/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_7030.py:464: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( 1.22.4 1.4.1 aaindex_df contains non-numerical data Total no. of non-numerial columns: 2 Selecting numerical data only PASS: successfully selected numerical columns only for aaindex_df Now checking for NA in the remaining aaindex_cols Counting aaindex_df cols with NA ncols with NA: 4 columns Dropping these... Original ncols: 127 Revised df ncols: 123 Checking NA in revised df... PASS: cols with NA successfully dropped from aaindex_df Proceeding with combining aa_df with other features_df PASS: ncols match Expected ncols: 123 Got: 123 Total no. of columns in clean aa_df: 123 Proceeding to merge, expected nrows in merged_df: 817 PASS: my_features_df and aa_df successfully combined nrows: 817 ncols: 269 count of NULL values before imputation or_mychisq 244 log10_or_mychisq 244 dtype: int64 count of NULL values AFTER imputation mutationinformation 0 or_rawI 0 logorI 0 dtype: int64 PASS: OR values imputed, data ready for ML Total no. of features for aaindex: 123 PASS: x_features has no target variable No. of columns for x_features: 174 PASS: ML data with input features, training and test generated... Total no. of input features: 174 --------No. of numerical features: 168 --------No. of categorical features: 6 Total no. of evolutionary features: 3 Total no. of stability features: 28 --------Common stabilty cols: 5 --------Foldx cols: 23 Total no. of affinity features: 5 --------Common affinity cols: 3 --------Gene specific affinity cols: 2 Total no. of residue level features: 132 --------AA index cols: 123 --------Residue Prop cols: 3 --------AA change Prop cols: 6 Total no. of genomic features: 6 --------MAF+OR cols: 2 --------Lineage cols: 4 --------Other cols: 0 ------------------------------------------------------------- Successfully split data: ALL features actual values: training set imputed values: blind test set Total data size: 467 Train data size: (312, 174) y_train numbers: Counter({1: 206, 0: 106}) Test data size: (155, 174) y_test_numbers: Counter({1: 103, 0: 52}) y_train ratio: 0.5145631067961165 y_test ratio: 0.5048543689320388 ------------------------------------------------------------- index: 0 ind: 1 Mask count check: True index: 1 ind: 2 Mask count check: True Original Data Counter({1: 206, 0: 106}) Data dim: (312, 174) Simple Random OverSampling Counter({1: 206, 0: 206}) (412, 174) Simple Random UnderSampling Counter({0: 106, 1: 106}) (212, 174) Simple Combined Over and UnderSampling Counter({0: 206, 1: 206}) (412, 174) SMOTE_NC OverSampling Counter({1: 206, 0: 206}) (412, 174) ##################################################################### Running ML analysis: feature groups Gene name: katG Drug name: isoniazid Output directory: /home/tanu/git/Data/isoniazid/output/ml/tts_7030/ ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( key: fit_time value: [0.03077984 0.03297734 0.03392029 0.03392172 0.03496599 0.03346992 0.03359389 0.03486037 0.03373337 0.03370595] mean value: 0.03359286785125733 key: score_time value: [0.0124476 0.01199484 0.01336312 0.01245666 0.0124948 0.01370454 0.01242924 0.01259065 0.012465 0.01205707] mean value: 0.012600350379943847 key: test_mcc value: [0.79772404 0.78959188 0.77484502 0.77484502 0.77484502 0.70992957 0.78625916 0.64203411 0.79524277 0.79524277] mean value: 0.7640559345359147 key: train_mcc value: [0.86371491 0.89599932 0.8564766 0.87250517 0.88150779 0.86449998 0.87995543 0.85567237 0.85594873 0.90448686] mean value: 0.8730767160189149 key: test_fscore value: [0.93333333 0.93023256 0.93023256 0.93023256 0.93023256 0.91304348 0.92682927 0.87804878 0.93023256 0.93023256] mean value: 0.9232650209211899 key: train_fscore value: [0.95514512 0.96551724 0.95238095 0.95744681 0.96062992 0.95466667 0.96042216 0.95263158 0.95287958 0.96842105] mean value: 0.9580141085250089 key: test_precision value: [0.875 0.90909091 0.90909091 0.90909091 0.90909091 0.84 0.9047619 0.85714286 0.86956522 0.86956522] mean value: 0.8852398833051007 key: train_precision value: [0.93298969 0.94791667 0.93264249 0.94240838 0.93367347 0.94210526 0.94300518 0.93298969 0.92857143 0.94845361] mean value: 0.93847558628316 key: test_recall value: [1. 