 0.8 0.6 0.8 1. 1. 0.93333333 0.8 ] mean value: 0.8233333333333335 key: train_accuracy value: [1. 1. 1. 0.94890511 1. 1. 0.98540146 1. 1. 1. ] mean value: 0.9934306569343067 key: test_roc_auc value: [0.8125 0.6875 0.79464286 0.80357143 0.59821429 0.78571429 1. 1. 0.9375 0.80357143] mean value: 0.8223214285714284 key: train_roc_auc value: [1. 1. 1. 0.94874254 1. 1. 0.98529412 1. 1. 1. ] mean value: 0.9934036658141517 key: test_jcc value: [0.66666667 0.5 0.625 0.66666667 0.4 0.57142857 1. 1. 0.875 0.66666667] mean value: 0.6971428571428572 key: train_jcc value: [1. 1. 1. 0.90540541 1. 1. 0.97058824 1. 1. 1. ] mean value: 0.9875993640699523 key: TN value: 66 mean value: 66.0 key: FP value: 17 mean value: 17.0 key: FN value: 10 mean value: 10.0 key: TP value: 59 mean value: 59.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.76 Accuracy on Blind test: 0.89 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01184487 0.01161718 0.00880337 0.00857401 0.00852346 0.00843644 0.00820518 0.00820851 0.00827384 0.00852752] mean value: 0.009101438522338866 key: score_time value: [0.01136661 0.01089358 0.00871873 0.00862837 0.00832224 0.00832319 0.00829744 0.00830674 0.00830483 0.00839972] mean value: 0.008956146240234376 key: test_mcc value: [-0.12598816 0.37796447 0.07142857 0.37796447 0.05455447 0.18898224 0.60714286 0.66143783 0.49099025 0.46428571] mean value: 0.3168762721066597 key: train_mcc value: [0.48661135 0.53311399 0.50469525 0.51832907 0.55776902 0.47754676 0.48902154 0.52059257 0.51856637 0.46009427] mean value: 0.5066340188686949 key: test_fscore value: [0.4 0.70588235 0.53333333 0.70588235 0.46153846 0.5 0.8 0.76923077 0.71428571 0.75 ] mean value: 0.6340152984270632 key: train_fscore value: [0.73282443 0.77777778 0.74626866 0.75912409 0.7079646 0.76433121 0.74074074 0.74418605 0.7518797 0.72180451] mean value: 0.744690175930603 key: test_precision value: [0.42857143 0.66666667 0.5 0.6 0.5 0.6 0.85714286 1. 0.83333333 0.75 ] mean value: 0.6735714285714286 key: train_precision value: [0.76190476 0.73684211 0.76923077 0.76470588 0.90909091 0.68181818 0.74626866 0.78688525 0.76923077 0.73846154] mean value: 0.7664438819971086 key: test_recall value: [0.375 0.75 0.57142857 0.85714286 0.42857143 0.42857143 0.75 0.625 0.625 0.75 ] mean value: 0.6160714285714286 key: train_recall value: [0.70588235 0.82352941 0.72463768 0.75362319 0.57971014 0.86956522 0.73529412 0.70588235 0.73529412 0.70588235] mean value: 0.7339300937766412 key: test_accuracy value: [0.4375 0.6875 0.53333333 0.66666667 0.53333333 0.6 0.8 0.8 0.73333333 0.73333333] mean value: 0.6525000000000001 key: train_accuracy value: [0.74264706 0.76470588 0.75182482 0.75912409 0.75912409 0.72992701 0.74452555 0.75912409 0.75912409 0.72992701] mean value: 0.7500053671103478 key: test_roc_auc value: [0.4375 0.6875 0.53571429 0.67857143 0.52678571 0.58928571 0.80357143 0.8125 0.74107143 0.73214286] mean value: 0.6544642857142857 key: train_roc_auc value: [0.74264706 0.76470588 0.75202472 0.75916454 0.76044331 0.72890026 0.74445865 0.75873828 0.75895141 0.72975277] mean value: 0.7499786871270249 key: test_jcc value: [0.25 0.54545455 0.36363636 0.54545455 0.3 0.33333333 0.66666667 0.625 0.55555556 0.6 ] mean value: 0.478510101010101 key: train_jcc value: [0.57831325 0.63636364 0.5952381 0.61176471 0.54794521 0.6185567 0.58823529 0.59259259 0.60240964 0.56470588] mean value: 0.5936125004623911 key: TN value: 52 mean value: 52.0 key: FP value: 29 mean value: 29.0 key: FN value: 24 mean value: 24.0 key: TP value: 47 mean value: 47.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.41 Accuracy on Blind test: 0.71 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00870776 0.00836706 0.00858164 0.00864911 0.00872946 0.00858092 0.00840116 0.00910521 0.00868034 0.00934458] mean value: 0.008714723587036132 key: score_time value: [0.00839782 0.00851846 0.00833011 0.00887752 0.00852442 0.00829554 0.00853753 0.00869346 0.0083499 0.00939226] mean value: 0.008591699600219726 key: test_mcc value: [-0.37796447 0.37796447 -0.07142857 0.07142857 -0.21821789 0.53452248 -0.04029115 0.56407607 0.25 -0.20044593] mean value: 0.08896435887710355 key: train_mcc value: [0.34459104 0.48960131 0.42586031 0.42586031 0.40740412 0.4055104 0.35292142 0.42688031 0.44946013 0.45151662] mean value: 0.41796059682716213 key: test_fscore value: [0.26666667 0.70588235 0.42857143 0.53333333 0.30769231 0.6 0.33333333 0.66666667 0.22222222 0.18181818] mean value: 0.42461864932453164 key: train_fscore value: [0.63414634 0.72440945 0.67741935 0.67741935 0.67716535 0.68217054 0.62184874 0.66666667 0.6984127 0.69354839] mean value: 0.6753206888598037 key: test_precision value: [0.28571429 0.66666667 0.42857143 0.5 0.33333333 1. 0.5 1. 1. 0.33333333] mean value: 0.6047619047619046 key: train_precision value: [0.70909091 0.77966102 0.76363636 0.76363636 0.74137931 0.73333333 0.7254902 0.76923077 0.75862069 0.76785714] mean value: 0.7511936094812467 key: test_recall value: [0.25 0.75 0.42857143 0.57142857 0.28571429 0.42857143 0.25 0.5 0.125 0.125 ] mean value: 0.37142857142857144 key: train_recall value: [0.57352941 0.67647059 0.60869565 0.60869565 0.62318841 0.63768116 0.54411765 0.58823529 0.64705882 0.63235294] mean value: 0.614002557544757 key: test_accuracy value: [0.3125 0.6875 0.46666667 0.53333333 0.4 0.73333333 0.46666667 0.73333333 0.53333333 0.4 ] mean value: 0.5266666666666666 key: train_accuracy value: [0.66911765 0.74264706 0.7080292 0.7080292 0.70072993 0.70072993 0.67153285 0.7080292 0.72262774 0.72262774] mean value: 0.7054100472305711 key: test_roc_auc value: [0.3125 0.6875 0.46428571 0.53571429 0.39285714 0.71428571 0.48214286 0.75 0.5625 0.41964286] mean value: 0.5321428571428571 key: train_roc_auc value: [0.66911765 0.74264706 0.70875959 0.70875959 0.70130009 0.70119352 0.67060955 0.70716113 0.72208014 0.72197357] mean value: 0.7053601875532822 key: test_jcc value: [0.15384615 0.54545455 0.27272727 0.36363636 0.18181818 0.42857143 0.2 0.5 0.125 0.1 ] mean value: 0.2871053946053946 key: train_jcc value: [0.46428571 0.56790123 0.51219512 0.51219512 0.51190476 0.51764706 0.45121951 0.5 0.53658537 0.5308642 ] mean value: 0.5104798089063991 key: TN value: 52 mean value: 52.0 key: FP value: 48 mean value: 48.0 key: FN value: 24 mean value: 24.0 key: TP value: 28 mean value: 28.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.33 Accuracy on Blind test: 0.66 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00825047 0.0082128 0.00805974 0.00811005 0.00801778 0.00804663 0.00801182 0.00809121 0.00806355 0.00797033] mean value: 0.008083438873291016 key: score_time value: [0.00938988 0.00926828 0.00917339 0.00922346 0.00917268 0.00918603 0.0092423 0.00932908 0.00922585 0.00921774] mean value: 0.009242868423461914 key: test_mcc value: [ 0. -0.37796447 0.07142857 0.19642857 -0.33928571 0.02620712 0.07142857 0.07142857 0.07142857 -0.05455447] mean value: -0.026345468179303454 key: train_mcc value: [0.3974458 0.38655567 0.41698711 0.43493568 0.44223491 0.34665985 0.40218295 0.46524407 0.34677114 0.41602728] mean value: 0.40550444580465417 key: test_fscore value: [0.5 0.26666667 0.53333333 0.57142857 0.28571429 0.22222222 0.53333333 0.53333333 0.53333333 0.42857143] mean value: 0.44079365079365085 key: train_fscore value: [0.69172932 0.66666667 0.70149254 0.69767442 0.68292683 0.65116279 0.6870229 0.704 0.64 0.70588235] mean value: 0.6828557819563523 key: test_precision value: [0.5 0.28571429 0.5 0.57142857 0.28571429 0.5 0.57142857 0.57142857 0.57142857 0.5 ] mean value: 0.48571428571428565 key: train_precision value: [0.70769231 0.72413793 0.72307692 0.75 0.77777778 0.7 0.71428571 0.77192982 0.70175439 0.70588235] mean value: 0.7276537217334698 key: test_recall value: [0.5 0.25 0.57142857 0.57142857 0.28571429 0.14285714 0.5 0.5 0.5 0.375 ] mean value: 0.4196428571428571 key: train_recall value: [0.67647059 0.61764706 0.68115942 0.65217391 0.60869565 0.60869565 0.66176471 0.64705882 0.58823529 0.70588235] mean value: 0.6447783461210571 key: test_accuracy value: [0.5 0.3125 0.53333333 0.6 0.33333333 0.53333333 0.53333333 0.53333333 0.53333333 0.46666667] mean value: 0.48791666666666667 key: train_accuracy value: [0.69852941 0.69117647 0.7080292 0.71532847 0.71532847 0.67153285 0.70072993 0.72992701 0.67153285 0.7080292 ] mean value: 0.7010143838557321 key: test_roc_auc value: [0.5 0.3125 0.53571429 0.59821429 0.33035714 0.50892857 0.53571429 0.53571429 0.53571429 0.47321429] mean value: 0.4866071428571428 key: train_roc_auc value: [0.69852941 0.69117647 0.70822677 0.71579284 0.71611253 0.67199488 0.70044757 0.72932651 0.67092924 0.70801364] mean value: 0.7010549872122762 key: test_jcc value: [0.33333333 0.15384615 0.36363636 0.4 0.16666667 0.125 0.36363636 0.36363636 0.36363636 0.27272727] mean value: 0.2906118881118881 key: train_jcc value: [0.52873563 0.5 0.54022989 0.53571429 0.51851852 0.48275862 0.52325581 0.54320988 0.47058824 0.54545455] mean value: 0.5188465413409199 key: TN value: 42 mean value: 42.0 key: FP value: 44 mean value: 44.0 key: FN value: 34 mean value: 34.0 key: TP value: 32 mean value: 32.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.02 Accuracy on Blind test: 0.53 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.00958729 0.00953531 0.00956225 0.00946403 0.0095284 0.00953674 0.01031899 0.0094645 0.00981331 0.00961852] mean value: 0.009642934799194336 key: score_time value: [0.00861764 0.00929141 0.00861168 0.00863266 0.00858045 0.00858712 0.00863266 0.00867867 0.00924301 0.00912786] mean value: 0.008800315856933593 key: test_mcc value: [ 0.12598816 0.40451992 0.19642857 0.49099025 -0.07142857 0.41931393 0.60714286 0.66143783 0.49099025 0.60714286] mean value: 0.39325260579502824 key: train_mcc value: [0.72129053 0.76770017 0.72266043 0.70801364 0.75261265 0.75369214 0.67983923 0.72321869 0.678815 0.69352089] mean value: 0.7201363365699368 key: test_fscore value: [0.53333333 0.61538462 0.57142857 0.75 0.42857143 0.44444444 0.8 0.76923077 0.71428571 0.8 ] mean value: 0.6426678876678877 key: train_fscore value: [0.86330935 0.88732394 0.86330935 0.85507246 0.87407407 0.88111888 0.83333333 0.85714286 0.83823529 0.84671533] mean value: 0.8599634880720005 key: test_precision value: [0.57142857 0.8 0.57142857 0.66666667 0.42857143 1. 0.85714286 1. 0.83333333 0.85714286] mean value: 0.7585714285714286 key: train_precision value: [0.84507042 0.85135135 0.85714286 0.85507246 0.89393939 0.85135135 0.859375 0.87692308 0.83823529 0.84057971] mean value: 0.8569040921273933 key: test_recall value: [0.5 0.5 0.57142857 0.85714286 0.42857143 0.28571429 0.75 0.625 0.625 0.75 ] mean value: 0.5892857142857142 key: train_recall value: [0.88235294 0.92647059 0.86956522 0.85507246 0.85507246 0.91304348 0.80882353 0.83823529 0.83823529 0.85294118] mean value: 0.8639812446717817 key: test_accuracy value: [0.5625 0.6875 0.6 0.73333333 0.46666667 0.66666667 0.8 0.8 0.73333333 0.8 ] mean value: 0.6849999999999999 key: train_accuracy value: [0.86029412 0.88235294 0.86131387 0.8540146 0.87591241 0.87591241 0.83941606 0.86131387 0.83941606 0.84671533] mean value: 0.8596661657363676 key: test_roc_auc value: [0.5625 0.6875 0.59821429 0.74107143 0.46428571 0.64285714 0.80357143 0.8125 0.74107143 0.80357143] mean value: 0.6857142857142857 key: train_roc_auc value: [0.86029412 0.88235294 0.8612532 0.85400682 0.87606564 0.87563939 0.83919437 0.86114663 0.8394075 0.84676044] mean value: 0.85961210571185 key: test_jcc value: [0.36363636 0.44444444 0.4 0.6 0.27272727 0.28571429 0.66666667 0.625 0.55555556 0.66666667] mean value: 0.48804112554112555 key: train_jcc value: [0.75949367 0.79746835 0.75949367 0.74683544 0.77631579 0.7875 0.71428571 0.75 0.72151899 0.73417722] mean value: 0.7547088845531551 key: TN value: 59 mean value: 59.0 key: FP value: 31 mean value: 31.0 key: FN value: 17 mean value: 17.0 key: TP value: 45 mean value: 45.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.34 Accuracy on Blind test: 0.68 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.5549593 0.55137181 0.55409098 0.67051911 0.5430727 0.54954982 0.56719279 0.6893487 0.55776834 0.52722049] mean value: 0.5765094041824341 key: score_time value: [0.01182675 0.01185775 0.01186991 0.01202655 0.01195168 0.01197886 0.01199174 0.01196313 0.01197791 0.01193333] mean value: 0.011937761306762695 key: test_mcc value: [0.37796447 0.25819889 0.46428571 0.6000992 0.33928571 0.53452248 0.875 0.76376262 0.73214286 0.21821789] mean value: 0.5163479836506467 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.57142857 0.71428571 0.76923077 0.66666667 0.6 0.93333333 0.85714286 0.875 0.57142857] mean value: 0.722518315018315 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.71428571 0.66666667 0.71428571 0.83333333 0.625 1. 1. 1. 0.875 0.66666667] mean value: 0.8095238095238095 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.625 0.5 0.71428571 0.71428571 0.71428571 0.42857143 0.875 0.75 0.875 0.5 ] mean value: 0.6696428571428571 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6875 0.625 0.73333333 0.8 0.66666667 0.73333333 0.93333333 0.86666667 0.86666667 0.6 ] mean value: 0.75125 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.6875 0.625 0.73214286 0.79464286 0.66964286 0.71428571 0.9375 0.875 0.86607143 0.60714286] mean value: 0.7508928571428573 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.4 0.55555556 0.625 0.5 0.42857143 0.875 0.75 0.77777778 0.4 ] mean value: 0.5811904761904763 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 63 mean value: 63.0 key: FP value: 25 mean value: 25.0 key: FN value: 13 mean value: 13.0 key: TP value: 51 mean value: 51.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.55 Accuracy on Blind test: 0.79 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01520491 0.01505613 0.0122366 0.01078939 0.01043081 0.01121283 0.01168776 0.01130247 0.01135254 0.01261473] mean value: 0.01218881607055664 key: score_time value: [0.01149416 0.01020551 0.00834394 0.00830197 0.00831151 0.00833273 0.0082345 0.00830793 0.00831676 0.00827813] mean value: 0.008812713623046874 key: test_mcc value: [0.75 0.8819171 0.76376262 0.53452248 0.87287156 1. 0.87287156 1. 1. 0.60714286] mean value: 0.8283088182369814 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.875 0.94117647 0.875 0.6 0.92307692 1. 0.94117647 1. 1. 0.8 ] mean value: 0.8955429864253392 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.875 0.88888889 0.77777778 1. 1. 1. 0.88888889 1. 1. 0.85714286] mean value: 0.9287698412698413 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.875 1. 1. 0.42857143 0.85714286 1. 1. 1. 1. 0.75 ] mean value: 0.8910714285714286 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.875 0.9375 0.86666667 0.73333333 0.93333333 1. 0.93333333 1. 1. 0.8 ] mean value: 0.9079166666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.875 0.9375 0.875 0.71428571 0.92857143 1. 0.92857143 1. 1. 0.80357143] mean value: 0.90625 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.77777778 0.88888889 0.77777778 0.42857143 0.85714286 1. 0.88888889 1. 1. 0.66666667] mean value: 0.8285714285714285 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 70 mean value: 70.0 key: FP value: 8 mean value: 8.0 key: FN value: 6 mean value: 6.0 key: TP value: 68 mean value: 68.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.76 Accuracy on Blind test: 0.89 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.08640933 0.08637953 0.08623791 0.08841944 0.08629084 0.08756542 0.08693004 0.08604431 0.08667326 0.0857749 ] mean value: 0.0866724967956543 key: score_time value: [0.01676607 0.01685476 0.01676655 0.01686096 0.01674843 0.01675296 0.01690412 0.0167594 0.01677799 0.01685715] mean value: 0.016804838180541994 key: test_mcc value: [0.62994079 0.5 0.49099025 0.6000992 0.07142857 0.75592895 0.87287156 0.66143783 0.73214286 0.75592895] mean value: 0.6070768948847128 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.75 0.75 0.76923077 0.53333333 0.83333333 0.94117647 0.76923077 0.875 0.88888889] mean value: 0.7910193564605329 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.75 0.66666667 0.83333333 0.5 1. 0.88888889 1. 0.875 0.8 ] mean value: 0.8171031746031746 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.75 0.85714286 0.71428571 0.57142857 0.71428571 1. 0.625 0.875 1. ] mean value: 0.7857142857142857 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.75 0.73333333 0.8 0.53333333 0.86666667 0.93333333 0.8 0.86666667 0.86666667] mean value: 0.79625 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.75 0.74107143 0.79464286 0.53571429 0.85714286 0.92857143 0.8125 0.86607143 0.85714286] mean value: 0.7955357142857142 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.6 0.6 0.625 0.36363636 0.71428571 0.88888889 0.625 0.77777778 0.8 ] mean value: 0.6661255411255411 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 61 mean value: 61.0 key: FP value: 16 mean value: 16.0 key: FN value: 15 mean value: 15.0 key: TP value: 60 mean value: 60.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.37 Accuracy on Blind test: 0.71 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00843382 0.00834179 0.00835443 0.00834417 0.00850677 0.00825882 0.00843382 0.00834155 0.00833392 0.00842953] mean value: 0.008377861976623536 key: score_time value: [0.00824547 0.00827909 0.00835085 0.00823879 0.00841045 0.00824952 0.00834084 0.00825453 0.00825787 0.00822473] mean value: 0.008285212516784667 key: test_mcc value: [0.62994079 0.62994079 0.34247476 0.6000992 0.47245559 0.75592895 0.21821789 0.76376262 0.60714286 0.46428571] mean value: 0.5484249149327715 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.82352941 0.8 0.54545455 0.76923077 0.66666667 0.83333333 0.57142857 0.85714286 0.8 0.75 ] mean value: 0.7416786155021449 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.77777778 0.85714286 0.75 0.83333333 0.8 1. 0.66666667 1. 0.85714286 0.75 ] mean value: 0.8292063492063491 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.875 0.75 0.42857143 0.71428571 0.57142857 0.71428571 0.5 0.75 0.75 0.75 ] mean value: 0.6803571428571429 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.8125 0.66666667 0.8 0.73333333 0.86666667 0.6 0.86666667 0.8 0.73333333] mean value: 0.7691666666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.8125 0.65178571 0.79464286 0.72321429 0.85714286 0.60714286 0.875 0.80357143 0.73214286] mean value: 0.7669642857142857 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.7 0.66666667 0.375 0.625 0.5 0.71428571 0.4 0.75 0.66666667 0.6 ] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( 0.5997619047619047 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 65 mean value: 65.0 key: FP value: 24 mean value: 24.0 key: FN value: 11 mean value: 11.0 key: TP value: 52 mean value: 52.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: -0.17 Accuracy on Blind test: 0.45 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.11679173 1.12226534 1.13545108 1.12388873 1.12518239 1.12447619 1.13210273 1.13458633 1.15282416 1.13486743] mean value: 1.1302436113357544 key: score_time value: [0.0877521 0.08782196 0.09261203 0.08724332 0.08948398 0.08766818 0.08709979 0.08709478 0.09637737 0.09251261] mean value: 0.08956661224365234 key: test_mcc value: [0.62994079 0.5 0.49099025 0.64465837 0.33928571 0.64465837 0.87287156 0.875 0.73214286 0.75592895] mean value: 0.6485476862211298 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.75 0.75 0.72727273 0.66666667 0.72727273 0.94117647 0.93333333 0.875 0.88888889] mean value: 0.8059610814022579 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.75 0.66666667 1. 