0.95238095 0.95238095 0.95238095 0.95238095 1. 0.95 0.9 1. 1. ] mean value: 0.9659523809523808 key: train_recall value: [0.97837838 0.98378378 0.97297297 0.97297297 0.98918919 0.96756757 0.97849462 0.97311828 0.97849462 0.98924731] mean value: 0.9784219703574543 key: test_accuracy value: [0.90625 0.90625 0.90322581 0.90322581 0.90322581 0.87096774 0.90322581 0.83870968 0.90322581 0.90322581] mean value: 0.8941532258064514 key: train_accuracy value: [0.93928571 0.95357143 0.93594306 0.9430605 0.94661922 0.93950178 0.94661922 0.93594306 0.93594306 0.95729537] mean value: 0.9433782409761058 key: test_roc_auc value: [0.86363636 0.88528139 0.87619048 0.87619048 0.87619048 0.8 0.88409091 0.81363636 0.86363636 0.86363636] mean value: 0.8602489177489178 key: train_roc_auc value: [0.92076814 0.93926031 0.91877815 0.92919482 0.92688626 0.92649212 0.93135257 0.91813809 0.9155631 0.94199208] mean value: 0.9268425641260956 key: test_jcc value: [0.875 0.86956522 0.86956522 0.86956522 0.86956522 0.84 0.86363636 0.7826087 0.86956522 0.86956522] mean value: 0.8578636363636363 key: train_jcc value: [0.91414141 0.93333333 0.90909091 0.91836735 0.92424242 0.91326531 0.92385787 0.90954774 0.91 0.93877551] mean value: 0.9194621850787158 key: TN value: 80 mean value: 80.0 key: FP value: 7 mean value: 7.0 key: FN value: 26 mean value: 26.0 key: TP value: 199 mean value: 199.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.79 Accuracy on Blind test: 0.91 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.70210862 0.69636774 0.83434844 0.7121985 0.71469593 0.83674455 0.72348166 0.69391394 0.79157877 0.70643449] mean value: 0.7411872625350953 key: score_time value: [0.01284766 0.01272511 0.01273108 0.01270127 0.0127151 0.01370049 0.0127027 0.01273274 0.01376271 0.01266599] mean value: 0.012928485870361328 key: test_mcc value: [1. 0.93154098 0.86831345 0.69695062 1. 0.78625916 0.65635466 0.73603286 0.78625916 0.86243936] mean value: 0.8324150242970061 key: train_mcc value: [0.98411246 1. 1. 0.98422269 0.99210029 1. 1. 1. 1. 0.98414076] mean value: 0.9944576204129685 key: test_fscore value: [1. 0.97674419 0.95 0.90909091 1. 0.92682927 0.87179487 0.89473684 0.92682927 0.95238095] mean value: 0.9408406298003875 key: train_fscore value: [0.99462366 1. 1. 0.99462366 0.99730458 1. 1. 1. 1. 0.99465241] mean value: 0.9981204300455312 key: test_precision value: [1. 0.95454545 1. 0.86956522 1. 0.95 0.89473684 0.94444444 0.9047619 0.90909091] mean value: 0.9427144772339279 key: train_precision value: [0.98930481 1. 1. 0.98930481 0.99462366 1. 1. 1. 1. 0.9893617 ] mean value: 0.9962594983710087 key: test_recall value: [1. 1. 0.9047619 0.95238095 1. 0.9047619 0.85 0.85 0.95 1. ] mean value: 0.9411904761904761 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.96875 0.93548387 0.87096774 1. 0.90322581 0.83870968 0.87096774 0.90322581 0.93548387] mean value: 0.9226814516129032 key: train_accuracy value: [0.99285714 1. 1. 0.99288256 0.99644128 1. 1. 1. 1. 0.99288256] mean value: 0.9975063548551093 key: test_roc_auc value: [1. 0.95454545 0.95238095 0.82619048 1. 0.90238095 0.83409091 0.87954545 0.88409091 0.90909091] mean value: 0.9142316017316017 key: train_roc_auc value: [0.98947368 1. 1. 0.98958333 0.99479167 1. 1. 1. 1. 0.98947368] mean value: 0.9963322368421054 key: test_jcc value: [1. 0.95454545 0.9047619 0.83333333 1. 0.86363636 0.77272727 0.80952381 0.86363636 0.90909091] mean value: 0.8911255411255411 key: train_jcc value: [0.98930481 1. 1. 0.98930481 0.99462366 1. 1. 1. 1. 0.9893617 ] mean value: 0.9962594983710087 key: TN value: 92 mean value: 92.0 key: FP value: 12 mean value: 12.0 key: FN value: 14 mean value: 14.0 key: TP value: 194 mean value: 194.