0.625 1. 0.88888889 1. 0.875 0.8 ] mean value: 0.8462698412698414 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.75 0.85714286 0.57142857 0.71428571 0.57142857 1. 0.875 0.875 1. ] mean value: 0.7964285714285714 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.75 0.73333333 0.8 0.66666667 0.8 0.93333333 0.93333333 0.86666667 0.86666667] mean value: 0.8162500000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.75 0.74107143 0.78571429 0.66964286 0.78571429 0.92857143 0.9375 0.86607143 0.85714286] mean value: 0.8133928571428571 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.6 0.6 0.57142857 0.5 0.57142857 0.88888889 0.875 0.77777778 0.8 ] mean value: 0.6851190476190476 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 63 mean value: 63.0 key: FP value: 15 mean value: 15.0 key: FN value: 13 mean value: 13.0 key: TP value: 61 mean value: 61.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.69 Accuracy on Blind test: 0.85 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.84114528 0.84817362 0.81728435 0.89065385 0.85970116 0.91562819 0.90411329 0.85566688 0.83894706 0.84567571] mean value: 0.8616989374160766 key: score_time value: [0.19162846 0.17003179 0.22988105 0.15647268 0.11516118 0.18131018 0.16762185 0.16671157 0.22042584 0.14845037] mean value: 0.17476949691772461 key: test_mcc value: [0.5 0.62994079 0.49099025 0.60714286 0.49099025 0.53452248 0.87287156 0.875 0.875 0.73214286] mean value: 0.6608601053465211 key: train_mcc value: [0.92737353 0.92737353 0.91392776 0.88654289 0.92944673 0.92787101 0.92791659 0.91281179 0.92791659 0.92791659] mean value: 0.9209097014971606 key: test_fscore value: [0.75 0.8 0.75 0.8 0.75 0.6 0.94117647 0.93333333 0.93333333 0.875 ] mean value: 0.8132843137254901 key: train_fscore value: [0.96402878 0.96402878 0.95774648 0.94444444 0.96503497 0.96453901 0.96402878 0.95652174 0.96402878 0.96402878] mean value: 0.9608430519467367 key: test_precision value: [0.75 0.85714286 0.66666667 0.75 0.66666667 1. 0.88888889 1. 1. 0.875 ] mean value: 0.8454365079365079 key: train_precision value: [0.94366197 0.94366197 0.93150685 0.90666667 0.93243243 0.94444444 0.94366197 0.94285714 0.94366197 0.94366197] mean value: 0.9376217394870684 key: test_recall value: [0.75 0.75 0.85714286 0.85714286 0.85714286 0.42857143 1. 0.875 0.875 0.875 ] mean value: 0.8125 key: train_recall value: [0.98529412 0.98529412 0.98550725 0.98550725 1. 0.98550725 0.98529412 0.97058824 0.98529412 0.98529412] mean value: 0.9853580562659847 key: test_accuracy value: [0.75 0.8125 0.73333333 0.8 0.73333333 0.73333333 0.93333333 0.93333333 0.93333333 0.86666667] mean value: 0.8229166666666666 key: train_accuracy value: [0.96323529 0.96323529 0.95620438 0.94160584 0.96350365 0.96350365 0.96350365 0.95620438 0.96350365 0.96350365] mean value: 0.9598003434950624 key: test_roc_auc value: [0.75 0.8125 0.74107143 0.80357143 0.74107143 0.71428571 0.92857143 0.9375 0.9375 0.86607143] mean value: 0.8232142857142858 key: train_roc_auc value: [0.96323529 0.96323529 0.95598892 0.94128303 0.96323529 0.96334186 0.96366155 0.95630861 0.96366155 0.96366155] mean value: 0.9597612958226769 key: test_jcc value: [0.6 0.66666667 0.6 0.66666667 0.6 0.42857143 0.88888889 0.875 0.875 0.77777778] mean value: 0.6978571428571428 key: train_jcc value: [0.93055556 0.93055556 0.91891892 0.89473684 0.93243243 0.93150685 0.93055556 0.91666667 0.93055556 0.93055556] mean value: 0.9247039487216128 key: TN value: 63 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( mean value: 63.0 key: FP value: 14 mean value: 14.0 key: FN value: 13 mean value: 13.0 key: TP value: 62 mean value: 62.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.73 Accuracy on Blind test: 0.87 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.04862261 0.04576516 0.04745841 0.04654217 0.04343677 0.04258347 0.04332376 0.04684305 0.04593372 0.04790926] mean value: 0.04584183692932129 key: score_time value: [0.00996399 0.01001549 0.01016593 0.01011443 0.01044893 0.01053905 0.00999618 0.01000643 0.01014352 0.01008415] mean value: 0.010147809982299805 key: test_mcc value: [0.62994079 0.8819171 0.60714286 0.87287156 0.6000992 1. 0.87287156 0.875 1. 0.87287156] mean value: 0.8212714630160655 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.94117647 0.8 0.92307692 0.76923077 1. 0.94117647 0.93333333 1. 0.94117647] mean value: 0.9049170437405731 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.88888889 0.75 1. 0.83333333 1. 0.88888889 1. 1. 0.88888889] mean value: 0.9107142857142858 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 1. 0.85714286 0.85714286 0.71428571 1. 1. 0.875 1. 1. ] mean value: 0.9053571428571429 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.9375 0.8 0.93333333 0.8 1. 0.93333333 0.93333333 1. 0.93333333] mean value: 0.9083333333333334 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.9375 0.80357143 0.92857143 0.79464286 1. 0.92857143 0.9375 1. 0.92857143] mean value: 0.9071428571428571 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.88888889 0.66666667 0.85714286 0.625 1. 0.88888889 0.875 1. 0.88888889] mean value: 0.8357142857142857 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 69 mean value: 69.0 key: FP value: 7 mean value: 7.0 key: FN value: 7 mean value: 7.0 key: TP value: 69 mean value: 69.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.83 Accuracy on Blind test: 0.92 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.0240798 0.03598094 0.02015948 0.02077174 0.02113819 0.02149487 0.04542184 0.04655457 0.04637337 0.05047441] mean value: 0.0332449197769165 key: score_time value: [0.02137375 0.01155782 0.01181102 0.01181436 0.01163363 0.01168227 0.02253079 0.0221858 0.02151966 0.02061629] mean value: 0.016672539710998534 key: test_mcc value: [ 0.40451992 0.16012815 0.6000992 0.6000992 0.19642857 0.64465837 0.37796447 0.875 0.60714286 -0.18898224] mean value: 0.4277058503877337 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 0.98550725 1. 1. ] mean value: 0.9985507246376812 key: test_fscore value: [0.61538462 0.36363636 0.76923077 0.76923077 0.57142857 0.72727273 0.61538462 0.93333333 0.8 0.30769231] mean value: 0.6472594072594072 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 0.99270073 1. 1. ] mean value: 0.9992700729927007 key: test_precision value: [0.8 0.66666667 0.83333333 0.83333333 0.57142857 1. 0.8 1. 0.85714286 0.4 ] mean value: 0.7761904761904762 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 0.98550725 1. 1. ] mean value: 0.9985507246376812 key: test_recall value: [0.5 0.25 0.71428571 0.71428571 0.57142857 0.57142857 0.5 0.875 0.75 0.25 ] mean value: 0.5696428571428571 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6875 0.5625 0.8 0.8 0.6 0.8 0.66666667 0.93333333 0.8 0.4 ] mean value: 0.705 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 0.99270073 1. 1. ] mean value: 0.9992700729927007 key: test_roc_auc value: [0.6875 0.5625 0.79464286 0.79464286 0.59821429 0.78571429 0.67857143 0.9375 0.80357143 0.41071429] mean value: 0.7053571428571429 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 0.99275362 1. 1. ] mean value: 0.9992753623188406 key: test_jcc value: [0.44444444 0.22222222 0.625 0.625 0.4 0.57142857 0.44444444 0.875 0.66666667 0.18181818] mean value: 0.5056024531024531 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 0.98550725 1. 1. ] mean value: 0.9985507246376812 key: TN value: 64 mean value: 64.0 key: FP value: 33 mean value: 33.0 key: FN value: 12 mean value: 12.0 key: TP value: 43 mean value: 43.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.42 Accuracy on Blind test: 0.71 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01895881 0.00925493 0.00860476 0.00828052 0.00832629 0.00839877 0.00822759 0.00912642 0.0082283 0.00829959] mean value: 0.009570598602294922 key: score_time value: [0.01123643 0.00862384 0.00847077 0.00826454 0.00827861 0.00830722 0.00817847 0.00884295 0.00857377 0.00845981] mean value: 0.008723640441894531 key: test_mcc value: [ 0.12598816 0.5 0.07142857 0.26189246 -0.18898224 0.20044593 0.46428571 0.66143783 0.76376262 0.19642857] mean value: 0.3056687616642674 key: train_mcc value: [0.5002164 0.51520752 0.51832907 0.51856637 0.50427283 0.35764563 0.44729758 0.48933032 0.50362319 0.50362319] mean value: 0.48581120859299487 key: test_fscore value: [0.53333333 0.75 0.53333333 0.66666667 0.47058824 0.4 0.75 0.76923077 0.85714286 0.625 ] mean value: 0.6355295195001076 key: train_fscore value: [0.74626866 0.76258993 0.75912409 0.76595745 0.76056338 0.68571429 0.73239437 0.73684211 0.75 0.75 ] mean value: 0.7449454256630041 key: test_precision value: [0.57142857 0.75 0.5 0.54545455 0.4 0.66666667 0.75 1. 1. 0.625 ] mean value: 0.6808549783549783 key: train_precision value: [0.75757576 0.74647887 0.76470588 0.75 0.73972603 0.67605634 0.7027027 0.75384615 0.75 0.75 ] mean value: 0.7391091735142421 key: test_recall value: [0.5 0.75 0.57142857 0.85714286 0.57142857 0.28571429 0.75 0.625 0.75 0.625 ] mean value: 0.6285714285714286 key: train_recall value: [0.73529412 0.77941176 0.75362319 0.7826087 0.7826087 0.69565217 0.76470588 0.72058824 0.75 0.75 ] mean value: 0.7514492753623189 key: test_accuracy value: [0.5625 0.75 0.53333333 0.6 0.4 0.6 0.73333333 0.8 0.86666667 0.6 ] mean value: 0.6445833333333333 key: train_accuracy value: [0.75 0.75735294 0.75912409 0.75912409 0.75182482 0.67883212 0.72262774 0.74452555 0.75182482 0.75182482] mean value: 0.7427060970373549 key: test_roc_auc value: [0.5625 0.75 0.53571429 0.61607143 0.41071429 0.58035714 0.73214286 0.8125 0.875 0.59821429] mean value: 0.6473214285714286 key: train_roc_auc value: [0.75 0.75735294 0.75916454 0.75895141 0.75159847 0.67870844 0.72293265 0.74435209 0.75181159 0.75181159] mean value: 0.7426683716965046 key: test_jcc value: [0.36363636 0.6 0.36363636 0.5 0.30769231 0.25 0.6 0.625 0.75 0.45454545] mean value: 0.48145104895104895 key: train_jcc value: [0.5952381 0.61627907 0.61176471 0.62068966 0.61363636 0.52173913 0.57777778 0.58333333 0.6 0.6 ] mean value: 0.594045813124256 key: TN value: 50 mean value: 50.0 key: FP value: 28 mean value: 28.0 key: FN value: 26 mean value: 26.0 key: TP value: 48 mean value: 48.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.36 Accuracy on Blind test: 0.68 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01021361 0.01337481 0.01588941 0.01346612 0.01493478 0.01573634 0.01412511 0.01390338 0.01494479 0.01496625] mean value: 0.0141554594039917 key: score_time value: [0.00817657 0.01119995 0.01120806 0.01116014 0.01124048 0.01114297 0.01117563 0.01107979 0.01117134 0.0111742 ] mean value: 0.010872912406921387 key: test_mcc value: [0.5 0.48038446 0.66143783 0.73214286 0.19642857 0.64465837 0.87287156 0.46770717 0.87287156 0.64465837] mean value: 0.6073160755428127 key: train_mcc value: [0.84942274 0.54335816 0.94199209 0.88320546 0.94201665 0.94199209 0.91240409 0.57874991 0.90259957 0.70581053] mean value: 0.8201551284685795 key: test_fscore value: [0.75 0.54545455 0.82352941 0.85714286 0.57142857 0.72727273 0.94117647 0.54545455 0.94117647 0.84210526] mean value: 0.7544740862852317 key: train_fscore value: [0.92517007 0.62626263 0.97142857 0.94202899 0.97058824 0.97142857 0.95588235 0.66666667 0.95104895 0.85534591] mean value: 0.8835850940554824 key: test_precision value: [0.75 1. 0.7 0.85714286 0.57142857 1. 0.88888889 1. 0.88888889 0.72727273] mean value: 0.8383621933621933 key: train_precision value: [0.86075949 1. 0.95774648 0.94202899 0.98507463 0.95774648 0.95588235 1. 0.90666667 0.74725275] mean value: 0.9313157830650873 key: test_recall value: [0.75 0.375 1. 0.85714286 0.57142857 0.57142857 1. 0.375 1. 1. ] mean value: 0.75 key: train_recall value: [1. 0.45588235 0.98550725 0.94202899 0.95652174 0.98550725 0.95588235 0.5 1. 1. ] mean value: 0.8781329923273657 key: test_accuracy value: [0.75 0.6875 0.8 0.86666667 0.6 0.8 0.93333333 0.66666667 0.93333333 0.8 ] mean value: 0.7837500000000001 key: train_accuracy value: [0.91911765 0.72794118 0.97080292 0.94160584 0.97080292 0.97080292 0.95620438 0.75182482 0.94890511 0.83211679] mean value: 0.8990124516960067 key: test_roc_auc value: [0.75 0.6875 0.8125 0.86607143 0.59821429 0.78571429 0.92857143 0.6875 0.92857143 0.78571429] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) 0.7830357142857143 key: train_roc_auc value: [0.91911765 0.72794118 0.9706948 0.94160273 0.97090793 0.9706948 0.95620205 0.75 0.94927536 0.83333333] mean value: 0.8989769820971867 key: test_jcc value: [0.6 0.375 0.7 0.75 0.4 0.57142857 0.88888889 0.375 0.88888889 0.72727273] mean value: 0.6276479076479077 key: train_jcc value: [0.86075949 0.45588235 0.94444444 0.89041096 0.94285714 0.94444444 0.91549296 0.5 0.90666667 0.74725275] mean value: 0.8108211208928097 key: TN value: 62 mean value: 62.0 key: FP value: 19 mean value: 19.0 key: FN value: 14 mean value: 14.0 key: TP value: 57 mean value: 57.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.7 Accuracy on Blind test: 0.85 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01295042 0.0128715 0.01299977 0.0135572 0.01265907 0.01324272 0.01365757 0.01310682 0.01281857 0.01315165] mean value: 0.013101530075073243 key: score_time value: [0.01125312 0.01119184 0.01119018 0.01110387 0.01123881 0.011729 0.01153088 0.01119852 0.01162338 0.01146555] mean value: 0.01135251522064209 key: test_mcc value: [0.37796447 0.40451992 0.25 0.875 0.25 0. 0.87287156 0.46770717 0.25 0.6000992 ] mean value: 0.43481623229268634 key: train_mcc value: [0.32510161 0.49770114 0.40420891 0.86939892 0.40420891 0.68283343 0.86339318 0.52198402 0.45246145 0.84688958] mean value: 0.5868181154630788 key: test_fscore value: [0.72727273 0.73684211 0.66666667 0.93333333 0.66666667 0. 0.94117647 0.54545455 0.22222222 0.82352941] mean value: 0.626316414923226 key: train_fscore value: [0.71204188 0.76836158 0.73796791 0.93617021 0.73796791 0.77876106 0.93150685 0.59793814 0.50549451 0.92413793] mean value: 0.7630348000501478 key: test_precision value: [0.57142857 0.63636364 0.5 0.875 0.5 0. 0.88888889 1. 1. 0.77777778] mean value: 0.6749458874458875 key: train_precision value: [0.55284553 0.62385321 0.58474576 0.91666667 0.58474576 1. 0.87179487 1. 1. 0.87012987] mean value: 0.8004781673479597 key: test_recall value: [1. 0.875 1. 1. 1. 0. 1. 0.375 0.125 0.875] mean value: 0.725 key: train_recall value: [1. 1. 1. 0.95652174 1. 0.63768116 1. 0.42647059 0.33823529 0.98529412] mean value: 0.8344202898550723 key: test_accuracy value: [0.625 0.6875 0.53333333 0.93333333 0.53333333 0.53333333 0.93333333 0.66666667 0.53333333 0.8 ] mean value: 0.6779166666666667 key: train_accuracy value: [0.59558824 0.69852941 0.64233577 0.93430657 0.64233577 0.81751825 0.9270073 0.71532847 0.67153285 0.91970803] mean value: 0.7564190639759553 key: test_roc_auc value: [0.625 0.6875 0.5625 0.9375 0.5625 0.5 0.92857143 0.6875 0.5625 0.79464286] mean value: 0.6848214285714287 key: train_roc_auc value: [0.59558824 0.69852941 0.63970588 0.93414322 0.63970588 0.81884058 0.92753623 0.71323529 0.66911765 0.92018329] mean value: 0.755658567774936 key: test_jcc value: [0.57142857 0.58333333 0.5 0.875 0.5 0. 0.88888889 0.375 0.125 0.7 ] mean value: 0.5118650793650794 key: train_jcc value: [0.55284553 0.62385321 0.58474576 0.88 0.58474576 0.63768116 0.87179487 0.42647059 0.33823529 0.85897436] mean value: 0.6359346537430649 key: TN value: 48 mean value: 48.0 key: FP value: 21 mean value: 21.0 key: FN value: 28 mean value: 28.0 key: TP value: 55 mean value: 55.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.73 Accuracy on Blind test: 0.87 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.11194253 0.09431767 0.09566665 0.09451294 0.09525967 0.0946734 0.09667301 0.0985446 0.09776855 0.09758306] mean value: 0.09769420623779297 key: score_time value: [0.0144887 0.01460576 0.01471305 0.01483321 0.01478577 0.01461482 0.01462436 0.01519608 0.01608753 0.01459646] mean value: 0.01485457420349121 key: test_mcc value: [0.62994079 0.8819171 0.73214286 1. 0.73214286 1. 0.87287156 0.875 1. 0.87287156] mean value: 0.8596886728210563 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.94117647 0.85714286 1. 0.85714286 1. 0.94117647 0.93333333 1. 0.94117647] mean value: 0.9271148459383755 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.88888889 0.85714286 1. 0.85714286 1. 0.88888889 1. 1. 0.88888889] mean value: 0.9238095238095239 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 1. 0.85714286 1. 0.85714286 1. 1. 0.875 1. 1. ] mean value: 0.9339285714285716 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.9375 0.86666667 1. 0.86666667 1. 0.93333333 0.93333333 1. 0.93333333] mean value: 0.9283333333333333 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.9375 0.86607143 1. 0.86607143 1. 0.92857143 0.9375 1. 0.92857143] mean value: 0.9276785714285716 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.88888889 0.75 1. 0.75 1. 0.88888889 0.875 1. 0.88888889] mean value: 0.8708333333333333 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 70 mean value: 70.0 key: FP value: 5 mean value: 5.0 key: FN value: 6 mean value: 6.0 key: TP value: 71 mean value: 71.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.67 Accuracy on Blind test: 0.84 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.03070569 0.03946638 0.03145647 0.03729653 0.0305562 0.03059769 0.02873802 0.04426146 0.03538823 0.02899194] mean value: 0.033745861053466795 key: score_time value: [0.02230477 0.01676178 0.0195477 0.02490187 0.01605606 0.01720977 0.0185101 0.01946902 0.02183986 0.0229888 ] mean value: 0.019958972930908203 key: test_mcc value: [0.62994079 0.77459667 0.76376262 0.87287156 0.87287156 0.75592895 0.87287156 1. 0.875 0.87287156] mean value: 0.8290715263210501 key: train_mcc value: [0.97100831 1. 0.98550725 1. 1. 1. 1. 1. 0.98550418 1. ] mean value: 0.9942019738120464 key: test_fscore value: [0.8 0.88888889 0.875 0.92307692 0.92307692 0.83333333 0.94117647 1. 0.93333333 0.94117647] mean value: 0.9059062342885873 key: train_fscore value: [0.98507463 1. 0.99270073 1. 1. 1. 1. 1. 0.99259259 1. ] mean value: 0.9970367949385273 key: test_precision value: [0.85714286 0.8 0.77777778 1. 1. 1. 0.88888889 1. 1. 0.88888889] mean value: 0.9212698412698412 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 1. 1. 0.85714286 0.85714286 0.71428571 1. 1. 0.875 1. ] mean value: 0.9053571428571429 key: train_recall value: [0.97058824 1. 0.98550725 1. 1. 1. 1. 1. 0.98529412 1. ] mean value: 0.9941389599317988 key: test_accuracy value: [0.8125 0.875 0.86666667 0.93333333 0.93333333 0.86666667 0.93333333 1. 0.93333333 0.93333333] mean value: 0.9087500000000001 key: train_accuracy value: [0.98529412 1. 0.99270073 1. 1. 1. 1. 1. 0.99270073 1. ] mean value: 0.9970695577501074 key: test_roc_auc value: [0.8125 0.875 0.875 0.92857143 0.92857143 0.85714286 0.92857143 1. 0.9375 0.92857143] mean value: 0.9071428571428571 key: train_roc_auc value: [0.98529412 1. 0.99275362 1. 1. 1. 1. 1. 0.99264706 1. ] mean value: 0.9970694799658993 key: test_jcc value: [0.66666667 0.8 0.77777778 0.85714286 0.85714286 0.71428571 0.88888889 1. 0.875 0.88888889] mean value: 0.8325793650793651 key: train_jcc value: [0.97058824 1. 0.98550725 1. 1. 1. 1. 1. 0.98529412 1. ] mean value: 0.9941389599317988 key: TN value: 69 mean value: 69.0 key: FP value: 7 mean value: 7.0 key: FN value: 7 mean value: 7.0 key: TP value: 69 mean value: 69.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.79 Accuracy on Blind test: 0.9 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.03822041 0.04516506 0.04575491 0.04620242 0.04413915 0.04986811 0.06049204 0.0440228 0.04267597 0.04133439] mean value: 0.04578752517700195 key: score_time value: [0.01998234 0.02082443 0.02234125 0.02065015 0.02187395 0.02372122 0.02151942 0.02155709 0.0215466 0.02156043] mean value: 0.02155768871307373 key: test_mcc value: [ 0.51639778 0.12598816 0.46428571 0.47245559 -0.21821789 0.64465837 0.49099025 0.49099025 0.66143783 0.19642857] mean value: 0.384541462895231 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.71428571 0.58823529 0.71428571 0.66666667 0.30769231 0.72727273 0.71428571 0.71428571 0.76923077 0.625 ] mean value: 0.6541240622122976 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.83333333 0.55555556 0.71428571 0.8 0.33333333 1. 0.83333333 0.83333333 1. 0.625 ] mean value: 0.7528174603174603 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.625 0.625 0.71428571 0.57142857 0.28571429 0.57142857 0.625 0.625 0.625 0.625 ] mean value: 0.5892857142857142 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.75 0.5625 0.73333333 0.73333333 0.4 0.8 0.73333333 0.73333333 0.8 0.6 ] mean value: 0.6845833333333333 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.5625 0.73214286 0.72321429 0.39285714 0.78571429 0.74107143 0.74107143 0.8125 0.59821429] mean value: 0.6839285714285714 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.55555556 0.41666667 0.55555556 0.5 0.18181818 0.57142857 0.55555556 0.55555556 0.625 0.45454545] mean value: 0.49716810966810965 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 59 mean value: 59.0 key: FP value: 31 mean value: 31.0 key: FN value: 17 mean value: 17.0 key: TP value: 45 mean value: 45.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.09 Accuracy on Blind test: 0.56 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.28157949 0.25451016 0.25529194 0.25148749 0.25579357 0.25624275 0.25024486 0.27509332 0.26206994 0.25641561] mean value: 0.2598729133605957 key: score_time value: [0.0089767 0.00908971 0.00900173 0.00886941 0.00891423 0.00886917 0.0089736 0.00901198 0.00910449 0.00886345] mean value: 0.008967447280883788 key: test_mcc value: [0.75 0.77459667 0.76376262 0.87287156 0.6000992 0.87287156 0.87287156 0.76376262 1. 0.87287156] mean value: 0.8143707342818287 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.875 0.88888889 0.875 0.92307692 0.76923077 0.92307692 0.94117647 0.85714286 1. 0.94117647] mean value: 0.8993769302592831 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.875 0.8 0.77777778 1. 0.83333333 1. 0.88888889 1. 1. 0.88888889] mean value: 0.906388888888889 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.875 1. 1. 0.85714286 0.71428571 0.85714286 1. 0.75 1. 1. ] mean value: 0.9053571428571429 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.875 0.875 0.86666667 0.93333333 0.8 0.93333333 0.93333333 0.86666667 1. 0.93333333] mean value: 0.9016666666666666 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.875 0.875 0.875 0.92857143 0.79464286 0.92857143 0.92857143 0.875 1. 0.92857143] mean value: 0.9008928571428572 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.77777778 0.8 0.77777778 0.85714286 0.625 0.85714286 0.88888889 0.75 1. 0.88888889] mean value: 0.8222619047619049 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 68 mean value: 68.0 key: FP value: 7 mean value: 7.0 key: FN value: 8 mean value: 8.0 key: TP value: 69 mean value: 69.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.79 Accuracy on Blind test: 0.9 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.0164001 0.01641011 0.01619625 0.016886 0.01677108 0.01692677 0.01703286 0.01680374 0.01673031 0.01696873] mean value: 0.016712594032287597 key: score_time value: [0.01169324 0.01153493 0.01181769 0.01264262 0.01444697 0.01417255 0.01461315 0.01426792 0.01169634 0.01290679] mean value: 0.012979221343994141 key: test_mcc value: [0.57735027 0.57735027 0.46770717 0.875 0.56407607 1. 0.64465837 0.87287156 0.75592895 0.64465837] mean value: 0.6979601035946793 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.8 0.73684211 0.93333333 0.77777778 1. 0.84210526 0.94117647 0.88888889 0.84210526] mean value: 0.8562229102167184 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.66666667 0.58333333 0.875 0.63636364 1. 0.72727273 0.88888889 0.8 0.72727273] mean value: 0.7571464646464647 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.75 0.75 0.66666667 0.93333333 0.73333333 1. 0.8 0.93333333 0.86666667 0.8 ] mean value: 0.8233333333333335 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.75 0.6875 0.9375 0.75 1. 0.78571429 0.92857143 0.85714286 0.78571429] mean value: 0.8232142857142858 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.66666667 0.58333333 0.875 0.63636364 1. 0.72727273 0.88888889 0.8 0.72727273] mean value: 0.7571464646464647 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 49 mean value: 49.0 key: FP value: 0 mean value: 0.0 key: FN value: 27 mean value: 27.0 key: TP value: 76 mean value: 76.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.61 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.0328958 0.03519583 0.06664515 0.04126024 0.04941845 0.05472493 0.04746461 0.03544927 0.03318977 0.06595039] mean value: 0.04621944427490234 key: score_time value: [0.01581264 0.02167654 0.01969385 0.03978634 0.02318883 0.02819037 0.02565694 0.02331448 0.02085543 0.01781631] mean value: 0.02359917163848877 /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:282: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:283: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_CV['Resampling'] = rs_ros /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:288: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:289: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_BT['Resampling'] = rs_ros key: test_mcc value: [0.51639778 0.25819889 0.60714286 0.73214286 0.6000992 0.64465837 1. 0.875 0.875 0.73214286] mean value: 0.6840782810039336 key: train_mcc value: [0.94158382 0.94117647 0.94160273 0.92709446 0.97080136 0.95629932 0.94160273 0.95629932 0.94160273 0.95630861] mean value: 0.9474371541021677 key: test_fscore value: [0.71428571 0.57142857 0.8 0.85714286 0.76923077 0.72727273 1. 0.93333333 0.93333333 0.875 ] mean value: 0.8181027306027306 key: train_fscore value: [0.97101449 0.97058824 0.97101449 0.96402878 0.98550725 0.97841727 0.97058824 0.97777778 0.97058824 0.97810219] mean value: 0.9737626948490679 key: test_precision value: [0.83333333 0.66666667 0.75 0.85714286 0.83333333 1. 1. 1. 1. 0.875 ] mean value: 0.881547619047619 key: train_precision value: [0.95714286 0.97058824 0.97101449 0.95714286 0.98550725 0.97142857 0.97058824 0.98507463 0.97058824 0.97101449] mean value: 0.9710089850346367 key: test_recall value: [0.625 0.5 0.85714286 0.85714286 0.71428571 0.57142857 1. 0.875 0.875 0.875 ] mean value: 0.775 key: train_recall value: [0.98529412 0.97058824 0.97101449 0.97101449 0.98550725 0.98550725 0.97058824 0.97058824 0.97058824 0.98529412] mean value: 0.9765984654731458 key: test_accuracy value: [0.75 0.625 0.8 0.86666667 0.8 0.8 1. 0.93333333 0.93333333 0.86666667] mean value: 0.8375 key: train_accuracy value: [0.97058824 0.97058824 0.97080292 0.96350365 0.98540146 0.97810219 0.97080292 0.97810219 0.97080292 0.97810219] mean value: 0.9736796908544442 key: test_roc_auc value: [0.75 0.625 0.80357143 0.86607143 0.79464286 0.78571429 1. 0.9375 0.9375 0.86607143] mean value: 0.8366071428571429 key: train_roc_auc value: [0.97058824 0.97058824 0.97080136 0.96344842 0.98540068 0.97804774 0.97080136 0.97804774 0.97080136 0.97815431] mean value: 0.9736679454390453 key: test_jcc value: [0.55555556 0.4 0.66666667 0.75 0.625 0.57142857 1. 0.875 0.875 0.77777778] mean value: 0.709642857142857 key: train_jcc value: [0.94366197 0.94285714 0.94366197 0.93055556 0.97142857 0.95774648 0.94285714 0.95652174 0.94285714 0.95714286] mean value: 0.9489290574364059 key: TN value: 68 mean value: 68.0 key: FP value: 17 mean value: 17.0 key: FN value: 8 mean value: 8.0 key: TP value: 59 mean value: 59.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.68 Accuracy on Blind test: 0.84 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.19917345 0.19675255 0.19733953 0.19775033 0.19791675 0.19742084 0.19664717 0.21271873 0.26473117 0.19704652] mean value: 0.2057497024536133 key: score_time value: [0.02243996 0.02309203 0.0232439 0.02306867 0.02281857 0.023067 0.01989818 0.02087092 0.02154446 0.02196884] mean value: 0.02220125198364258 key: test_mcc value: [0.77459667 0.25819889 0.6000992 0.87287156 0.6000992 0.75592895 1. 0.875 0.875 0.73214286] mean value: 0.7343937319391883 key: train_mcc value: [1. 0.98540068 0.98550725 0.98550725 0.98550725 0.98550725 0.98550418 0.95629932 0.98550418 0.95630861] mean value: 0.9811045948988539 key: test_fscore value: [0.85714286 0.57142857 0.76923077 0.92307692 0.76923077 0.83333333 1. 0.93333333 0.93333333 0.875 ] mean value: 0.846510989010989 key: train_fscore value: [1. 0.99259259 0.99270073 0.99270073 0.99270073 0.99270073 0.99259259 0.97777778 0.99259259 0.97810219] mean value: 0.9904460665044608 key: test_precision value: [1. 0.66666667 0.83333333 1. 0.83333333 1. 1. 1. 1. 0.875 ] mean value: 0.9208333333333334 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 0.98507463 1. 0.97101449] mean value: 0.9956089119619295 key: test_recall value: [0.75 0.5 0.71428571 0.85714286 0.71428571 0.71428571 1. 0.875 0.875 0.875 ] mean value: 0.7875 key: train_recall value: [1. 0.98529412 0.98550725 0.98550725 0.98550725 0.98550725 0.98529412 0.97058824 0.98529412 0.98529412] mean value: 0.9853793691389597 key: test_accuracy value: [0.875 0.625 0.8 0.93333333 0.8 0.86666667 1. 0.93333333 0.93333333 0.86666667] mean value: 0.8633333333333335 key: train_accuracy value: [1. 0.99264706 0.99270073 0.99270073 0.99270073 0.99270073 0.99270073 0.97810219 0.99270073 0.97810219] mean value: 0.9905055817947618 key: test_roc_auc value: [0.875 0.625 0.79464286 0.92857143 0.79464286 0.85714286 1. 0.9375 0.9375 0.86607143] mean value: 0.8616071428571429 key: train_roc_auc value: [1. 0.99264706 0.99275362 0.99275362 0.99275362 0.99275362 0.99264706 0.97804774 0.99264706 0.97815431] mean value: 0.9905157715260018 key: test_jcc value: [0.75 0.4 0.625 0.85714286 0.625 0.71428571 1. 0.875 0.875 0.77777778] mean value: 0.7499206349206349 key: train_jcc value: [1. 0.98529412 0.98550725 0.98550725 0.98550725 0.98550725 0.98529412 0.95652174 0.98529412 0.95714286] mean value: 0.9811575934721715 key: TN value: 71 mean value: 71.0 key: FP value: 16 mean value: 16.0 key: FN value: 5 mean value: 5.0 key: TP value: 60 mean value: 60.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.65 Accuracy on Blind test: 0.82 PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02283621 0.02274132 0.0230844 0.02192616 0.02395177 0.02656484 0.02280498 0.02494693 0.02449894 0.02404141] mean value: 0.02373969554901123 key: score_time value: [0.01147294 0.0114243 0.01145148 0.01142192 0.01138258 0.01138496 0.0114224 0.01137829 0.01136613 0.01135755] mean value: 0.011406254768371583 key: test_mcc value: [ 0.65465367 0. 0.40824829 0.40824829 0.1 -0.05976143 0.1 0.31622777 0.55 0.55 ] mean value: 0.30276165871858207 key: train_mcc value: [0.79134188 0.81322028 0.85811633 0.81322028 0.90586932 0.88254861 0.81556009 0.79343933 0.8151574 0.83722687] mean value: 0.8325700404796367 key: test_fscore value: [0.83333333 0.44444444 0.66666667 0.72727273 0.5 0.54545455 0.5 0.72727273 0.8 0.8 ] mean value: 0.6544444444444444 key: train_fscore value: [0.88607595 0.9 0.92682927 0.9 0.95348837 0.94252874 0.90243902 0.88607595 0.9 0.91358025] mean value: 0.9111017546055893 key: test_precision value: [0.71428571 0.5 0.75 0.66666667 0.5 0.42857143 0.5 0.66666667 0.8 0.8 ] mean value: 0.6326190476190476 key: train_precision value: [0.94594595 0.94736842 0.95 0.94736842 0.95348837 0.93181818 0.94871795 0.94594595 0.94736842 0.94871795] mean value: 0.9466739606396889 key: test_recall value: [1. 0.4 0.6 0.8 0.5 0.75 0.5 0.8 0.8 0.8 ] mean value: 0.695 key: train_recall value: [0.83333333 0.85714286 0.9047619 0.85714286 0.95348837 0.95348837 0.86046512 0.83333333 0.85714286 0.88095238] mean value: 0.879125138427464 key: test_accuracy value: [0.8 0.5 0.7 0.7 0.55555556 0.44444444 0.55555556 0.66666667 0.77777778 0.77777778] mean value: 0.6477777777777778 key: train_accuracy value: [0.89285714 0.9047619 0.92857143 0.9047619 0.95294118 0.94117647 0.90588235 0.89411765 0.90588235 0.91764706] mean value: 0.9148599439775911 key: test_roc_auc value: [0.8 0.5 0.7 0.7 0.55 0.475 0.55 0.65 0.775 0.775] mean value: 0.6475000000000001 key: train_roc_auc value: [0.89285714 0.9047619 0.92857143 0.9047619 0.95293466 0.9410299 0.90642303 0.89341085 0.90531561 0.91722038] mean value: 0.9147286821705425 key: test_jcc value: [0.71428571 0.28571429 0.5 0.57142857 0.33333333 0.375 0.33333333 0.57142857 0.66666667 0.66666667] mean value: 0.5017857142857143 key: train_jcc value: [0.79545455 0.81818182 0.86363636 0.81818182 0.91111111 0.89130435 0.82222222 0.79545455 0.81818182 0.84090909] mean value: 0.837463768115942 key: TN value: 28 mean value: 28.0 key: FP value: 14 mean value: 14.0 key: FN value: 19 mean value: 19.0 key: TP value: 33 mean value: 33.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.49 Accuracy on Blind test: 0.76 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.53838086 0.66656113 0.56348276 0.54039288 0.59699011 0.64962125 0.55005503 0.54683185 0.61618948 0.53688884] mean value: 0.5805394172668457 key: score_time value: [0.01186943 0.01767612 0.01306176 0.01297593 0.01463532 0.0142684 0.01302242 0.01305795 0.01307464 0.0128808 ] mean value: 0.013652276992797852 key: test_mcc value: [ 0.65465367 0.21821789 0.40824829 0.81649658 -0.1 0.55 0.31622777 0.79056942 0.35 0.35 ] mean value: 0.4354413613394491 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.83333333 0.5 0.66666667 0.90909091 0.44444444 0.75 0.57142857 0.90909091 0.66666667 0.66666667] mean value: 0.6917388167388167 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.71428571 0.66666667 0.75 0.83333333 0.4 0.75 0.66666667 0.83333333 0.75 0.75 ] mean value: 0.7114285714285714 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.4 0.6 1. 0.5 0.75 0.5 1. 0.6 0.6 ] mean value: 0.695 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.6 0.7 0.9 0.44444444 0.77777778 0.66666667 0.88888889 0.66666667 0.66666667] mean value: 0.7111111111111111 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8 0.6 0.7 0.9 0.45 0.775 0.65 0.875 0.675 0.675] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) mean value: 0.71 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.71428571 0.33333333 0.5 0.83333333 0.28571429 0.6 0.4 0.83333333 0.5 0.5 ] mean value: 0.55 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 34 mean value: 34.0 key: FP value: 14 mean value: 14.0 key: FN value: 13 mean value: 13.0 key: TP value: 33 mean value: 33.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.57 Accuracy on Blind test: 0.79 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01164794 0.01129961 0.009058 0.00929093 0.00825739 0.00817442 0.00800943 0.00845218 0.00890684 0.00797081] mean value: 0.009106755256652832 key: score_time value: [0.01142001 0.01111174 0.00943184 0.008775 0.00926685 0.00886226 0.00826621 0.00894976 0.00857258 0.00840521] mean value: 0.00930614471435547 key: test_mcc value: [ 0.21821789 0.65465367 0.2 0.21821789 -0.1 -0.05976143 0.31622777 -0.1 0.05976143 0. ] mean value: 0.14073172171968001 key: train_mcc value: [0.47574393 0.49471786 0.69542165 0.52620136 0.53873283 0.6199864 0.52960905 0.48301723 0.51376105 0.50286789] mean value: 0.5380059259650506 key: test_fscore value: [0.66666667 0.83333333 0.6 0.66666667 0.44444444 0.54545455 0.66666667 0.44444444 0.66666667 0.71428571] mean value: 0.6248629148629149 key: train_fscore value: [0.7628866 0.77227723 0.83544304 0.75 0.79207921 0.82474227 0.78787879 0.76470588 0.76923077 0.77227723] mean value: 0.7831521006782899 key: test_precision value: [0.57142857 0.71428571 0.6 0.57142857 0.4 0.42857143 0.5 0.5 0.57142857 0.55555556] mean value: 0.5412698412698412 key: train_precision value: [0.67272727 0.66101695 0.89189189 0.78947368 0.68965517 0.74074074 0.69642857 0.65 0.71428571 0.66101695] mean value: 0.7167236946003597 key: test_recall value: [0.8 1. 0.6 0.8 0.5 0.75 1. 0.4 0.8 1. ] mean value: 0.7649999999999999 key: train_recall value: [0.88095238 0.92857143 0.78571429 0.71428571 0.93023256 0.93023256 0.90697674 0.92857143 0.83333333 0.92857143] mean value: 0.8767441860465116 key: test_accuracy value: [0.6 0.8 0.6 0.6 0.44444444 0.44444444 0.55555556 0.44444444 0.55555556 0.55555556] mean value: 0.5599999999999999 key: train_accuracy value: [0.72619048 0.72619048 0.8452381 0.76190476 0.75294118 0.8 0.75294118 0.71764706 0.75294118 0.72941176] mean value: 0.7565406162464986 key: test_roc_auc value: [0.6 0.8 0.6 0.6 0.45 0.475 0.6 0.45 0.525 0.5 ] mean value: 0.56 key: train_roc_auc value: [0.72619048 0.72619048 0.8452381 0.76190476 0.75083056 0.79844961 0.75110742 0.72009967 0.75387597 0.73172757] mean value: 0.7565614617940198 key: test_jcc value: [0.5 0.71428571 0.42857143 0.5 0.28571429 0.375 0.5 0.28571429 0.5 0.55555556] mean value: 0.464484126984127 key: train_jcc value: [0.61666667 0.62903226 0.7173913 0.6 0.6557377 0.70175439 0.65 0.61904762 0.625 0.62903226] mean value: 0.6443662197074088 key: TN value: 17 mean value: 17.0 key: FP value: 11 mean value: 11.0 key: FN value: 30 mean value: 30.0 key: TP value: 36 mean value: 36.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.32 Accuracy on Blind test: 0.69 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00824237 0.00883603 0.00824642 0.00817323 0.00879383 0.00821328 0.00828743 0.00824833 0.00819349 0.00816178] mean value: 0.008339619636535645 key: score_time value: [0.00865197 0.00939441 0.00830483 0.00840235 0.00835133 0.00827479 0.00831819 0.00830507 0.00837803 0.00836182] mean value: 0.008474278450012206 key: test_mcc value: [ 0. -0.21821789 0.21821789 0. -0.47809144 -0.55 -0.35 -0.47809144 -0.5976143 -0.31622777] mean value: -0.277002495815155 key: train_mcc value: [0.48507125 0.31391444 0.36220897 0.4843221 0.56230818 0.46270205 0.52510205 0.49481869 0.4465631 0.52960905] mean value: 0.46666198870701453 key: test_fscore value: [0. 0.25 0.66666667 0.44444444 0. 0.22222222 0.25 0.5 0. 0.25 ] mean value: 0.25833333333333336 key: train_fscore value: [0.71052632 0.62337662 0.64935065 0.66666667 0.72222222 0.71604938 0.72 0.7027027 0.62686567 0.70422535] mean value: 0.6841985586578854 key: test_precision value: [0. 0.33333333 0.57142857 0.5 0. 0.2 0.25 0.42857143 0. 0.33333333] mean value: 0.26166666666666666 key: train_precision value: [0.79411765 0.68571429 0.71428571 0.85185185 0.89655172 0.76315789 0.84375 0.8125 0.84 0.86206897] mean value: 0.806399808330269 key: test_recall value: [0. 0.2 0.8 0.4 0. 0.25 0.25 0.6 0. 0.2 ] mean value: 0.27 key: train_recall value: [0.64285714 0.57142857 0.5952381 0.54761905 0.60465116 0.6744186 0.62790698 0.61904762 0.5 0.5952381 ] mean value: 0.5978405315614618 key: test_accuracy value: [0.5 0.4 0.6 0.5 0.33333333 0.22222222 0.33333333 0.33333333 0.22222222 0.33333333] mean value: 0.3777777777777778 key: train_accuracy value: [0.73809524 0.6547619 0.67857143 0.72619048 0.76470588 0.72941176 0.75294118 0.74117647 0.70588235 0.75294118] mean value: 0.7244677871148459 key: test_roc_auc value: [0.5 0.4 0.6 0.5 0.3 0.225 0.325 0.3 0.25 0.35 ] mean value: 0.375 key: train_roc_auc value: [0.73809524 0.6547619 0.67857143 0.72619048 0.7666113 0.73006645 0.75442968 0.73975637 0.70348837 0.75110742] mean value: 0.7243078626799556 key: test_jcc value: [0. 0.14285714 0.5 0.28571429 0. 0.125 0.14285714 0.33333333 0. 0.14285714] mean value: 0.16726190476190475 key: train_jcc value: [0.55102041 0.45283019 0.48076923 0.5 0.56521739 0.55769231 0.5625 0.54166667 0.45652174 0.54347826] mean value: 0.5211696193275064 key: TN value: 23 mean value: 23.0 key: FP value: 34 mean value: 34.0 key: FN value: 24 mean value: 24.0 key: TP value: 13 mean value: 13.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.37 Accuracy on Blind test: 0.68 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00804973 0.00821543 0.00890303 0.00834084 0.00842309 0.00840545 0.00832963 0.00788832 0.00874853 0.0088377 ] mean value: 0.008414173126220703 key: score_time value: [0.01010227 0.00930381 0.00980425 0.00989151 0.01001334 0.00915027 0.00909543 0.00902271 0.00962782 0.00973582] mean value: 0.00957472324371338 key: test_mcc value: [ 0. 0. -0.21821789 0. -0.55 -0.1 -0.31622777 0.15811388 0.35 0.47809144] mean value: -0.019824032951065405 key: train_mcc value: [0.38138504 0.35724413 0.48112522 0.33333333 0.53019399 0.38920425 0.41163441 0.46034451 0.36500203 0.34182831] mean value: 0.4051295218916732 key: test_fscore value: [0.44444444 0.28571429 0.5 0.28571429 0.22222222 0.44444444 0.4 0.5 0.66666667 0.57142857] mean value: 0.43206349206349215 key: train_fscore value: [0.69767442 0.6746988 0.71794872 0.66666667 0.76190476 0.71111111 0.71264368 0.73563218 0.66666667 0.6744186 ] mean value: 0.7019365604803427 key: test_precision value: [0.5 0.5 0.42857143 0.5 0.2 0.4 0.33333333 0.66666667 0.75 1. ] mean value: 0.5278571428571428 key: train_precision value: [0.68181818 0.68292683 0.77777778 0.66666667 0.7804878 0.68085106 0.70454545 0.71111111 0.69230769 0.65909091] mean value: 0.7037583491293923 key: test_recall value: [0.4 0.2 0.6 0.2 0.25 0.5 0.5 0.4 0.6 0.4 ] mean value: 0.40499999999999997 key: train_recall value: [0.71428571 0.66666667 0.66666667 0.66666667 0.74418605 0.74418605 0.72093023 0.76190476 0.64285714 0.69047619] mean value: 0.7018826135105206 key: test_accuracy value: [0.5 0.5 0.4 0.5 0.22222222 0.44444444 0.33333333 0.55555556 0.66666667 0.66666667] mean value: 0.47888888888888886 key: train_accuracy value: [0.69047619 0.67857143 0.73809524 0.66666667 0.76470588 0.69411765 0.70588235 0.72941176 0.68235294 0.67058824] mean value: 0.7020868347338937 key: test_roc_auc value: [0.5 0.5 0.4 0.5 0.225 0.45 0.35 0.575 0.675 0.7 ] mean value: 0.4875 key: train_roc_auc value: [0.69047619 0.67857143 0.73809524 0.66666667 0.76495017 0.69352159 0.70570321 0.72978959 0.68189369 0.67081949] mean value: 0.7020487264673312 key: test_jcc value: [0.28571429 0.16666667 0.33333333 0.16666667 0.125 0.28571429 0.25 0.33333333 0.5 0.4 ] mean value: 0.2846428571428571 key: train_jcc value: [0.53571429 0.50909091 0.56 0.5 0.61538462 0.55172414 0.55357143 0.58181818 0.5 0.50877193] mean value: 0.5416075488335017 key: TN value: 26 mean value: 26.0 key: FP value: 28 mean value: 28.0 key: FN value: 21 mean value: 21.0 key: TP value: 19 mean value: 19.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.17 Accuracy on Blind test: 0.58 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.00887966 0.00883842 0.00862217 0.00868559 0.00890541 0.00862718 0.00858402 0.00897694 0.00865579 0.00867534] mean value: 0.008745050430297852 key: score_time value: [0.00847769 0.0083859 0.008461 0.00841331 0.00859261 0.00839782 0.0084157 0.00859141 0.00845885 0.00843167] mean value: 0.00846259593963623 key: test_mcc value: [ 0. 0.40824829 0.40824829 0. 0.1 0.15811388 -0.1 0.1 0.31622777 0.63245553] mean value: 0.2023293761986659 key: train_mcc value: [0.61904762 0.69224438 0.66742381 0.59540689 0.76782927 0.71824836 0.55290867 0.62437683 0.64833694 0.64709151] mean value: 0.6532914275382986 key: test_fscore value: [0.44444444 0.72727273 0.66666667 0.61538462 0.5 0.6 0.44444444 0.6 0.72727273 0.75 ] mean value: 0.6075485625485626 key: train_fscore value: [0.80952381 0.83950617 0.8372093 0.8 0.88888889 0.86363636 0.7816092 0.81395349 0.81481481 0.81927711] mean value: 0.8268419144237091 key: test_precision value: [0.5 0.66666667 0.75 0.5 0.5 0.5 0.4 0.6 0.66666667 1. ] mean value: 0.6083333333333333 key: train_precision value: [0.80952381 0.87179487 0.81818182 0.79069767 0.85106383 0.84444444 0.77272727 0.79545455 0.84615385 0.82926829] mean value: 0.8229310405169373 key: test_recall value: [0.4 0.8 0.6 0.8 0.5 0.75 0.5 0.6 0.8 0.6 ] mean value: 0.635 key: train_recall value: [0.80952381 0.80952381 0.85714286 0.80952381 0.93023256 0.88372093 0.79069767 0.83333333 0.78571429 0.80952381] mean value: 0.8318936877076413 key: test_accuracy value: [0.5 0.7 0.7 0.5 0.55555556 0.55555556 0.44444444 0.55555556 0.66666667 0.77777778] mean value: 0.5955555555555556 key: train_accuracy value: [0.80952381 0.8452381 0.83333333 0.79761905 0.88235294 0.85882353 0.77647059 0.81176471 0.82352941 0.82352941] mean value: 0.8262184873949578 key: test_roc_auc value: [0.5 0.7 0.7 0.5 0.55 0.575 0.45 0.55 0.65 0.8 ] mean value: 0.5975 key: train_roc_auc value: [0.80952381 0.8452381 0.83333333 0.79761905 0.88178295 0.85852713 0.77630122 0.8120155 0.8230897 0.82336656] mean value: 0.8260797342192691 key: test_jcc value: [0.28571429 0.57142857 0.5 0.44444444 0.33333333 0.42857143 0.28571429 0.42857143 0.57142857 0.6 ] mean value: 0.4449206349206348 key: train_jcc value: [0.68 0.72340426 0.72 0.66666667 0.8 0.76 0.64150943 0.68627451 0.6875 0.69387755] mean value: 0.705923241677241 key: TN value: 26 mean value: 26.0 key: FP value: 17 mean value: 17.0 key: FN value: 21 mean value: 21.0 key: TP value: 30 mean value: 30.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.35 Accuracy on Blind test: 0.66 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.43665123 0.46531892 0.39793062 0.38484907 0.37270451 0.38749909 0.40523052 0.50272679 0.37766552 0.39441299] mean value: 0.412498927116394 key: score_time value: [0.01195264 0.01200628 0.01183558 0.01191711 0.01194119 0.01192427 0.01196218 0.01190591 0.01190948 0.01190758] mean value: 0.011926221847534179 key: test_mcc value: [ 0.5 0. 0.21821789 0.40824829 -0.31622777 -0.1 -0.63245553 0.5976143 -0.31622777 0.31622777] mean value: 0.06753971873165382 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.76923077 0.28571429 0.5 0.72727273 0.4 0.44444444 0. 0.83333333 0.25 0.72727273] mean value: 0.49372682872682877 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.625 0.5 0.66666667 0.66666667 0.33333333 0.4 0. 0.71428571 0.33333333 0.66666667] mean value: 0.4905952380952381 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.2 0.4 0.8 0.5 0.5 0. 1. 0.2 0.8] mean value: 0.54 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.7 0.5 0.6 0.7 0.33333333 0.44444444 0.22222222 0.77777778 0.33333333 0.66666667] mean value: 0.5277777777777778 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.7 0.5 0.6 0.7 0.35 0.45 0.2 0.75 0.35 0.65] mean value: 0.525 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.625 0.16666667 0.33333333 0.57142857 0.25 0.28571429 0. 0.71428571 0.14285714 0.57142857] mean value: 0.36607142857142855 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 24 mean value: 24.0 key: FP value: 21 mean value: 21.0 key: FN value: 23 mean value: 23.0 key: TP value: 26 mean value: 26.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.35 Accuracy on Blind test: 0.69 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01370859 0.0131712 0.01013899 0.01002789 0.00981402 0.00985694 0.00986934 0.01002073 0.0107832 0.01089931] mean value: 0.010829019546508788 key: score_time value: [0.01132679 0.01054096 0.00878572 0.00834441 0.00832295 0.00824118 0.00826597 0.00855398 0.00896335 0.00902319] mean value: 0.009036850929260255 key: test_mcc value: [0.81649658 0.81649658 0.5 1. 0.5976143 0.79056942 0.55 1. 0.8 0.55 ] mean value: 0.7421176881564744 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.88888889 0.90909091 0.57142857 1. 0.66666667 0.85714286 0.75 1. 0.88888889 0.8 ] mean value: 0.8332106782106783 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.83333333 1. 1. 1. 1. 0.75 1. 1. 0.8 ] mean value: 0.9383333333333335 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 1. 0.4 1. 0.5 0.75 0.75 1. 0.8 0.8 ] mean value: 0.78 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9 0.9 0.7 1. 0.77777778 0.88888889 0.77777778 1. 0.88888889 0.77777778] mean value: 0.8611111111111113 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9 0.9 0.7 1. 0.75 0.875 0.775 1. 0.9 0.775] mean value: 0.8575000000000002 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.8 0.83333333 0.4 1. 0.5 0.75 0.6 1. 0.8 0.66666667] mean value: 0.735 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 44 mean value: 44.0 key: FP value: 10 mean value: 10.0 key: FN value: 3 mean value: 3.0 key: TP value: 37 mean value: 37.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.67 Accuracy on Blind test: 0.84 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.08355451 0.08239985 0.08116961 0.0823493 0.08216691 0.0857296 0.08329606 0.08283973 0.08216929 0.08263755] mean value: 0.08283123970031739 key: score_time value: [0.01656103 0.01668882 0.01673579 0.01669979 0.01810622 0.01679373 0.01664114 0.01679635 0.01673222 0.01815653] mean value: 0.016991162300109865 key: test_mcc value: [ 0.21821789 0.65465367 0. 0.65465367 -0.1 -0.1 -0.35 0.55 -0.05976143 0.1 ] mean value: 0.1567763801185227 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.83333333 0.44444444 0.83333333 0.44444444 0.44444444 0.25 0.8 0.28571429 0.6 ] mean value: 0.5602380952380952 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.57142857 0.71428571 0.5 0.71428571 0.4 0.4 0.25 0.8 0.5 0.6 ] mean value: 0.5449999999999999 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 1. 0.4 1. 0.5 0.5 0.25 0.8 0.2 0.6 ] mean value: 0.605 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.8 0.5 0.8 0.44444444 0.44444444 0.33333333 0.77777778 0.44444444 0.55555556] mean value: 0.5700000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.6 0.8 0.5 0.8 0.45 0.45 0.325 0.775 0.475 0.55 ] mean value: 0.5725 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.71428571 0.28571429 0.71428571 0.28571429 0.28571429 0.14285714 0.66666667 0.16666667 0.42857143] mean value: 0.41904761904761906 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 25 mean value: 25.0 key: FP value: 18 mean value: 18.0 key: FN value: 22 mean value: 22.0 key: TP value: 29 mean value: 29.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.29 Accuracy on Blind test: 0.65 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.0083909 0.0083034 0.00807333 0.00824189 0.00825071 0.00829339 0.00813794 0.0088129 0.00833988 0.00830221] mean value: 0.00831465721130371 key: score_time value: [0.00840092 0.00839686 0.0083375 0.00847769 0.00835156 0.00837564 0.0086894 0.00898671 0.00903249 0.00888491] mean value: 0.008593368530273437 key: test_mcc value: [ 0.21821789 -0.2 0. -0.40824829 -0.15811388 0.15811388 -0.35 0.15811388 0.15811388 -0.35 ] mean value: -0.07738026342110325 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.4 0.54545455 0.36363636 0.28571429 0.6 0.25 0.5 0.5 0.4 ] mean value: 0.45114718614718613 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.57142857 0.4 0.5 0.33333333 0.33333333 0.5 0.25 0.66666667 0.66666667 0.4 ] mean value: 0.4621428571428572 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.4 0.6 0.4 0.25 0.75 0.25 0.4 0.4 0.4 ] mean value: 0.465 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.4 0.5 0.3 0.44444444 0.55555556 0.33333333 0.55555556 0.55555556 0.33333333] mean value: 0.45777777777777773 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.6 0.4 0.5 0.3 0.425 0.575 0.325 0.575 0.575 0.325] mean value: 0.4600000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.25 0.375 0.22222222 0.16666667 0.42857143 0.14285714 0.33333333 0.33333333 0.25 ] mean value: 0.3001984126984127 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 21 mean value: 21.0 key: FP value: 25 mean value: 25.0 key: FN value: 26 mean value: 26.0 key: TP value: 22 mean value: 22.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.26 Accuracy on Blind test: 0.65 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.06693983 1.04320121 1.05023813 1.05610228 1.05119729 1.10538578 1.0328548 1.04281378 1.02934623 1.02944255] mean value: 1.0507521867752074 key: score_time value: [0.10711551 0.09000683 0.09520698 0.09461856 0.09211016 0.09304357 0.08741426 0.09318328 0.08647776 0.08651567] mean value: 0.09256925582885742 key: test_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [ 0.81649658 0.65465367 0.21821789 0.81649658 0.31622777 -0.1 0.05976143 0.79056942 0.35 0.31622777] mean value: 0.42386511003419114 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.90909091 0.83333333 0.5 0.90909091 0.57142857 0.44444444 0.33333333 0.90909091 0.66666667 0.72727273] mean value: 0.6803751803751804 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.83333333 0.71428571 0.66666667 0.83333333 0.66666667 0.4 0.5 0.83333333 0.75 0.66666667] mean value: 0.6864285714285715 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.4 1. 0.5 0.5 0.25 1. 0.6 0.8 ] mean value: 0.705 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9 0.8 0.6 0.9 0.66666667 0.44444444 0.55555556 0.88888889 0.66666667 0.66666667] mean value: 0.7088888888888889 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9 0.8 0.6 0.9 0.65 0.45 0.525 0.875 0.675 0.65 ] mean value: 0.7025 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.83333333 0.71428571 0.33333333 0.83333333 0.4 0.28571429 0.2 0.83333333 0.5 0.57142857] mean value: 0.5504761904761905 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 33 mean value: 33.0 key: FP value: 13 mean value: 13.0 key: FN value: 14 mean value: 14.0 key: TP value: 34 mean value: 34.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.53 Accuracy on Blind test: 0.77 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.8055284 0.83201861 0.82570887 0.8074472 0.82467222 0.85785246 0.80040121 0.82698226 0.83134413 0.83202553] mean value: 0.8243980884552002 key: score_time value: [0.18204045 0.19004273 0.14946604 0.1972661 0.15155721 0.19060373 0.16753435 0.19431329 0.16234851 0.18571186] mean value: 0.17708842754364013 key: test_mcc value: [ 0.6 0.65465367 0.65465367 0.65465367 0.31622777 0.15811388 -0.15811388 0.79056942 0.35 0.79056942] mean value: 0.4811327608224959 key: train_mcc value: [0.85811633 0.90578946 0.9047619 0.85811633 0.88444936 0.88444936 0.90586932 0.90586932 0.83545719 0.88261351] mean value: 0.8825492092714203 key: test_fscore value: [0.8 0.83333333 0.75 0.83333333 0.57142857 0.6 0.28571429 0.90909091 0.66666667 0.90909091] mean value: 0.7158658008658008 key: train_fscore value: [0.93023256 0.95348837 0.95238095 0.93023256 0.94382022 0.94382022 0.95348837 0.95238095 0.91566265 0.94117647] mean value: 0.9416683335855869 key: test_precision value: [0.8 0.71428571 1. 0.71428571 0.66666667 0.5 0.33333333 0.83333333 0.75 0.83333333] mean value: 0.7145238095238095 key: train_precision value: [0.90909091 0.93181818 0.95238095 0.90909091 0.91304348 0.91304348 0.95348837 0.95238095 0.92682927 0.93023256] mean value: 0.9291399059808885 key: test_recall value: [0.8 1. 0.6 1. 0.5 0.75 0.25 1. 0.6 1. ] mean value: 0.75 key: train_recall value: [0.95238095 0.97619048 0.95238095 0.95238095 0.97674419 0.97674419 0.95348837 0.95238095 0.9047619 0.95238095] mean value: 0.9549833887043189 key: test_accuracy value: [0.8 0.8 0.8 0.8 0.66666667 0.55555556 0.44444444 0.88888889 0.66666667 0.88888889] mean value: 0.7311111111111112 key: train_accuracy value: [0.92857143 0.95238095 0.95238095 0.92857143 0.94117647 0.94117647 0.95294118 0.95294118 0.91764706 0.94117647] mean value: 0.9408963585434174 key: test_roc_auc value: [0.8 0.8 0.8 0.8 0.65 0.575 0.425 0.875 0.675 0.875] mean value: 0.7275 key: train_roc_auc value: [0.92857143 0.95238095 0.95238095 0.92857143 0.94075305 0.94075305 0.95293466 0.95293466 0.91749723 0.94130676] mean value: 0.9408084163898115 key: test_jcc value: [0.66666667 0.71428571 0.6 0.71428571 0.4 0.42857143 0.16666667 0.83333333 0.5 0.83333333] mean value: 0.5857142857142856 key: train_jcc value: [0.86956522 0.91111111 0.90909091 0.86956522 0.89361702 0.89361702 0.91111111 0.90909091 0.84444444 0.88888889] mean value: 0.8900101851073174 key: TN value: 33 mean value: 33.0 key: FP value: 11 mean value: 11.0 key: FN value: 14 mean value: 14.0 key: TP value: 36 mean value: 36.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.53 Accuracy on Blind test: 0.77 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.04922676 0.03869319 0.03898191 0.0404942 0.03656006 0.03749299 0.03978753 0.0382123 0.04020357 0.03854203] mean value: 0.03981945514678955 key: score_time value: [0.01003122 0.01038003 0.01095271 0.01000929 0.01008701 0.01012039 0.01010728 0.01007485 0.01013327 0.01011372] mean value: 0.010200977325439453 key: test_mcc value: [1. 0.65465367 0.40824829 1. 0.55 0.55 0.31622777 0.79056942 0.79056942 0.79056942] mean value: 0.6850837972314963 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.83333333 0.66666667 1. 0.75 0.75 0.57142857 0.90909091 0.90909091 0.90909091] mean value: 0.8298701298701298 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.71428571 0.75 1. 0.75 0.75 0.66666667 0.83333333 0.83333333 0.83333333] mean value: 0.8130952380952381 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.6 1. 0.75 0.75 0.5 1. 1. 1. ] mean value: 0.86 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.8 0.7 1. 0.77777778 0.77777778 0.66666667 0.88888889 0.88888889 0.88888889] mean value: 0.8388888888888889 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.8 0.7 1. 0.775 0.775 0.65 0.875 0.875 0.875] mean value: 0.8324999999999999 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.71428571 0.5 1. 0.6 0.6 0.4 0.83333333 0.83333333 0.83333333] mean value: 0.7314285714285714 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 38 mean value: 38.0 key: FP value: 6 mean value: 6.0 key: FN value: 9 mean value: 9.0 key: TP value: 41 mean value: 41.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.76 Accuracy on Blind test: 0.89 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.01391625 0.01757169 0.03148174 0.04039502 0.04079723 0.04090428 0.0404675 0.04072762 0.04066205 0.04058099] mean value: 0.03475043773651123 key: score_time value: [0.01157188 0.01159167 0.02229023 0.02026033 0.02213502 0.02002287 0.02123189 0.02278447 0.01990438 0.02181673] mean value: 0.019360947608947753 key: test_mcc value: [ 0.81649658 0.81649658 0.5 1. -0.1 -0.15811388 0.1 -0.1 0.79056942 0.55 ] mean value: 0.4215448693889128 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.90909091 0.88888889 0.57142857 1. 0.44444444 0.28571429 0.5 0.44444444 0.90909091 0.8 ] mean value: 0.6753102453102453 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.83333333 1. 1. 1. 0.4 0.33333333 0.5 0.5 0.83333333 0.8 ] mean value: 0.72 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.8 0.4 1. 0.5 0.25 0.5 0.4 1. 0.8 ] mean value: 0.6649999999999999 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9 0.9 0.7 1. 0.44444444 0.44444444 0.55555556 0.44444444 0.88888889 0.77777778] mean value: 0.7055555555555556 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9 0.9 0.7 1. 0.45 0.425 0.55 0.45 0.875 0.775] mean value: 0.7025 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.83333333 0.8 0.4 1. 0.28571429 0.16666667 0.33333333 0.28571429 0.83333333 0.66666667] mean value: 0.5604761904761905 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 35 mean value: 35.0 key: FP value: 15 mean value: 15.0 key: FN value: 12 mean value: 12.0 key: TP value: 32 mean value: 32.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.25 Accuracy on Blind test: 0.6 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.02287936 0.00857711 0.00837636 0.00851226 0.00820565 0.00880599 0.0081892 0.00892711 0.00876164 0.00872445] mean value: 0.0099959135055542 key: score_time value: [0.01275063 0.00863481 0.00858188 0.00834084 0.0082655 0.00896335 0.00858784 0.00890231 0.00822568 0.00817227] mean value: 0.008942508697509765 key: test_mcc value: [ 0.2 -0.40824829 0.21821789 -0.21821789 0.05976143 -0.1 0.35 0.31622777 0.31622777 0.31622777] mean value: 0.10501964380533704 key: train_mcc value: [0.40766097 0.54777433 0.42857143 0.45250923 0.50609081 0.55315615 0.43521595 0.41222451 0.43521595 0.41374985] mean value: 0.45921691782565255 key: test_fscore value: [0.6 0.22222222 0.5 0.5 0.33333333 0.44444444 0.66666667 0.72727273 0.72727273 0.72727273] mean value: 0.5448484848484848 key: train_fscore value: [0.71910112 0.77108434 0.71428571 0.72941176 0.75294118 0.77647059 0.72093023 0.69135802 0.71428571 0.6835443 ] mean value: 0.7273412979975062 key: test_precision value: [0.6 0.25 0.66666667 0.42857143 0.5 0.4 0.6 0.66666667 0.66666667 0.66666667] mean value: 0.5445238095238095 key: train_precision value: [0.68085106 0.7804878 0.71428571 0.72093023 0.76190476 0.78571429 0.72093023 0.71794872 0.71428571 0.72972973] mean value: 0.7327068257693039 key: test_recall value: [0.6 0.2 0.4 0.6 0.25 0.5 0.75 0.8 0.8 0.8 ] mean value: 0.57 key: train_recall value: [0.76190476 0.76190476 0.71428571 0.73809524 0.74418605 0.76744186 0.72093023 0.66666667 0.71428571 0.64285714] mean value: 0.7232558139534884 key: test_accuracy value: [0.6 0.3 0.6 0.4 0.55555556 0.44444444 0.66666667 0.66666667 0.66666667 0.66666667] mean value: 0.5566666666666666 key: train_accuracy value: [0.70238095 0.77380952 0.71428571 0.72619048 0.75294118 0.77647059 0.71764706 0.70588235 0.71764706 0.70588235] mean value: 0.7293137254901961 key: test_roc_auc value: [0.6 0.3 0.6 0.4 0.525 0.45 0.675 0.65 0.65 0.65 ] mean value: 0.55 key: train_roc_auc value: [0.70238095 0.77380952 0.71428571 0.72619048 0.7530454 0.77657807 0.71760797 0.70542636 0.71760797 0.7051495 ] mean value: 0.7292081949058694 key: test_jcc value: [0.42857143 0.125 0.33333333 0.33333333 0.2 0.28571429 0.5 0.57142857 0.57142857 0.57142857] mean value: 0.3920238095238095 key: train_jcc value: [0.56140351 0.62745098 0.55555556 0.57407407 0.60377358 0.63461538 0.56363636 0.52830189 0.55555556 0.51923077] mean value: 0.5723597663529902 key: TN value: 25 mean value: 25.0 key: FP value: 20 mean value: 20.0 key: FN value: 22 mean value: 22.0 key: TP value: 27 mean value: 27.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.52 Accuracy on Blind test: 0.76 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.00962329 0.01323986 0.01237082 0.01236582 0.01327753 0.01318359 0.01319122 0.01325846 0.01270938 0.01257205] mean value: 0.012579202651977539 key: score_time value: [0.008636 0.01129222 0.01114297 0.01113963 0.01109719 0.01116228 0.01118302 0.01116157 0.01114726 0.01120234] mean value: 0.010916447639465332 key: test_mcc value: [ 1. 0.6 0.5 0.6 -0.15811388 -0.05976143 -0.05976143 0.39528471 0.39528471 0.1 ] mean value: 0.33129326711002366 key: train_mcc value: [0.74535599 0.65094455 0.66953406 0.63245553 0.82687419 0.48724235 0.69036294 0.92967885 0.72991065 0.95396693] mean value: 0.7316326053036888 key: test_fscore value: [1. 0.8 0.57142857 0.8 0.28571429 0.54545455 0.54545455 0.76923077 0.76923077 0.6 ] mean value: 0.6686513486513486 key: train_fscore value: [0.83333333 0.83168317 0.76470588 0.72727273 0.8974359 0.76785714 0.85148515 0.96470588 0.86597938 0.97560976] mean value: 0.8480068319977526 key: test_precision value: [1. 0.8 1. 0.8 0.33333333 0.42857143 0.42857143 0.625 0.625 0.6 ] mean value: 0.664047619047619 key: train_precision value: [1. 0.71186441 1. 1. 1. 0.62318841 0.74137931 0.95348837 0.76363636 1. ] mean value: 0.8793556858650977 key: test_recall value: [1. 0.8 0.4 0.8 0.25 0.75 0.75 1. 1. 0.6 ] mean value: 0.735 key: train_recall value: [0.71428571 1. 0.61904762 0.57142857 0.81395349 1. 1. 0.97619048 1. 0.95238095] mean value: 0.8647286821705427 key: test_accuracy value: [1. 0.8 0.7 0.8 0.44444444 0.44444444 0.44444444 0.66666667 0.66666667 0.55555556] mean value: 0.6522222222222223 key: train_accuracy value: [0.85714286 0.79761905 0.80952381 0.78571429 0.90588235 0.69411765 0.82352941 0.96470588 0.84705882 0.97647059] mean value: 0.8461764705882352 key: test_roc_auc value: [1. 0.8 0.7 0.8 0.425 0.475 0.475 0.625 0.625 0.55 ] mean value: 0.6475 key: train_roc_auc value: [0.85714286 0.79761905 0.80952381 0.78571429 0.90697674 0.69047619 0.82142857 0.96483942 0.84883721 0.97619048] mean value: 0.8458748615725359 key: test_jcc value: [1. 0.66666667 0.4 0.66666667 0.16666667 0.375 0.375 0.625 0.625 0.42857143] mean value: 0.5328571428571429 key: train_jcc value: [0.71428571 0.71186441 0.61904762 0.57142857 0.81395349 0.62318841 0.74137931 0.93181818 0.76363636 0.95238095] mean value: 0.7442983013891087 key: TN value: 27 mean value: 27.0 key: FP value: 12 mean value: 12.0 key: FN value: 20 mean value: 20.0 key: TP value: 35 mean value: 35.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.61 Accuracy on Blind test: 0.81 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01054049 0.01207495 0.01262665 0.01228166 0.01229572 0.01198316 0.0122354 0.012609 0.012393 0.01217747] mean value: 0.012121748924255372 key: score_time value: [0.01128149 0.01144648 0.01137066 0.01132822 0.01126194 0.01129317 0.01131725 0.01129222 0.01129723 0.01152325] mean value: 0.011341190338134766 key: test_mcc value: [ 0.33333333 0.65465367 0.33333333 0.6 0.31622777 -0.05976143 -0.31622777 0.79056942 0.79056942 0.63245553] mean value: 0.40751532690257897 key: train_mcc value: [0.34641016 0.6882472 0.57735027 0.82462113 0.92967885 0.52445525 0.74667082 0.92964003 0.85983393 0.80605538] mean value: 0.7232963006785581 key: test_fscore value: [0.71428571 0.83333333 0.33333333 0.8 0.57142857 0.54545455 0. 0.90909091 0.90909091 0.75 ] mean value: 0.6366017316017316 key: train_fscore value: [0.71794872 0.84848485 0.66666667 0.89473684 0.96470588 0.78181818 0.86419753 0.96385542 0.93023256 0.88 ] mean value: 0.8512646650067099 key: test_precision value: [0.55555556 0.71428571 1. 0.8 0.66666667 0.42857143 0. 0.83333333 0.83333333 1. ] mean value: 0.6831746031746031 key: train_precision value: [0.56 0.73684211 1. 1. 0.97619048 0.64179104 0.92105263 0.97560976 0.90909091 1. ] mean value: 0.8720576922997172 key: test_recall value: [1. 1. 0.2 0.8 0.5 0.75 0. 1. 1. 0.6 ] mean value: 0.6849999999999999 key: train_recall value: [1. 1. 0.5 0.80952381 0.95348837 1. 0.81395349 0.95238095 0.95238095 0.78571429] mean value: 0.8767441860465116 key: test_accuracy value: [0.6 0.8 0.6 0.8 0.66666667 0.44444444 0.44444444 0.88888889 0.88888889 0.77777778] mean value: 0.691111111111111 key: train_accuracy value: [0.60714286 0.82142857 0.75 0.9047619 0.96470588 0.71764706 0.87058824 0.96470588 0.92941176 0.89411765] mean value: 0.8424509803921568 key: test_roc_auc value: [0.6 0.8 0.6 0.8 0.65 0.475 0.4 0.875 0.875 0.8 ] mean value: 0.6874999999999999 key: train_roc_auc value: [0.60714286 0.82142857 0.75 0.9047619 0.96483942 0.71428571 0.87126246 0.96456257 0.92967885 0.89285714] mean value: 0.8420819490586933 key: test_jcc value: [0.55555556 0.71428571 0.2 0.66666667 0.4 0.375 0. 0.83333333 0.83333333 0.6 ] mean value: 0.5178174603174603 key: train_jcc value: [0.56 0.73684211 0.5 0.80952381 0.93181818 0.64179104 0.76086957 0.93023256 0.86956522 0.78571429] mean value: 0.7526356767843785 key: TN value: 32 mean value: 32.0 key: FP value: 14 mean value: 14.0 key: FN value: 15 mean value: 15.0 key: TP value: 33 mean value: 33.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.51 Accuracy on Blind test: 0.77 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.09869885 0.08734775 0.08220458 0.08168125 0.08259201 0.08675361 0.08353996 0.08585095 0.08518791 0.08337188] mean value: 0.08572287559509277 key: score_time value: [0.01459026 0.01583123 0.01436472 0.01501179 0.01493001 0.01488781 0.01446915 0.01454186 0.01576042 0.0150547 ] mean value: 0.014944195747375488 key: test_mcc value: [0.6 0.81649658 0. 1. 0.55 0.55 0.55 0.79056942 0.5976143 0.55 ] mean value: 0.6004680300637018 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.90909091 0.28571429 1. 0.75 0.75 0.75 0.90909091 0.83333333 0.8 ] mean value: 0.7787229437229437 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.83333333 0.5 1. 0.75 0.75 0.75 0.83333333 0.71428571 0.8 ] mean value: 0.7730952380952381 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 1. 0.2 1. 0.75 0.75 0.75 1. 1. 0.8 ] mean value: 0.805 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.9 0.5 1. 0.77777778 0.77777778 0.77777778 0.88888889 0.77777778 0.77777778] mean value: 0.7977777777777778 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8 0.9 0.5 1. 0.775 0.775 0.775 0.875 0.75 0.775] mean value: 0.7925000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.83333333 0.16666667 1. 0.6 0.6 0.6 0.83333333 0.71428571 0.66666667] mean value: 0.6680952380952382 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 37 mean value: 37.0 key: FP value: 9 mean value: 9.0 key: FN value: 10 mean value: 10.0 key: TP value: 38 mean value: 38.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.73 Accuracy on Blind test: 0.87 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.02725291 0.02678299 0.02645731 0.03228021 0.03082395 0.03413343 0.03731322 0.02821302 0.03662419 0.03972507] mean value: 0.03196063041687012 key: score_time value: [0.02253675 0.0191834 0.01804137 0.01951694 0.02123547 0.01963592 0.01912975 0.01852632 0.02202606 0.0218308 ] mean value: 0.02016627788543701 key: test_mcc value: [0.81649658 0.65465367 0.21821789 1. 0.55 0.55 0.31622777 0.79056942 0.79056942 0.55 ] mean value: 0.6236734737972723 key: train_mcc value: [0.97646729 1. 0.95238095 1. 0.97673145 0.97673145 0.95396693 0.97674419 0.97674419 1. ] mean value: 0.9789766453432106 key: test_fscore value: [0.88888889 0.83333333 0.5 1. 0.75 0.75 0.57142857 0.90909091 0.90909091 0.8 ] mean value: 0.7911832611832612 key: train_fscore value: [0.98823529 1. 0.97619048 1. 0.98850575 0.98850575 0.97727273 0.98823529 0.98823529 1. ] mean value: 0.9895180580069018 key: test_precision value: [1. 0.71428571 0.66666667 1. 0.75 0.75 0.66666667 0.83333333 0.83333333 0.8 ] mean value: 0.8014285714285714 key: train_precision value: [0.97674419 1. 0.97619048 1. 0.97727273 0.97727273 0.95555556 0.97674419 0.97674419 1. ] mean value: 0.9816524044431022 key: test_recall value: [0.8 1. 0.4 1. 0.75 0.75 0.5 1. 1. 0.8 ] mean value: 0.8 key: train_recall value: [1. 1. 0.97619048 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9976190476190476 key: test_accuracy value: [0.9 0.8 0.6 1. 0.77777778 0.77777778 0.66666667 0.88888889 0.88888889 0.77777778] mean value: 0.8077777777777779 key: train_accuracy value: [0.98809524 1. 0.97619048 1. 0.98823529 0.98823529 0.97647059 0.98823529 0.98823529 1. ] mean value: 0.9893697478991598 key: test_roc_auc value: [0.9 0.8 0.6 1. 0.775 0.775 0.65 0.875 0.875 0.775] mean value: 0.8025 key: train_roc_auc value: [0.98809524 1. 0.97619048 1. 0.98809524 0.98809524 0.97619048 0.98837209 0.98837209 1. ] mean value: 0.9893410852713178 key: test_jcc value: [0.8 0.71428571 0.33333333 1. 0.6 0.6 0.4 0.83333333 0.83333333 0.66666667] mean value: 0.6780952380952381 key: train_jcc value: [0.97674419 1. 0.95348837 1. 0.97727273 0.97727273 0.95555556 0.97674419 0.97674419 1. ] mean value: 0.9793821940333569 key: TN value: 38 mean value: 38.0 key: FP value: 9 mean value: 9.0 key: FN value: 9 mean value: 9.0 key: TP value: 38 mean value: 38.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.76 Accuracy on Blind test: 0.89 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.01234031 0.01516294 0.01516199 0.0153873 0.01572132 0.01583409 0.01557136 0.01581097 0.0155983 0.0156157 ] mean value: 0.015220427513122558 key: score_time value: [0.01128292 0.01133847 0.01157451 0.01171637 0.01160073 0.01173162 0.01159739 0.01156569 0.01162362 0.01267815] mean value: 0.011670947074890137 key: test_mcc value: [ 0. 0.40824829 0. 0.40824829 -0.31622777 -0.31622777 -0.55 -0.05976143 -0.31622777 0.31622777] mean value: -0.04257203815726696 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.54545455 0.66666667 0.44444444 0.72727273 0.4 0.4 0.22222222 0.28571429 0.25 0.72727273] mean value: 0.46690476190476193 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.75 0.5 0.66666667 0.33333333 0.33333333 0.2 0.5 0.33333333 0.66666667] mean value: 0.47833333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.6 0.6 0.4 0.8 0.5 0.5 0.25 0.2 0.2 0.8 ] mean value: 0.48500000000000004 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.5 0.7 0.5 0.7 0.33333333 0.33333333 0.22222222 0.44444444 0.33333333 0.66666667] mean value: 0.47333333333333333 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.7 0.5 0.7 0.35 0.35 0.225 0.475 0.35 0.65 ] mean value: 0.4800000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.375 0.5 0.28571429 0.57142857 0.25 0.25 0.125 0.16666667 0.14285714 0.57142857] mean value: 0.3238095238095238 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 22 mean value: 22.0 key: FP value: 24 mean value: 24.0 key: FN value: 25 mean value: 25.0 key: TP value: 23 mean value: 23.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.12 Accuracy on Blind test: 0.56 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.21245885 0.189152 0.19599938 0.19062042 0.19430876 0.19379067 0.19145727 0.19425774 0.19209957 0.18954039] mean value: 0.19436850547790527 key: score_time value: [0.00882983 0.00903535 0.00904727 0.00899792 0.00883126 0.00867009 0.01124382 0.00880098 0.00910807 0.00881577] mean value: 0.009138035774230956 key: test_mcc value: [0.81649658 0.81649658 0.40824829 1. 0.5976143 0.8 0.55 0.79056942 0.79056942 0.79056942] mean value: 0.7360564002112795 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.88888889 0.90909091 0.66666667 1. 0.66666667 0.88888889 0.75 0.90909091 0.90909091 0.90909091] mean value: 0.8497474747474747 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.83333333 0.75 1. 1. 0.8 0.75 0.83333333 0.83333333 0.83333333] mean value: 0.8633333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 1. 0.6 1. 0.5 1. 0.75 1. 1. 1. ] mean value: 0.865 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9 0.9 0.7 1. 0.77777778 0.88888889 0.77777778 0.88888889 0.88888889 0.88888889] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") mean value: 0.861111111111111 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9 0.9 0.7 1. 0.75 0.9 0.775 0.875 0.875 0.875] mean value: 0.8550000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.8 0.83333333 0.5 1. 0.5 0.8 0.6 0.83333333 0.83333333 0.83333333] mean value: 0.7533333333333333 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 40 mean value: 40.0 key: FP value: 6 mean value: 6.0 key: FN value: 7 mean value: 7.0 key: TP value: 41 mean value: 41.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.73 Accuracy on Blind test: 0.87 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01017809 0.01389313 0.01364994 0.01352382 0.01384807 0.0141468 0.01385188 0.01381516 0.01406789 0.01416254] mean value: 0.013513731956481933 key: score_time value: [0.01132488 0.01158571 0.01188087 0.01190567 0.01249957 0.01161242 0.01325011 0.01272106 0.01160336 0.01318431] mean value: 0.012156796455383301 key: test_mcc value: [-0.2 -0.2 0. -0.21821789 -0.47809144 0.31622777 0.05976143 -0.31622777 0.15811388 -0.5976143 ] mean value: -0.1476048325161808 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.4 0.4 0.61538462 0.5 0. 0.57142857 0.33333333 0.25 0.5 0. ] mean value: 0.35701465201465205 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.4 0.4 0.5 0.42857143 0. 0.66666667 0.5 0.33333333 0.66666667 0. ] mean value: 0.3895238095238095 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.4 0.4 0.8 0.6 0. 0.5 0.25 0.2 0.4 0. ] mean value: 0.35500000000000004 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.4 0.4 0.5 0.4 0.33333333 0.66666667 0.55555556 0.33333333 0.55555556 0.22222222] mean value: 0.4366666666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.4 0.4 0.5 0.4 0.3 0.65 0.525 0.35 0.575 0.25 ] mean value: 0.43499999999999994 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.25 0.25 0.44444444 0.33333333 0. 0.4 0.2 0.14285714 0.33333333 0. ] mean value: 0.2353968253968254 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 24 mean value: 24.0 key: FP value: 30 mean value: 30.0 key: FN value: 23 mean value: 23.0 key: TP value: 17 mean value: 17.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: -0.01 Accuracy on Blind test: 0.48 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.03160405 0.02819967 0.02554464 0.03197432 0.030545 0.03205037 0.02785897 0.03191566 0.02753639 0.03187776] mean value: 0.029910683631896973 key: score_time value: [0.01829004 0.02292609 0.02184868 0.01490974 0.02200842 0.01734495 0.02255273 0.02025127 0.02185893 0.02053523] mean value: 0.02025260925292969 key: test_mcc value: [ 0.5 0.40824829 0.40824829 0.81649658 -0.1 0.35 0.31622777 0.79056942 0.55 0.35 ] mean value: 0.4389790342914385 key: train_mcc value: [1. 1. 1. 1. 0.97674419 0.97673145 0.97673145 0.95293466 1. 1. ] mean value: 0.9883141751954136 key: test_fscore value: [0.76923077 0.66666667 0.66666667 0.90909091 0.44444444 0.66666667 0.57142857 0.90909091 0.8 0.66666667] mean value: 0.7069952269952269 key: train_fscore value: [1. 1. 1. 1. 0.98823529 0.98850575 0.98850575 0.97619048 1. 1. ] mean value: 0.9941437264560996 key: test_precision value: [0.625 0.75 0.75 0.83333333 0.4 0.6 0.66666667 0.83333333 0.8 0.75 ] mean value: 0.7008333333333334 key: train_precision value: [1. 1. 1. 1. 1. 0.97727273 0.97727273 0.97619048 1. 1. ] mean value: 0.9930735930735931 key: test_recall value: [1. 0.6 0.6 1. 0.5 0.75 0.5 1. 0.8 0.6 ] mean value: 0.735 key: train_recall value: [1. 1. 1. 1. 0.97674419 1. 1. 0.97619048 1. 1. ] mean value: 0.9952934662236987 key: test_accuracy value: [0.7 0.7 0.7 0.9 0.44444444 0.66666667 0.66666667 0.88888889 0.77777778 0.66666667] mean value: 0.711111111111111 key: train_accuracy value: [1. 1. 1. 1. 0.98823529 0.98823529 0.98823529 0.97647059 1. 1. ] mean value: 0.9941176470588236 key: test_roc_auc value: [0.7 0.7 0.7 0.9 0.45 0.675 0.65 0.875 0.775 0.675] mean value: 0.7100000000000001 key: train_roc_auc value: [1. 1. 1. 1. 0.98837209 0.98809524 0.98809524 0.97646733 1. 1. ] mean value: 0.9941029900332226 key: test_jcc value: [0.625 0.5 0.5 0.83333333 0.28571429 0.5 0.4 0.83333333 0.66666667 0.5 ] mean value: 0.564404761904762 key: train_jcc value: [1. 1. 1. 1. 0.97674419 0.97727273 0.97727273 0.95348837 1. 1. ] mean value: 0.9884778012684989 key: TN value: 32 mean value: 32.0 key: FP value: 12 mean value: 12.0 key: FN value: 15 mean value: 15.0 key: TP value: 35 mean value: 35.0 key: trainingY_neg /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:356: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:357: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_CV['Resampling'] = rs_rus /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:362: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:363: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_BT['Resampling'] = rs_rus value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.56 Accuracy on Blind test: 0.79 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.20420551 0.23215771 0.18351507 0.18484735 0.18196034 0.18221092 0.18410993 0.17798209 0.18241525 0.17692542] mean value: 0.1890329599380493 key: score_time value: [0.02086949 0.01631784 0.02118921 0.02124715 0.02169418 0.02193141 0.02344728 0.02114987 0.02232218 0.0211997 ] mean value: 0.021136832237243653 key: test_mcc value: [ 0.65465367 0.40824829 0.65465367 0.81649658 -0.1 0.55 0.5976143 0.79056942 0.8 0.35 ] mean value: 0.5522235932516835 key: train_mcc value: [1. 1. 1. 1. 0.97674419 1. 1. 0.95293466 1. 1. ] mean value: 0.99296788482835 key: test_fscore value: [0.83333333 0.66666667 0.75 0.90909091 0.44444444 0.75 0.66666667 0.90909091 0.88888889 0.66666667] mean value: 0.7484848484848484 key: train_fscore value: [1. 1. 1. 1. 0.98823529 1. 1. 0.97619048 1. 1. ] mean value: 0.9964425770308123 key: test_precision value: [0.71428571 0.75 1. 0.83333333 0.4 0.75 1. 0.83333333 1. 0.75 ] mean value: 0.8030952380952382 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 0.97619048 1. 1. ] mean value: 0.9976190476190476 key: test_recall value: [1. 0.6 0.6 1. 0.5 0.75 0.5 1. 0.8 0.6 ] mean value: 0.735 key: train_recall value: [1. 1. 1. 1. 0.97674419 1. 1. 0.97619048 1. 1. ] mean value: 0.9952934662236987 key: test_accuracy value: [0.8 0.7 0.8 0.9 0.44444444 0.77777778 0.77777778 0.88888889 0.88888889 0.66666667] mean value: 0.7644444444444445 key: train_accuracy value: [1. 1. 1. 1. 0.98823529 1. 1. 0.97647059 1. 1. ] mean value: 0.9964705882352941 key: test_roc_auc value: [0.8 0.7 0.8 0.9 0.45 0.775 0.75 0.875 0.9 0.675] mean value: 0.7625000000000001 key: train_roc_auc value: [1. 1. 1. 1. 0.98837209 1. 1. 0.97646733 1. 1. ] mean value: 0.996483942414175 key: test_jcc value: [0.71428571 0.5 0.6 0.83333333 0.28571429 0.6 0.5 0.83333333 0.8 0.5 ] mean value: 0.6166666666666667 key: train_jcc value: [1. 1. 1. 1. 0.97674419 1. 1. 0.95348837 1. 1. ] mean value: 0.9930232558139535 key: TN value: 37 mean value: 37.0 key: FP value: 12 mean value: 12.0 key: FN value: 10 mean value: 10.0 key: TP value: 35 mean value: 35.0 key: trainingY_neg value: 47 mean value: 47.0 key: trainingY_pos value: 47 mean value: 47.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.42 Accuracy on Blind test: 0.71 PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.03890443 0.04712152 0.04645896 0.0403986 0.02747059 0.02553892 0.0305891 0.02482867 0.02699924 0.02433038] mean value: 0.03326404094696045 key: score_time value: [0.0116756 0.0124619 0.01195359 0.01166916 0.01168728 0.01173449 0.01199651 0.01173067 0.01165748 0.01163507] mean value: 0.011820173263549805 key: test_mcc value: [0.51639778 0.51639778 0.60714286 0.73214286 0.32732684 0.64465837 0.64465837 0.60714286 0.73214286 0.875 ] mean value: 0.620301056535467 key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( train_mcc value: [0.808911 0.808911 0.85400682 0.8251972 0.8251972 0.81031543 0.79688349 0.85400682 0.82480818 0.79560955] mean value: 0.8203846710669712 key: test_fscore value: [0.71428571 0.71428571 0.8 0.85714286 0.61538462 0.72727273 0.84210526 0.8 0.875 0.93333333] mean value: 0.7878810224862856 key: train_fscore value: [0.90510949 0.9037037 0.92753623 0.91176471 0.91176471 0.90510949 0.89393939 0.92647059 0.91176471 0.89705882] mean value: 0.909422183704111 key: test_precision value: [0.83333333 0.83333333 0.75 0.85714286 0.66666667 1. 0.72727273 0.85714286 0.875 1. ] mean value: 0.8399891774891775 key: train_precision value: [0.89855072 0.91044776 0.92753623 0.92537313 0.92537313 0.91176471 0.921875 0.92647059 0.91176471 0.89705882] mean value: 0.9156214809901897 key: test_recall value: [0.625 0.625 0.85714286 0.85714286 0.57142857 0.57142857 1. 0.75 0.875 0.875 ] mean value: 0.7607142857142858 key: train_recall value: [0.91176471 0.89705882 0.92753623 0.89855072 0.89855072 0.89855072 0.86764706 0.92647059 0.91176471 0.89705882] mean value: 0.9034953111679453 key: test_accuracy value: [0.75 0.75 0.8 0.86666667 0.66666667 0.8 0.8 0.8 0.86666667 0.93333333] mean value: 0.8033333333333335 key: train_accuracy value: [0.90441176 0.90441176 0.9270073 0.91240876 0.91240876 0.90510949 0.89781022 0.9270073 0.91240876 0.89781022] mean value: 0.9100794332331473 key: test_roc_auc value: [0.75 0.75 0.80357143 0.86607143 0.66071429 0.78571429 0.78571429 0.80357143 0.86607143 0.9375 ] mean value: 0.8008928571428571 key: train_roc_auc value: [0.90441176 0.90441176 0.92700341 0.91251066 0.91251066 0.90515772 0.89759165 0.92700341 0.91240409 0.89780477] mean value: 0.910080988917306 key: test_jcc value: [0.55555556 0.55555556 0.66666667 0.75 0.44444444 0.57142857 0.72727273 0.66666667 0.77777778 0.875 ] mean value: 0.6590367965367965 key: train_jcc value: [0.82666667 0.82432432 0.86486486 0.83783784 0.83783784 0.82666667 0.80821918 0.8630137 0.83783784 0.81333333] mean value: 0.8340602246081698 key: TN value: 64 mean value: 64.0 key: FP value: 18 mean value: 18.0 key: FN value: 12 mean value: 12.0 key: TP value: 58 mean value: 58.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.66 Accuracy on Blind test: 0.84 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.60730147 0.62539339 0.75115871 0.61732793 0.60234904 0.76394868 0.6198101 0.61897731 0.96049881 0.89969039] mean value: 0.7066455841064453 key: score_time value: [0.01314354 0.01313806 0.01318407 0.01316643 0.01313019 0.01303983 0.01355648 0.01404309 0.01357317 0.01316524] mean value: 0.013314008712768555 key: test_mcc value: [0.75 0.67419986 0.46428571 0.73214286 0.46428571 0.64465837 0.75592895 0.87287156 0.73214286 0.60714286] mean value: 0.6697658740645969 key: train_mcc value: [1. 0.98540068 1. 1. 0.94160273 1. 1. 1. 1. 1. ] mean value: 0.9927003404297572 key: test_fscore value: [0.875 0.76923077 0.71428571 0.85714286 0.71428571 0.72727273 0.88888889 0.94117647 0.875 0.8 ] mean value: 0.8162283141694907 key: train_fscore value: [1. 0.99259259 1. 1. 0.97101449 1. 1. 1. 1. 1. ] mean value: 0.9963607085346216 key: test_precision value: [0.875 1. 0.71428571 0.85714286 0.71428571 1. 0.8 0.88888889 0.875 0.85714286] mean value: 0.8581746031746033 key: train_precision value: [1. 1. 1. 1. 0.97101449 1. 1. 1. 1. 1. ] mean value: 0.9971014492753623 key: test_recall value: [0.875 0.625 0.71428571 0.85714286 0.71428571 0.57142857 1. 1. 0.875 0.75 ] mean value: 0.7982142857142857 key: train_recall value: [1. 0.98529412 1. 1. 0.97101449 1. 1. 1. 1. 1. ] mean value: 0.9956308610400683 key: test_accuracy value: [0.875 0.8125 0.73333333 0.86666667 0.73333333 0.8 0.86666667 0.93333333 0.86666667 0.8 ] mean value: 0.8287500000000001 key: train_accuracy value: [1. 0.99264706 1. 1. 0.97080292 1. 1. 1. 1. 1. ] mean value: 0.9963449978531559 key: test_roc_auc value: [0.875 0.8125 0.73214286 0.86607143 0.73214286 0.78571429 0.85714286 0.92857143 0.86607143 0.80357143] mean value: 0.8258928571428571 key: train_roc_auc value: [1. 0.99264706 1. 1. 0.97080136 1. 1. 1. 1. 1. ] mean value: 0.99634484228474 key: test_jcc value: [0.77777778 0.625 0.55555556 0.75 0.55555556 0.57142857 0.8 0.88888889 0.77777778 0.66666667] mean value: 0.6968650793650794 key: train_jcc value: [1. 0.98529412 1. 1. 0.94366197 1. 1. 1. 1. 1. ] mean value: 0.9928956089478046 key: TN value: 67 mean value: 67.0 key: FP value: 16 mean value: 16.0 key: FN value: 9 mean value: 9.0 key: TP value: 60 mean value: 60.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.73 Accuracy on Blind test: 0.87 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01197147 0.01149893 0.00909448 0.00837827 0.00916386 0.00894952 0.00821567 0.00840712 0.00828362 0.00828409] mean value: 0.009224700927734374 key: score_time value: [0.01136208 0.01117897 0.00898957 0.00902128 0.0088501 0.00829625 0.00840592 0.00831342 0.00911379 0.00844216] mean value: 0.009197354316711426 key: test_mcc value: [ 0.13483997 0.62994079 -0.18898224 0.87287156 0.05455447 0.05455447 0.33928571 0.33928571 0.21821789 0.46428571] mean value: 0.2918854063491848 key: train_mcc value: [0.45593166 0.51520752 0.50394373 0.51832907 0.56316734 0.47754676 0.49979869 0.50427283 0.46184366 0.43147539] mean value: 0.493151664179406 key: test_fscore value: [0.46153846 0.8 0.47058824 0.92307692 0.46153846 0.46153846 0.66666667 0.66666667 0.57142857 0.75 ] mean value: 0.623304244774833 key: train_fscore value: [0.72592593 0.76258993 0.75 0.75912409 0.7761194 0.76433121 0.68421053 0.74242424 0.71317829 0.70229008] mean value: 0.7380193694400431 key: test_precision value: [0.6 0.85714286 0.4 1. 0.5 0.5 0.71428571 0.71428571 0.66666667 0.75 ] mean value: 0.6702380952380953 key: train_precision value: [0.73134328 0.74647887 0.76119403 0.76470588 0.8 0.68181818 0.84782609 0.765625 0.75409836 0.73015873] mean value: 0.7583248428614385 key: test_recall value: [0.375 0.75 0.57142857 0.85714286 0.42857143 0.42857143 0.625 0.625 0.5 0.75 ] mean value: 0.5910714285714286 key: train_recall value: [0.72058824 0.77941176 0.73913043 0.75362319 0.75362319 0.86956522 0.57352941 0.72058824 0.67647059 0.67647059] mean value: 0.7263000852514919 key: test_accuracy value: [0.5625 0.8125 0.4 0.93333333 0.53333333 0.53333333 0.66666667 0.66666667 0.6 0.73333333] mean value: 0.6441666666666667 key: train_accuracy value: [0.72794118 0.75735294 0.75182482 0.75912409 0.7810219 0.72992701 0.73722628 0.75182482 0.72992701 0.71532847] mean value: 0.7441498497209104 key: test_roc_auc value: [0.5625 0.8125 0.41071429 0.92857143 0.52678571 0.52678571 0.66964286 0.66964286 0.60714286 0.73214286] mean value: 0.6446428571428571 key: train_roc_auc value: [0.72794118 0.75735294 0.75191816 0.75916454 0.78122336 0.72890026 0.73604007 0.75159847 0.72953964 0.71504689] mean value: 0.7438725490196079 key: test_jcc value: [0.3 0.66666667 0.30769231 0.85714286 0.3 0.3 0.5 0.5 0.4 0.6 ] mean value: 0.4731501831501831 key: train_jcc value: [0.56976744 0.61627907 0.6 0.61176471 0.63414634 0.6185567 0.52 0.59036145 0.55421687 0.54117647] mean value: 0.5856269043845849 key: TN value: 53 mean value: 53.0 key: FP value: 31 mean value: 31.0 key: FN value: 23 mean value: 23.0 key: TP value: 45 mean value: 45.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.41 Accuracy on Blind test: 0.71 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00908732 0.00908971 0.00919962 0.00935507 0.00946116 0.00924921 0.00891304 0.00912547 0.00865936 0.00867224] mean value: 0.009081220626831055 key: score_time value: [0.00918055 0.00966501 0.00865173 0.00877142 0.00867128 0.00875568 0.00846004 0.00884628 0.0087254 0.00924444] mean value: 0.008897185325622559 key: test_mcc value: [ 0.48038446 0.51639778 -0.46428571 0.64465837 -0.33928571 0.05455447 -0.19642857 0.49099025 0.09449112 -0.20044593] mean value: 0.10810305245378571 key: train_mcc value: [0.34459104 0.46444879 0.40740412 0.43493568 0.49877088 0.4754592 0.45696395 0.42182014 0.40803461 0.4060076 ] mean value: 0.43184360253867615 key: test_fscore value: [0.54545455 0.71428571 0.26666667 0.72727273 0.28571429 0.46153846 0.4 0.71428571 0.46153846 0.18181818] mean value: 0.4758574758574758 key: train_fscore value: [0.63414634 0.69918699 0.67716535 0.69767442 0.72 0.73134328 0.68333333 0.67741935 0.66666667 0.672 ] mean value: 0.6858935744689493 key: test_precision value: [1. 0.83333333 0.25 1. 0.28571429 0.5 0.42857143 0.83333333 0.6 0.33333333] mean value: 0.6064285714285714 key: train_precision value: [0.70909091 0.78181818 0.74137931 0.75 0.80357143 0.75384615 0.78846154 0.75 0.74545455 0.73684211] mean value: 0.7560464172850743 key: test_recall value: [0.375 0.625 0.28571429 0.57142857 0.28571429 0.42857143 0.375 0.625 0.375 0.125 ] mean value: 0.40714285714285714 key: train_recall value: [0.57352941 0.63235294 0.62318841 0.65217391 0.65217391 0.71014493 0.60294118 0.61764706 0.60294118 0.61764706] mean value: 0.6284739982949701 key: test_accuracy value: [0.6875 0.75 0.26666667 0.8 0.33333333 0.53333333 0.4 0.73333333 0.53333333 0.4 ] mean value: 0.54375 key: train_accuracy value: [0.66911765 0.72794118 0.70072993 0.71532847 0.74452555 0.73722628 0.72262774 0.7080292 0.70072993 0.70072993] mean value: 0.7126985830828682 key: test_roc_auc value: [0.6875 0.75 0.26785714 0.78571429 0.33035714 0.52678571 0.40178571 0.74107143 0.54464286 0.41964286] mean value: 0.5455357142857143 key: train_roc_auc value: [0.66911765 0.72794118 0.70130009 0.71579284 0.7452046 0.7374254 0.72176044 0.70737425 0.70002131 0.70012788] mean value: 0.7126065643648765 key: test_jcc value: [0.375 0.55555556 0.15384615 0.57142857 0.16666667 0.3 0.25 0.55555556 0.3 0.1 ] mean value: 0.33280525030525027 key: train_jcc value: [0.46428571 0.5375 0.51190476 0.53571429 0.5625 0.57647059 0.51898734 0.51219512 0.5 0.5060241 ] mean value: 0.522558191024897 key: TN value: 52 mean value: 52.0 key: FP value: 45 mean value: 45.0 key: FN value: 24 mean value: 24.0 key: TP value: 31 mean value: 31.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.33 Accuracy on Blind test: 0.66 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01157355 0.00804496 0.00798678 0.0088408 0.00798821 0.00800323 0.00893235 0.00840855 0.00808954 0.00812149] mean value: 0.008598947525024414 key: score_time value: [0.01089644 0.00977564 0.00971937 0.00999427 0.01003385 0.0099225 0.00957441 0.01004291 0.00989676 0.01009011] mean value: 0.00999462604522705 key: test_mcc value: [ 0.40451992 -0.13483997 -0.04029115 0.07142857 -0.32732684 0.04029115 -0.07142857 0.09449112 0.37796447 0.37796447] mean value: 0.07927731739020696 key: train_mcc value: [0.38655567 0.39917949 0.41698711 0.37355916 0.41236007 0.40282023 0.43781657 0.40309474 0.39212366 0.38722856] mean value: 0.4011725253527968 key: test_fscore value: [0.61538462 0.30769231 0.55555556 0.53333333 0.375 0.36363636 0.5 0.46153846 0.61538462 0.61538462] mean value: 0.4942909867909867 key: train_fscore value: [0.66666667 0.68217054 0.70149254 0.67669173 0.66666667 0.69172932 0.68292683 0.68217054 0.66129032 0.68181818] mean value: 0.6793623342216782 key: test_precision value: [0.8 0.4 0.45454545 0.5 0.33333333 0.5 0.5 0.6 0.8 0.8 ] mean value: 0.5687878787878787 key: train_precision value: [0.72413793 0.72131148 0.72307692 0.703125 0.75925926 0.71875 0.76363636 0.72131148 0.73214286 0.703125 ] mean value: 0.7269876284969559 key: test_recall value: [0.5 0.25 0.71428571 0.57142857 0.42857143 0.28571429 0.5 0.375 0.5 0.5 ] mean value: 0.4625 key: train_recall value: [0.61764706 0.64705882 0.68115942 0.65217391 0.5942029 0.66666667 0.61764706 0.64705882 0.60294118 0.66176471] mean value: 0.6388320545609548 key: test_accuracy value: [0.6875 0.4375 0.46666667 0.53333333 0.33333333 0.53333333 0.46666667 0.53333333 0.66666667 0.66666667] mean value: 0.5325 key: train_accuracy value: [0.69117647 0.69852941 0.7080292 0.68613139 0.70072993 0.70072993 0.71532847 0.70072993 0.69343066 0.69343066] mean value: 0.6988246028338342 key: test_roc_auc value: [0.6875 0.4375 0.48214286 0.53571429 0.33928571 0.51785714 0.46428571 0.54464286 0.67857143 0.67857143] mean value: 0.5366071428571428 key: train_roc_auc value: [0.69117647 0.69852941 0.70822677 0.68638107 0.70151321 0.70098039 0.71462063 0.70034101 0.69277494 0.69320119] mean value: 0.6987745098039216 key: test_jcc value: [0.44444444 0.18181818 0.38461538 0.36363636 0.23076923 0.22222222 0.33333333 0.3 0.44444444 0.44444444] mean value: 0.334972804972805 key: train_jcc value: [0.5 0.51764706 0.54022989 0.51136364 0.5 0.52873563 0.51851852 0.51764706 0.4939759 0.51724138] mean value: 0.5145359072695397 key: TN value: 46 mean value: 46.0 key: FP value: 41 mean value: 41.0 key: FN value: 30 mean value: 30.0 key: TP value: 35 mean value: 35.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.02 Accuracy on Blind test: 0.53 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.00973821 0.00956583 0.00944161 0.00950122 0.0096302 0.00952268 0.00966358 0.00958252 0.00967121 0.00958061] mean value: 0.009589767456054688 key: score_time value: [0.00870752 0.0086112 0.00871968 0.00859761 0.00864124 0.00872111 0.00869703 0.00871563 0.00870109 0.00881076] mean value: 0.008692288398742675 key: test_mcc value: [ 0.40451992 0.40451992 -0.04029115 0.87287156 0.07142857 0.18898224 0.32732684 0.49099025 0.60714286 0.60714286] mean value: 0.3934633858302462 key: train_mcc value: [0.6918501 0.677644 0.70801364 0.66581484 0.73747083 0.7082535 0.73747083 0.75186529 0.64981886 0.69345557] mean value: 0.702165745179242 key: test_fscore value: [0.61538462 0.61538462 0.55555556 0.92307692 0.53333333 0.5 0.70588235 0.71428571 0.8 0.8 ] mean value: 0.6762903109961933 key: train_fscore value: [0.84892086 0.83333333 0.85507246 0.83916084 0.87142857 0.85714286 0.86567164 0.87407407 0.82089552 0.84444444] mean value: 0.8510144610840692 key: test_precision value: [0.8 0.8 0.45454545 1. 0.5 0.6 0.66666667 0.83333333 0.85714286 0.85714286] mean value: 0.7368831168831168 key: train_precision value: [0.83098592 0.859375 0.85507246 0.81081081 0.85915493 0.84507042 0.87878788 0.88059701 0.83333333 0.85074627] mean value: 0.8503934037887863 key: test_recall value: [0.5 0.5 0.71428571 0.85714286 0.57142857 0.42857143 0.75 0.625 0.75 0.75 ] mean value: 0.6446428571428571 key: train_recall value: [0.86764706 0.80882353 0.85507246 0.86956522 0.88405797 0.86956522 0.85294118 0.86764706 0.80882353 0.83823529] mean value: 0.8522378516624041 key: test_accuracy value: [0.6875 0.6875 0.46666667 0.93333333 0.53333333 0.6 0.66666667 0.73333333 0.8 0.8 ] mean value: 0.6908333333333333 key: train_accuracy value: [0.84558824 0.83823529 0.8540146 0.83211679 0.86861314 0.8540146 0.86861314 0.87591241 0.82481752 0.84671533] mean value: 0.8508641047659941 key: test_roc_auc value: [0.6875 0.6875 0.48214286 0.92857143 0.53571429 0.58928571 0.66071429 0.74107143 0.80357143 0.80357143] mean value: 0.6919642857142858 key: train_roc_auc value: [0.84558824 0.83823529 0.85400682 0.83184143 0.86849957 0.85390026 0.86849957 0.87585251 0.82470162 0.84665388] mean value: 0.8507779198635976 key: test_jcc value: [0.44444444 0.44444444 0.38461538 0.85714286 0.36363636 0.33333333 0.54545455 0.55555556 0.66666667 0.66666667] mean value: 0.5261960261960262 key: train_jcc value: [0.7375 0.71428571 0.74683544 0.72289157 0.7721519 0.75 0.76315789 0.77631579 0.69620253 0.73076923] mean value: 0.7410110068948254 key: TN value: 56 mean value: 56.0 key: FP value: 27 mean value: 27.0 key: FN value: 20 mean value: 20.0 key: TP value: 49 mean value: 49.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.34 Accuracy on Blind test: 0.68 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.64121795 0.56911659 0.5362215 0.69446111 0.57372665 0.55694437 0.56460571 0.69939494 0.5615809 0.53818989] mean value: 0.5935459613800049 key: score_time value: [0.01190567 0.01195049 0.01202846 0.01563144 0.01198316 0.01194787 0.01196098 0.01196241 0.01197839 0.0123086 ] mean value: 0.01236574649810791 key: test_mcc value: [0.51639778 0.40451992 0.46428571 0.60714286 0.6000992 0.34247476 0.47245559 0.60714286 0.73214286 0.37796447] mean value: 0.5124626004817079 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.71428571 0.61538462 0.71428571 0.8 0.76923077 0.54545455 0.77777778 0.8 0.875 0.61538462] mean value: 0.7226803751803752 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.83333333 0.8 0.71428571 0.75 0.83333333 0.75 0.7 0.85714286 0.875 0.8 ] mean value: 0.7913095238095238 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.625 0.5 0.71428571 0.85714286 0.71428571 0.42857143 0.875 0.75 0.875 0.5 ] mean value: 0.6839285714285713 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.75 0.6875 0.73333333 0.8 0.8 0.66666667 0.73333333 0.8 0.86666667 0.66666667] mean value: 0.7504166666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.6875 0.73214286 0.80357143 0.79464286 0.65178571 0.72321429 0.80357143 0.86607143 0.67857143] mean value: 0.7491071428571429 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.55555556 0.44444444 0.55555556 0.66666667 0.625 0.375 0.63636364 0.66666667 0.77777778 0.44444444] mean value: 0.5747474747474748 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 62 mean value: 62.0 key: FP value: 24 mean value: 24.0 key: FN value: 14 mean value: 14.0 key: TP value: 52 mean value: 52.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.55 Accuracy on Blind test: 0.79 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01560283 0.01606917 0.01327395 0.01165366 0.01096725 0.0107851 0.01119351 0.0121634 0.01090837 0.01211286] mean value: 0.012473011016845703 key: score_time value: [0.0116744 0.0098846 0.00842261 0.00833201 0.0082736 0.00857115 0.00834417 0.00823522 0.00829744 0.00839138] mean value: 0.008842658996582032 key: test_mcc value: [0.8819171 0.8819171 0.87287156 0.6000992 0.75592895 0.87287156 0.87287156 0.60714286 0.875 0.60714286] mean value: 0.782776274866145 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.93333333 0.94117647 0.92307692 0.76923077 0.83333333 0.92307692 0.94117647 0.8 0.93333333 0.8 ] mean value: 0.8797737556561087 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.88888889 1. 0.83333333 1. 1. 0.88888889 0.85714286 1. 0.85714286] mean value: 0.9325396825396826 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.875 1. 0.85714286 0.71428571 0.71428571 0.85714286 1. 0.75 0.875 0.75 ] mean value: 0.8392857142857142 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9375 0.9375 0.93333333 0.8 0.86666667 0.93333333 0.93333333 0.8 0.93333333 0.8 ] mean value: 0.8875 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9375 0.9375 0.92857143 0.79464286 0.85714286 0.92857143 0.92857143 0.80357143 0.9375 0.80357143] mean value: 0.8857142857142858 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.875 0.88888889 0.85714286 0.625 0.71428571 0.85714286 0.88888889 0.66666667 0.875 0.66666667] mean value: 0.7914682539682539 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 71 mean value: 71.0 key: FP value: 12 mean value: 12.0 key: FN value: 5 mean value: 5.0 key: TP value: 64 mean value: 64.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.76 Accuracy on Blind test: 0.89 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.09069848 0.08667779 0.08607292 0.08668923 0.08903408 0.08655572 0.08875108 0.09235215 0.08659458 0.0865767 ] mean value: 0.08800027370452881 key: score_time value: [0.01674008 0.01685047 0.01681376 0.01759171 0.01680708 0.01680541 0.01830816 0.01680827 0.01712513 0.01683068] mean value: 0.017068076133728027 key: test_mcc value: [0.5 0.62994079 0.37796447 0.6000992 0.33928571 0.6000992 0.64465837 0.60714286 1. 0.6000992 ] mean value: 0.5899289798453752 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.75 0.8 0.70588235 0.76923077 0.66666667 0.76923077 0.84210526 0.8 1. 0.82352941] mean value: 0.7926645232991982 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.85714286 0.6 0.83333333 0.625 0.83333333 0.72727273 0.85714286 1. 0.77777778] mean value: 0.7861002886002886 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.75 0.85714286 0.71428571 0.71428571 0.71428571 1. 0.75 1. 0.875 ] mean value: 0.8125 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.75 0.8125 0.66666667 0.8 0.66666667 0.8 0.8 0.8 1. 0.8 ] mean value: 0.7895833333333333 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.8125 0.67857143 0.79464286 0.66964286 0.79464286 0.78571429 0.80357143 1. 0.79464286] mean value: 0.7883928571428571 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.6 0.66666667 0.54545455 0.625 0.5 0.625 0.72727273 0.66666667 1. 0.7 ] mean value: 0.6656060606060606 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 58 mean value: 58.0 key: FP value: 14 mean value: 14.0 key: FN value: 18 mean value: 18.0 key: TP value: 62 mean value: 62.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.37 Accuracy on Blind test: 0.71 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00859261 0.00851965 0.00834799 0.00839686 0.00845599 0.00841427 0.00834894 0.00838566 0.00838327 0.00876045] mean value: 0.008460569381713866 key: score_time value: [0.00829458 0.00828695 0.00835848 0.00816917 0.00823879 0.00826216 0.00824952 0.00828099 0.0082531 0.00842953] mean value: 0.008282327651977539 key: test_mcc value: [0.62994079 0.37796447 0.49099025 0.33928571 0.20044593 0.75592895 0.09449112 0.21821789 0.56407607 0.46428571] mean value: 0.41356269037191895 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.82352941 0.66666667 0.75 0.66666667 0.4 0.83333333 0.46153846 0.57142857 0.66666667 0.75 ] mean value: 0.6589829778065073 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.77777778 0.71428571 0.66666667 0.625 0.66666667 1. 0.6 0.66666667 1. 0.75 ] mean value: 0.7467063492063492 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.875 0.625 0.85714286 0.71428571 0.28571429 0.71428571 0.375 0.5 0.5 0.75 ] mean value: 0.6196428571428572 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.6875 0.73333333 0.66666667 0.6 0.86666667 0.53333333 0.6 0.73333333 0.73333333] mean value: 0.6966666666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.6875 0.74107143 0.66964286 0.58035714 0.85714286 0.54464286 0.60714286 0.75 0.73214286] mean value: 0.6982142857142857 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.7 0.5 0.6 0.5 0.25 0.71428571 0.3 0.4 0.5 0.6 ] mean value: 0.5064285714285713 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 59 mean value: 59.0 key: FP value: 29 mean value: 29.0 key: FN value: 17 mean value: 17.0 key: TP value: 47 mean value: 47.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: -0.17 Accuracy on Blind test: 0.45 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.12350488 1.12175465 1.12073541 1.12479877 1.1150465 1.11790395 1.12138486 1.13313246 1.12897301 1.12279344] mean value: 1.123002791404724 key: score_time value: [0.08789635 0.08756709 0.08721948 0.08704138 0.08737206 0.0873611 0.08692431 0.09309626 0.08746028 0.0869782 ] mean value: 0.08789165019989013 key: test_mcc value: [0.62994079 0.62994079 0.73214286 0.6000992 0.6000992 0.75592895 0.64465837 0.73214286 1. 0.87287156] mean value: 0.7197824565463824 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.8 0.85714286 0.76923077 0.76923077 0.83333333 0.84210526 0.875 1. 0.94117647] mean value: 0.8487219462683859 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.85714286 0.85714286 0.83333333 0.83333333 1. 0.72727273 0.875 1. 0.88888889] mean value: 0.8729256854256855 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.75 0.85714286 0.71428571 0.71428571 0.71428571 1. 0.875 1. 1. ] mean value: 0.8375 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.8125 0.86666667 0.8 0.8 0.86666667 0.8 0.86666667 1. 0.93333333] mean value: 0.8558333333333333 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.8125 0.86607143 0.79464286 0.79464286 0.85714286 0.78571429 0.86607143 1. 0.92857143] mean value: 0.8517857142857143 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.66666667 0.75 0.625 0.625 0.71428571 0.72727273 0.77777778 1. 0.88888889] mean value: 0.7441558441558442 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 66 mean value: 66.0 key: FP value: 12 mean value: 12.0 key: FN value: 10 mean value: 10.0 key: TP value: 64 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( mean value: 64.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.66 Accuracy on Blind test: 0.84 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.79375076 0.85313535 0.81692481 0.92896914 0.86560106 0.92556882 0.86164188 0.86464143 0.81101966 0.85759234] mean value: 0.8578845262527466 key: score_time value: [0.19001794 0.19530773 0.17721558 0.17968392 0.17640471 0.17840695 0.15903306 0.17607164 0.18895531 0.16712546] mean value: 0.17882223129272462 key: test_mcc value: [0.77459667 0.62994079 0.49099025 0.73214286 0.60714286 0.53452248 0.64465837 0.73214286 0.73214286 0.87287156] mean value: 0.6751151555181728 key: train_mcc value: [0.92737353 0.92737353 0.91392776 0.92787101 0.9158731 0.92787101 0.9139999 0.91281179 0.92791659 0.92791659] mean value: 0.9222934807228448 key: test_fscore value: [0.85714286 0.8 0.75 0.85714286 0.8 0.6 0.84210526 0.875 0.875 0.94117647] mean value: 0.8197567448031844 key: train_fscore value: [0.96402878 0.96402878 0.95774648 0.96453901 0.95833333 0.96453901 0.95714286 0.95652174 0.96402878 0.96402878] mean value: 0.9614937530577929 key: test_precision value: [1. 0.85714286 0.66666667 0.85714286 0.75 1. 0.72727273 0.875 0.875 0.88888889] mean value: 0.8497113997113998 key: train_precision value: [0.94366197 0.94366197 0.93150685 0.94444444 0.92 0.94444444 0.93055556 0.94285714 0.94366197 0.94366197] mean value: 0.9388456323940598 key: test_recall value: [0.75 0.75 0.85714286 0.85714286 0.85714286 0.42857143 1. 0.875 0.875 1. ] mean value: 0.825 key: train_recall value: [0.98529412 0.98529412 0.98550725 0.98550725 1. 0.98550725 0.98529412 0.97058824 0.98529412 0.98529412] mean value: 0.9853580562659847 key: test_accuracy value: [0.875 0.8125 0.73333333 0.86666667 0.8 0.73333333 0.8 0.86666667 0.86666667 0.93333333] mean value: 0.8287500000000001 key: train_accuracy value: [0.96323529 0.96323529 0.95620438 0.96350365 0.95620438 0.96350365 0.95620438 0.95620438 0.96350365 0.96350365] mean value: 0.9605302705023616 key: test_roc_auc value: [0.875 0.8125 0.74107143 0.86607143 0.80357143 0.71428571 0.78571429 0.86607143 0.86607143 0.92857143] mean value: 0.8258928571428571 key: train_roc_auc value: [0.96323529 0.96323529 0.95598892 0.96334186 0.95588235 0.96334186 0.95641517 0.95630861 0.96366155 0.96366155] mean value: 0.9605072463768115 key: test_jcc value: [0.75 0.66666667 0.6 0.75 0.66666667 0.42857143 0.72727273 0.77777778 0.77777778 0.88888889] mean value: 0.7033621933621934 key: train_jcc value: [0.93055556 0.93055556 0.91891892 0.93150685 0.92 0.93150685 0.91780822 0.91666667 0.93055556 0.93055556] mean value: 0.9258629725616027 key: TN value: 63 mean value: 63.0 key: FP value: 13 mean value: 13.0 key: FN value: 13 mean value: 13.0 key: TP value: 63 mean value: 63.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.73 Accuracy on Blind test: 0.87 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.06579757 0.04870868 0.04732394 0.0479579 0.04998922 0.04546762 0.04415822 0.04513502 0.04604268 0.04867816] mean value: 0.04892590045928955 key: score_time value: [0.01023173 0.01019073 0.01020455 0.01103163 0.01026368 0.01042342 0.01025009 0.01019382 0.01028919 0.01038814] mean value: 0.010346698760986327 key: test_mcc value: [0.77459667 0.8819171 0.73214286 0.875 0.75592895 1. 0.64465837 0.73214286 1. 0.87287156] mean value: 0.8269258365398123 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.85714286 0.94117647 0.85714286 0.93333333 0.83333333 1. 0.84210526 0.875 1. 0.94117647] mean value: 0.9080410585286746 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.88888889 0.85714286 0.875 1. 1. 0.72727273 0.875 1. 0.88888889] mean value: 0.9112193362193363 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 1. 0.85714286 1. 0.71428571 1. 1. 0.875 1. 1. ] mean value: 0.9196428571428571 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.875 0.9375 0.86666667 0.93333333 0.86666667 1. 0.8 0.86666667 1. 0.93333333] mean value: 0.9079166666666666 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.875 0.9375 0.86607143 0.9375 0.85714286 1. 0.78571429 0.86607143 1. 0.92857143] mean value: 0.9053571428571429 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.75 0.88888889 0.75 0.875 0.71428571 1. 0.72727273 0.77777778 1. 0.88888889] mean value: 0.8372113997113997 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 68 mean value: 68.0 key: FP value: 6 mean value: 6.0 key: FN value: 8 mean value: 8.0 key: TP value: 70 mean value: 70.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.83 Accuracy on Blind test: 0.92 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02466106 0.06205916 0.04544544 0.05353069 0.06253004 0.04796767 0.02154779 0.02102947 0.02075434 0.04984069] mean value: 0.040936636924743655 key: score_time value: [0.01995134 0.02262306 0.02079415 0.0347898 0.02177382 0.01164317 0.01162004 0.01168466 0.01153636 0.02154279] mean value: 0.01879591941833496 key: test_mcc value: [0.51639778 0.40451992 0.21821789 0.75592895 0.32732684 0.18898224 0.21821789 0.33928571 0.60714286 0.37796447] mean value: 0.3953984539759108 key: train_mcc value: [1. 1. 1. 0.98550725 1. 1. 1. 1. 1. 1. ] mean value: 0.9985507246376812 key: test_fscore value: [0.71428571 0.61538462 0.625 0.83333333 0.61538462 0.5 0.57142857 0.66666667 0.8 0.61538462] mean value: 0.6556868131868132 key: train_fscore value: [1. 1. 1. 0.99270073 1. 1. 1. 1. 1. 1. ] mean value: 0.9992700729927007 key: test_precision value: [0.83333333 0.8 0.55555556 1. 0.66666667 0.6 0.66666667 0.71428571 0.85714286 0.8 ] mean value: 0.7493650793650793 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.625 0.5 0.71428571 0.71428571 0.57142857 0.42857143 0.5 0.625 0.75 0.5 ] mean value: 0.5928571428571429 key: train_recall value: [1. 1. 1. 0.98550725 1. 1. 1. 1. 1. 1. ] mean value: 0.9985507246376812 key: test_accuracy value: [0.75 0.6875 0.6 0.86666667 0.66666667 0.6 0.6 0.66666667 0.8 0.66666667] mean value: 0.6904166666666667 key: train_accuracy value: [1. 1. 1. 0.99270073 1. 1. 1. 1. 1. 1. ] mean value: 0.9992700729927007 key: test_roc_auc value: [0.75 0.6875 0.60714286 0.85714286 0.66071429 0.58928571 0.60714286 0.66964286 0.80357143 0.67857143] mean value: 0.6910714285714287 key: train_roc_auc value: [1. 1. 1. 0.99275362 1. 1. 1. 1. 1. 1. ] mean value: 0.9992753623188406 key: test_jcc value: [0.55555556 0.44444444 0.45454545 0.71428571 0.44444444 0.33333333 0.4 0.5 0.66666667 0.44444444] mean value: 0.49577200577200575 key: train_jcc value: [1. 1. 1. 0.98550725 1. 1. 1. 1. 1. 1. ] mean value: 0.9985507246376812 key: TN value: 60 mean value: 60.0 key: FP value: 31 mean value: 31.0 key: FN value: 16 mean value: 16.0 key: TP value: 45 mean value: 45.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.42 Accuracy on Blind test: 0.71 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.02083278 0.00883913 0.0087893 0.00854588 0.00853181 0.00851607 0.00848556 0.00949192 0.00852609 0.00841713] mean value: 0.009897565841674805 key: score_time value: [0.01730442 0.00882506 0.00852299 0.00838923 0.00841427 0.00838327 0.00844216 0.00907063 0.00833464 0.00883269] mean value: 0.009451937675476075 key: test_mcc value: [ 0.13483997 0.62994079 -0.18898224 0.87287156 0.07142857 0.07142857 0.34247476 0.19642857 0.46428571 0.60714286] mean value: 0.32018591307057886 key: train_mcc value: [0.44136741 0.41337702 0.4457507 0.46009427 0.50373224 0.47592003 0.53458044 0.4457507 0.50427283 0.5182264 ] mean value: 0.47430720433398815 key: test_fscore value: [0.46153846 0.8 0.47058824 0.92307692 0.53333333 0.53333333 0.73684211 0.625 0.75 0.8 ] mean value: 0.6633712391839327 key: train_fscore value: [0.72463768 0.71830986 0.73239437 0.73758865 0.75714286 0.75 0.75384615 0.71212121 0.74242424 0.75555556] mean value: 0.7384020580083823 key: test_precision value: [0.6 0.85714286 0.4 1. 0.5 0.5 0.63636364 0.625 0.75 0.85714286] mean value: 0.672564935064935 key: train_precision value: [0.71428571 0.68918919 0.71232877 0.72222222 0.74647887 0.72 0.79032258 0.734375 0.765625 0.76119403] mean value: 0.7356021376555757 key: test_recall value: [0.375 0.75 0.57142857 0.85714286 0.57142857 0.57142857 0.875 0.625 0.75 0.75 ] mean value: 0.6696428571428571 key: train_recall value: [0.73529412 0.75 0.75362319 0.75362319 0.76811594 0.7826087 0.72058824 0.69117647 0.72058824 0.75 ] mean value: 0.7425618073316282 key: test_accuracy value: [0.5625 0.8125 0.4 0.93333333 0.53333333 0.53333333 0.66666667 0.6 0.73333333 0.8 ] mean value: 0.6575 key: train_accuracy value: [0.72058824 0.70588235 0.72262774 0.72992701 0.75182482 0.73722628 0.76642336 0.72262774 0.75182482 0.75912409] mean value: 0.7368076427651352 key: test_roc_auc value: [0.5625 0.8125 0.41071429 0.92857143 0.53571429 0.53571429 0.65178571 0.59821429 0.73214286 0.80357143] mean value: 0.6571428571428571 key: train_roc_auc value: [0.72058824 0.70588235 0.72239983 0.72975277 0.75170503 0.73689258 0.76609122 0.72239983 0.75159847 0.75905797] mean value: 0.7366368286445013 key: test_jcc value: [0.3 0.66666667 0.30769231 0.85714286 0.36363636 0.36363636 0.58333333 0.45454545 0.6 0.66666667] mean value: 0.5163320013320013 key: train_jcc value: [0.56818182 0.56043956 0.57777778 0.58426966 0.6091954 0.6 0.60493827 0.55294118 0.59036145 0.60714286] mean value: 0.5855247972620871 key: TN value: 49 mean value: 49.0 key: FP value: 25 mean value: 25.0 key: FN value: 27 mean value: 27.0 key: TP value: 51 mean value: 51.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.36 Accuracy on Blind test: 0.68 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.00937438 0.01451468 0.01359081 0.01436234 0.01521063 0.01441789 0.01421762 0.01421499 0.01419473 0.01438093] mean value: 0.013847899436950684 key: score_time value: [0.00882316 0.01088619 0.01098871 0.01142168 0.01146245 0.01141715 0.0114491 0.01136684 0.01177049 0.01148844] mean value: 0.011107420921325684 key: test_mcc value: [0.28867513 0.57735027 0.47245559 0.66143783 0.47245559 0.28571429 0.26189246 0.73214286 0.56407607 0.36689969] mean value: 0.4683099787909484 key: train_mcc value: [0.73854895 0.92657079 0.63749097 0.80014442 0.89869927 0.58151892 0.41647809 0.91281179 0.56742751 0.65830386] mean value: 0.7137994554372971 key: test_fscore value: [0.7 0.66666667 0.66666667 0.82352941 0.66666667 0.25 0.5 0.875 0.66666667 0.4 ] mean value: 0.6215196078431373 key: train_fscore value: [0.87179487 0.96296296 0.73394495 0.90196078 0.94814815 0.67307692 0.45454545 0.95652174 0.65346535 0.75229358] mean value: 0.7908714762617265 key: test_precision value: [0.58333333 1. 0.8 0.7 0.8 1. 0.75 0.875 1. 1. ] mean value: 0.8508333333333333 key: train_precision value: [0.77272727 0.97014925 1. 0.82142857 0.96969697 1. 1. 0.94285714 1. 1. ] mean value: 0.9476859210441301 key: test_recall value: [0.875 0.5 0.57142857 1. 0.57142857 0.14285714 0.375 0.875 0.5 0.25 ] mean value: 0.5660714285714286 key: train_recall value: [1. 0.95588235 0.57971014 1. 0.92753623 0.50724638 0.29411765 0.97058824 0.48529412 0.60294118] mean value: 0.7323316283034953 key: test_accuracy value: [0.625 0.75 0.73333333 0.8 0.73333333 0.6 0.6 0.86666667 0.73333333 0.6 ] mean value: 0.7041666666666666 key: train_accuracy value: [0.85294118 0.96323529 0.78832117 0.89051095 0.94890511 0.75182482 0.64963504 0.95620438 0.74452555 0.80291971] mean value: 0.8349023185916703 key: test_roc_auc value: [0.625 0.75 0.72321429 0.8125 0.72321429 0.57142857 0.61607143 0.86607143 0.75 0.625 ] mean value: 0.70625 key: train_roc_auc value: [0.85294118 0.96323529 0.78985507 0.88970588 0.94906223 0.75362319 0.64705882 0.95630861 0.74264706 0.80147059] mean value: 0.8345907928388746 key: test_jcc value: [0.53846154 0.5 0.5 0.7 0.5 0.14285714 0.33333333 0.77777778 0.5 0.25 ] mean value: 0.47424297924297926 key: train_jcc value: [0.77272727 0.92857143 0.57971014 0.82142857 0.90140845 0.50724638 0.29411765 0.91666667 0.48529412 0.60294118] mean value: 0.6810111853013765 key: TN value: 64 mean value: 64.0 key: FP value: 33 mean value: 33.0 key: FN value: 12 mean value: 12.0 key: TP value: 43 mean value: 43.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.73 Accuracy on Blind test: 0.87 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01372719 0.01259232 0.01290774 0.01390266 0.01349616 0.01290226 0.01301146 0.01315951 0.01388788 0.01293182] mean value: 0.013251900672912598 key: score_time value: [0.01121998 0.01127958 0.01127815 0.01130819 0.01134586 0.01132011 0.01129007 0.01129627 0.01130867 0.01120424] mean value: 0.011285114288330077 key: test_mcc value: [0.77459667 0.62994079 0.21821789 0.73214286 0.41931393 0.41931393 0.49099025 0.60714286 0.73214286 1. ] mean value: 0.6023802041663276 key: train_mcc value: [0.94158382 0.78357455 0.82788248 0.91392776 0.75258453 0.78803902 0.70450233 0.8978896 0.88920184 0.79573573] mean value: 0.8294921649284455 key: test_fscore value: [0.85714286 0.8 0.625 0.85714286 0.44444444 0.44444444 0.71428571 0.8 0.875 1. ] mean value: 0.7417460317460318 key: train_fscore value: [0.97014925 0.8951049 0.91666667 0.95774648 0.84033613 0.88372093 0.79646018 0.94814815 0.9375 0.89932886] mean value: 0.9045161543262183 key: test_precision value: [1. 0.85714286 0.55555556 0.85714286 1. 1. 0.83333333 0.85714286 0.875 1. ] mean value: 0.883531746031746 key: train_precision value: [0.98484848 0.85333333 0.88 0.93150685 1. 0.95 1. 0.95522388 1. 0.82716049] mean value: 0.9382073041921062 key: test_recall value: [0.75 0.75 0.71428571 0.85714286 0.28571429 0.28571429 0.625 0.75 0.875 1. ] mean value: 0.6892857142857143 key: train_recall value: [0.95588235 0.94117647 0.95652174 0.98550725 0.72463768 0.82608696 0.66176471 0.94117647 0.88235294 0.98529412] mean value: 0.8860400682011935 key: test_accuracy value: [0.875 0.8125 0.6 0.86666667 0.66666667 0.66666667 0.73333333 0.8 0.86666667 1. ] mean value: 0.78875 key: train_accuracy value: [0.97058824 0.88970588 0.91240876 0.95620438 0.86131387 0.89051095 0.83211679 0.94890511 0.94160584 0.89051095] mean value: 0.9093870759982826 key: test_roc_auc value: [0.875 0.8125 0.60714286 0.86607143 0.64285714 0.64285714 0.74107143 0.80357143 0.86607143 1. ] mean value: 0.7857142857142858 key: train_roc_auc value: [0.97058824 0.88970588 0.9120844 0.95598892 0.86231884 0.89098465 0.83088235 0.9488491 0.94117647 0.89119778] mean value: 0.9093776641091219 key: test_jcc value: [0.75 0.66666667 0.45454545 0.75 0.28571429 0.28571429 0.55555556 0.66666667 0.77777778 1. ] mean value: 0.6192640692640692 key: train_jcc value: [0.94202899 0.81012658 0.84615385 0.91891892 0.72463768 0.79166667 0.66176471 0.90140845 0.88235294 0.81707317] mean value: 0.8296131949179335 key: TN value: 67 mean value: 67.0 key: FP value: 23 mean value: 23.0 key: FN value: 9 mean value: 9.0 key: TP value: 53 mean value: 53.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.77 Accuracy on Blind test: 0.89 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.11206031 0.09339643 0.092906 0.09413314 0.09428453 0.0976553 0.09314227 0.09387136 0.09383416 0.0931735 ] mean value: 0.09584569931030273 key: score_time value: [0.01528907 0.0143764 0.01447558 0.01455736 0.02002788 0.01480746 0.01442051 0.014539 0.01443863 0.01441383] mean value: 0.015134572982788086 key: test_mcc value: [0.8819171 0.8819171 0.87287156 0.875 0.87287156 0.87287156 0.64465837 0.73214286 0.875 0.87287156] mean value: 0.8382121679515432 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.93333333 0.94117647 0.92307692 0.93333333 0.92307692 0.92307692 0.84210526 0.875 0.93333333 0.94117647] mean value: 0.9168688973565133 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.88888889 1. 0.875 1. 1. 0.72727273 0.875 1. 0.88888889] mean value: 0.9255050505050505 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.875 1. 0.85714286 1. 0.85714286 0.85714286 1. 0.875 0.875 1. ] mean value: 0.9196428571428571 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9375 0.9375 0.93333333 0.93333333 0.93333333 0.93333333 0.8 0.86666667 0.93333333 0.93333333] mean value: 0.9141666666666668 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9375 0.9375 0.92857143 0.9375 0.92857143 0.92857143 0.78571429 0.86607143 0.9375 0.92857143] mean value: 0.9116071428571428 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.875 0.88888889 0.85714286 0.875 0.85714286 0.85714286 0.72727273 0.77777778 0.875 0.88888889] mean value: 0.8479256854256855 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 69 mean value: 69.0 key: FP value: 6 mean value: 6.0 key: FN value: 7 mean value: 7.0 key: TP value: 70 mean value: 70.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.67 Accuracy on Blind test: 0.84 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.02656746 0.02978373 0.03765631 0.03283286 0.04684496 0.04813409 0.03314996 0.04111743 0.0357182 0.03724337] mean value: 0.03690483570098877 key: score_time value: [0.01678157 0.01610088 0.02883625 0.01758456 0.03211522 0.01859903 0.01993012 0.02483487 0.02393889 0.02293921] mean value: 0.022166061401367187 key: test_mcc value: [0.77459667 0.8819171 0.875 0.47245559 0.64465837 0.87287156 0.75592895 0.60714286 1. 0.87287156] mean value: 0.7757442660460769 key: train_mcc value: [0.98540068 0.98540068 1. 0.98550418 0.98550725 1. 1. 0.98550725 1. 1. ] mean value: 0.992732002454171 key: test_fscore value: [0.85714286 0.94117647 0.93333333 0.66666667 0.72727273 0.92307692 0.88888889 0.8 1. 0.94117647] mean value: 0.8678734337557866 key: train_fscore value: [0.99259259 0.99259259 1. 0.99280576 0.99270073 1. 1. 0.99270073 1. 1. ] mean value: 0.9963392400434884 key: test_precision value: [1. 0.88888889 0.875 0.8 1. 1. 0.8 0.85714286 1. 0.88888889] mean value: 0.9109920634920636 key: train_precision value: [1. 1. 1. 0.98571429 1. 1. 1. 0.98550725 1. 1. ] mean value: 0.9971221532091097 key: test_recall value: [0.75 1. 1. 0.57142857 0.57142857 0.85714286 1. 0.75 1. 1. ] mean value: 0.85 key: train_recall value: [0.98529412 0.98529412 1. 1. 0.98550725 1. 1. 1. 1. 1. ] mean value: 0.9956095481670928 key: test_accuracy value: [0.875 0.9375 0.93333333 0.73333333 0.8 0.93333333 0.86666667 0.8 1. 0.93333333] mean value: 0.88125 key: train_accuracy value: [0.99264706 0.99264706 1. 0.99270073 0.99270073 1. 1. 0.99270073 1. 1. ] mean value: 0.9963396307428081 key: test_roc_auc value: [0.875 0.9375 0.9375 0.72321429 0.78571429 0.92857143 0.85714286 0.80357143 1. 0.92857143] mean value: 0.8776785714285713 key: train_roc_auc value: [0.99264706 0.99264706 1. 0.99264706 0.99275362 1. 1. 0.99275362 1. 1. ] mean value: 0.99634484228474 key: test_jcc value: [0.75 0.88888889 0.875 0.5 0.57142857 0.85714286 0.8 0.66666667 1. 0.88888889] mean value: 0.7798015873015872 key: train_jcc value: [0.98529412 0.98529412 1. 0.98571429 0.98550725 1. 1. 0.98550725 1. 1. ] mean value: 0.9927317013762027 key: TN value: 69 mean value: 69.0 key: FP value: 11 mean value: 11.0 key: FN value: 7 mean value: 7.0 key: TP value: 65 mean value: 65.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.79 Accuracy on Blind test: 0.9 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.03322458 0.04529357 0.0201118 0.01896811 0.01995182 0.03240466 0.04606581 0.04578567 0.04500175 0.04564452] mean value: 0.035245227813720706 key: score_time value: [0.02058101 0.02035928 0.01196337 0.01195431 0.0119977 0.02226043 0.020432 0.02399397 0.02383852 0.02329302] mean value: 0.01906735897064209 key: test_mcc value: [ 0.37796447 0.40451992 0.21821789 0.47245559 -0.09449112 0.47245559 0.19642857 0.21821789 0.49099025 0.73214286] mean value: 0.348890191683233 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.61538462 0.625 0.66666667 0.33333333 0.66666667 0.625 0.57142857 0.71428571 0.875 ] mean value: 0.6359432234432234 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.71428571 0.8 0.55555556 0.8 0.4 0.8 0.625 0.66666667 0.83333333 0.875 ] mean value: 0.7069841269841269 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.625 0.5 0.71428571 0.57142857 0.28571429 0.57142857 0.625 0.5 0.625 0.875 ] mean value: 0.5892857142857142 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6875 0.6875 0.6 0.73333333 0.46666667 0.73333333 0.6 0.6 0.73333333 0.86666667] mean value: 0.6708333333333332 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.6875 0.6875 0.60714286 0.72321429 0.45535714 0.72321429 0.59821429 0.60714286 0.74107143 0.86607143] mean value: 0.6696428571428571 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.44444444 0.45454545 0.5 0.2 0.5 0.45454545 0.4 0.55555556 0.77777778] mean value: 0.47868686868686866 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 57 mean value: 57.0 key: FP value: 31 mean value: 31.0 key: FN value: 19 mean value: 19.0 key: TP value: 45 mean value: 45.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.09 Accuracy on Blind test: 0.56 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.28226662 0.25447321 0.25262737 0.25650167 0.25346684 0.25236917 0.25692153 0.25983357 0.2588613 0.25281692] mean value: 0.25801382064819334 key: score_time value: [0.00900578 0.0091064 0.00908732 0.00908399 0.00883031 0.00888848 0.00898743 0.00883675 0.00886703 0.00891113] mean value: 0.008960461616516114 key: test_mcc value: [0.8819171 0.8819171 0.875 0.6000992 0.75592895 0.64465837 0.75592895 0.73214286 1. 0.87287156] mean value: 0.8000464086869412 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.93333333 0.94117647 0.93333333 0.76923077 0.83333333 0.72727273 0.88888889 0.875 1. 0.94117647] mean value: 0.8842745326568856 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.88888889 0.875 0.83333333 1. 1. 0.8 0.875 1. 0.88888889] mean value: 0.9161111111111111 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.875 1. 1. 0.71428571 0.71428571 0.57142857 1. 0.875 1. 1. ] mean value: 0.875 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9375 0.9375 0.93333333 0.8 0.86666667 0.8 0.86666667 0.86666667 1. 0.93333333] mean value: 0.8941666666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9375 0.9375 0.9375 0.79464286 0.85714286 0.78571429 0.85714286 0.86607143 1. 0.92857143] mean value: 0.8901785714285715 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.875 0.88888889 0.875 0.625 0.71428571 0.57142857 0.8 0.77777778 1. 0.88888889] mean value: 0.8016269841269841 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 69 mean value: 69.0 key: FP value: 9 mean value: 9.0 key: FN value: 7 mean value: 7.0 key: TP value: 67 mean value: 67.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.79 Accuracy on Blind test: 0.9 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis()/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01454568 0.02235794 0.01665139 0.0166266 0.01675177 0.01677036 0.01663136 0.01952362 0.01697612 0.01721311] mean value: 0.017404794692993164 key: score_time value: [0.01141596 0.01199937 0.01192641 0.01170635 0.01309991 0.01283741 0.01319933 0.01196384 0.01391196 0.01398873] mean value: 0.012604928016662598 key: test_mcc value: [0.77459667 0.77459667 0.56407607 0.66143783 0.66143783 0.76376262 0.41931393 0.75592895 0.87287156 0.64465837] mean value: 0.6892680497530497 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.88888889 0.88888889 0.77777778 0.82352941 0.82352941 0.875 0.76190476 0.88888889 0.94117647 0.84210526] mean value: 0.851168976362475 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.8 0.63636364 0.7 0.7 0.77777778 0.61538462 0.8 0.88888889 0.72727273] mean value: 0.7445687645687646 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.875 0.875 0.73333333 0.8 0.8 0.86666667 0.66666667 0.86666667 0.93333333 0.8 ] mean value: 0.8216666666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.875 0.875 0.75 0.8125 0.8125 0.875 0.64285714 0.85714286 0.92857143 0.78571429] mean value: 0.8214285714285715 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.8 0.8 0.63636364 0.7 0.7 0.77777778 0.61538462 0.8 0.88888889 0.72727273] mean value: 0.7445687645687646 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 49 mean value: 49.0 key: FP value: 0 mean value: 0.0 key: FN value: 27 mean value: 27.0 key: TP value: 76 mean value: 76.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.61 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02903247 0.0127809 0.01289296 0.01284266 0.01288414 0.03248739 0.04138494 0.03767085 0.04280519 0.04593873] mean value: 0.028072023391723634 key: score_time value: [0.02271175 0.01150751 0.01150703 0.01153731 0.01144195 0.01955628 0.02092075 0.02405858 0.02117944 0.0240984 ] mean value: 0.017851901054382325 key: test_mcc value: [0.62994079 0.67419986 0.73214286 0.73214286 0.46428571 0.64465837 0.64465837 0.87287156 0.875 0.76376262] mean value: 0.7033662998593934 key: train_mcc value: [0.95681396 0.94158382 0.95629932 0.97122151 0.95629932 0.95630861 0.92791659 0.94160273 0.94160273 0.95629932] mean value: 0.950594790135199 key: test_fscore value: [0.8 0.76923077 0.85714286 0.85714286 0.71428571 0.72727273 0.84210526 0.94117647 0.93333333 0.85714286] mean value: 0.8298832849297246 key: train_fscore value: [0.97841727 0.97014925 0.97841727 0.98529412 0.97841727 0.97810219 0.96402878 0.97058824 0.97058824 0.97777778] mean value: 0.9751780385065005 key: test_precision value: [0.85714286 1. 0.85714286 0.85714286 0.71428571 1. 0.72727273 0.88888889 1. 1. ] mean value: 0.8901875901875901 key: train_precision value: [0.95774648 0.98484848 0.97142857 1. 0.97142857 0.98529412 0.94366197 0.97058824 0.97058824 0.98507463] mean value: 0.9740659293510818 key: test_recall value: [0.75 0.625 0.85714286 0.85714286 0.71428571 0.57142857 1. 1. 0.875 0.75 ] mean value: 0.8 key: train_recall value: [1. 0.95588235 0.98550725 0.97101449 0.98550725 0.97101449 0.98529412 0.97058824 0.97058824 0.97058824] mean value: 0.9765984654731458 key: test_accuracy value: [0.8125 0.8125 0.86666667 0.86666667 0.73333333 0.8 0.8 0.93333333 0.93333333 0.86666667] mean value: 0.8425 key: train_accuracy value: [0.97794118 0.97058824 0.97810219 0.98540146 0.97810219 0.97810219 0.96350365 0.97080292 0.97080292 0.97810219] mean value: 0.9751449119793903 key: test_roc_auc value: [0.8125 0.8125 0.86607143 0.86607143 0.73214286 0.78571429 0.78571429 0.92857143 0.9375 0.875 ] mean value: 0.8401785714285716 key: train_roc_auc value: [0.97794118 0.97058824 0.97804774 0.98550725 0.97804774 0.97815431 0.96366155 0.97080136 0.97080136 0.97804774] mean value: 0.9751598465473146 key: test_jcc value: [0.66666667 0.625 0.75 0.75 0.55555556 0.57142857 0.72727273 0.88888889 0.875 0.75 ] mean value: 0.715981240981241 key: train_jcc value: [0.95774648 0.94202899 0.95774648 0.97101449 0.95774648 0.95714286 0.93055556 0.94285714 0.94285714 0.95652174] mean value: 0.9516217352423721 key: TN value: 67 mean value: 67.0 key: FP value: 15 mean value: 15.0 key: FN value: 9 mean value: 9.0 key: TP value: 61 mean value: 61.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.68 Accuracy on Blind test: 0.84 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:432: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:433: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_CV['Resampling'] = rs_rouC /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:438: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:439: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_BT['Resampling'] = rs_rouC Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=166)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.1613934 0.19614768 0.19616151 0.1958096 0.19677949 0.1965363 0.1959517 0.19425201 0.28738642 0.32958293] mean value: 0.21500010490417482 key: score_time value: [0.02007866 0.02105141 0.02255297 0.023417 0.02007699 0.02118158 0.02282619 0.02111578 0.02351999 0.02216053] mean value: 0.021798110008239745 key: test_mcc value: [0.62994079 0.67419986 0.73214286 0.73214286 0.6000992 0.75592895 0.64465837 0.87287156 0.875 0.49099025] mean value: 0.7007974694460358 key: train_mcc value: [0.98540068 0.94158382 0.97080136 0.97122151 0.98550725 0.98550725 0.92791659 0.98550418 0.98550418 0.97120941] mean value: 0.971015622159533 key: test_fscore value: [0.8 0.76923077 0.85714286 0.85714286 0.76923077 0.83333333 0.84210526 0.94117647 0.93333333 0.71428571] mean value: 0.8316981367445763 key: train_fscore value: [0.99270073 0.97014925 0.98550725 0.98529412 0.99270073 0.99270073 0.96402878 0.99259259 0.99259259 0.98507463] mean value: 0.9853341396565509 key: test_precision value: [0.85714286 1. 0.85714286 0.85714286 0.83333333 1. 0.72727273 0.88888889 1. 0.83333333] mean value: 0.8854256854256854 key: train_precision value: [0.98550725 0.98484848 0.98550725 1. 1. 1. 0.94366197 1. 1. 1. ] mean value: 0.9899524949433094 key: test_recall value: [0.75 0.625 0.85714286 0.85714286 0.71428571 0.71428571 1. 1. 0.875 0.625 ] mean value: 0.8017857142857142 key: train_recall value: [1. 0.95588235 0.98550725 0.97101449 0.98550725 0.98550725 0.98529412 0.98529412 0.98529412 0.97058824] mean value: 0.9809889173060528 key: test_accuracy value: [0.8125 0.8125 0.86666667 0.86666667 0.8 0.86666667 0.8 0.93333333 0.93333333 0.73333333] mean value: 0.8425 key: train_accuracy value: [0.99264706 0.97058824 0.98540146 0.98540146 0.99270073 0.99270073 0.96350365 0.99270073 0.99270073 0.98540146] mean value: 0.9853746243022756 key: test_roc_auc value: [0.8125 0.8125 0.86607143 0.86607143 0.79464286 0.85714286 0.78571429 0.92857143 0.9375 0.74107143] mean value: 0.8401785714285716 key: train_roc_auc value: [0.99264706 0.97058824 0.98540068 0.98550725 0.99275362 0.99275362 0.96366155 0.99264706 0.99264706 0.98529412] mean value: 0.9853900255754475 key: test_jcc value: [0.66666667 0.625 0.75 0.75 0.625 0.71428571 0.72727273 0.88888889 0.875 0.55555556] mean value: 0.7177669552669552 key: train_jcc value: [0.98550725 0.94202899 0.97142857 0.97101449 0.98550725 0.98550725 0.93055556 0.98529412 0.98529412 0.97058824] mean value: 0.9712725814963667 key: TN value: 67 mean value: 67.0 key: FP value: 15 mean value: 15.0 key: FN value: 9 mean value: 9.0 key: TP value: 61 mean value: 61.0 key: trainingY_neg value: 76 mean value: 76.0 key: trainingY_pos value: 76 mean value: 76.0 key: blindY_neg value: 24 mean value: 24.0 key: blindY_pos value: 38 mean value: 38.0 MCC on Blind test: 0.65 Accuracy on Blind test: 0.82 PASS: sorting df by score that is mapped onto the order I want Concatenating dfs with different resampling methods [WF]: 70/30 No. of dfs combining: 10 The sampling methods are: Resampling Logistic Regression none Logistic Regression smnc Logistic Regression ros Logistic Regression rus Logistic Regression rouC PASS: 10 dfs successfully combined nrows in combined_df_wf: 240 ncols in combined_df_wf: 9 Concatenating dfs with different resampling methods: 70/30 No. of dfs combining: 5 The sampling methods are: Resampling training_size 0 none 123 24 smnc 152 48 ros 152 72 rus 94 96 rouC 152 PASS: 5 dfs successfully combined nrows in combined_df: 120 ncols in combined_df: 32 File successfully written: /home/tanu/git/Data/pyrazinamide/output/ml/tts_7030/pnca_baselineC_7030.csv File successfully written: /home/tanu/git/Data/pyrazinamide/output/ml/tts_7030/pnca_baselineC_ext_7030.csv