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.86 Accuracy on Blind test: 0.94 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.0132184 0.01286697 0.00963426 0.00930786 0.00908041 0.00924706 0.00901842 0.00894332 0.00898361 0.00899673] mean value: 0.009929704666137695 key: score_time value: [0.01198387 0.01038861 0.00893116 0.00865841 0.00870872 0.00867224 0.00869894 0.00875521 0.00867367 0.00875235] mean value: 0.009222316741943359 key: test_mcc value: [0.47306844 0.39072951 0.69695062 0.20935895 0.7047619 0.36059915 0.29545455 0.27532188 0.48992888 0.40572206] mean value: 0.4301895937784511 key: train_mcc value: [0.41534408 0.48082512 0.4751815 0.49567937 0.41755413 0.46123131 0.46864391 0.47740752 0.47255597 0.47217054] mean value: 0.46365934504124107 key: test_fscore value: [0.8 0.7804878 0.90909091 0.73170732 0.9047619 0.82608696 0.75 0.7027027 0.84444444 0.81818182] mean value: 0.8067463857654736 key: train_fscore value: [0.80952381 0.79300292 0.82849604 0.832 0.79891304 0.82228117 0.81940701 0.81643836 0.82352941 0.82939633] mean value: 0.8172988079253738 key: test_precision value: [0.84210526 0.8 0.86956522 0.75 0.9047619 0.76 0.75 0.76470588 0.76 0.75 ] mean value: 0.7951138267664045 key: train_precision value: [0.79274611 0.86075949 0.80927835 0.82105263 0.80327869 0.80729167 0.82162162 0.83240223 0.81914894 0.81025641] mean value: 0.8177836147631308 key: test_recall value: [0.76190476 0.76190476 0.95238095 0.71428571 0.9047619 0.9047619 0.75 0.65 0.95 0.9 ] mean value: 0.825 key: train_recall value: [0.82702703 0.73513514 0.84864865 0.84324324 0.79459459 0.83783784 0.8172043 0.80107527 0.82795699 0.84946237] mean value: 0.8182185411217671 key: test_accuracy value: [0.75 0.71875 0.87096774 0.64516129 0.87096774 0.74193548 0.67741935 0.64516129 0.77419355 0.74193548] mean value: 0.7436491935483871 key: train_accuracy value: [0.74285714 0.74642857 0.76868327 0.77580071 0.7366548 0.76156584 0.76156584 0.76156584 0.76512456 0.76868327] mean value: 0.7588929842399593 key: test_roc_auc value: [0.74458874 0.6991342 0.82619048 0.60714286 0.85238095 0.65238095 0.64772727 0.64318182 0.70227273 0.67727273] mean value: 0.7052272727272728 key: train_roc_auc value: [0.7029872 0.75177809 0.73161599 0.74453829 0.7097973 0.72621059 0.73491794 0.7426429 0.73503113 0.72999434] mean value: 0.7309513758240413 key: test_jcc value: [0.66666667 0.64 0.83333333 0.57692308 0.82608696 0.7037037 0.6 0.54166667 0.73076923 0.69230769] mean value: 0.681145732689211 key: train_jcc value: [0.68 0.65700483 0.70720721 0.71232877 0.66515837 0.6981982 0.69406393 0.68981481 0.7 0.70852018] mean value: 0.6912296295614943 key: TN value: 62 mean value: 62.0 key: FP value: 36 mean value: 36.0 key: FN value: 44 mean value: 44.0 key: TP value: 170 mean value: 170.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.33 Accuracy on Blind test: 0.7 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00950885 0.00936079 0.01019502 0.00956726 0.01026011 0.00957608 0.00969887 0.00971508 0.00942612 0.01114821] mean value: 0.009845638275146484 key: score_time value: [0.00886631 0.00886059 0.00883245 0.00917625 0.00903559 0.00905657 0.00917363 0.00900912 0.00908494 0.01070547] mean value: 0.009180092811584472 key: test_mcc value: [ 0.4133805 0.15803489 0.01471225 0.35192842 0.45253757 0.09967105 -0.07802347 0.14863011 0.22469871 0.14863011] mean value: 0.19342001287335014 key: train_mcc value: [0.38595876 0.40659146 0.40169892 0.42341022 0.35612129 0.33100274 0.36505417 0.35193106 0.33525911 0.35908657] mean value: 0.3716114301409626 key: test_fscore value: [0.83333333 0.76595745 0.69767442 0.83333333 0.84444444 0.8 0.68181818 0.75555556 0.7826087 0.75555556] mean value: 0.775028096510574 key: train_fscore value: [0.83018868 0.82758621 0.82640587 0.82793017 0.80604534 0.81339713 0.82380952 0.81372549 0.81622912 0.81751825] mean value: 0.8202835777038959 key: test_precision value: [0.74074074 0.69230769 0.68181818 0.74074074 0.79166667 0.68965517 0.625 0.68 0.69230769 0.68 ] mean value: 0.7014236886995507 key: train_precision value: [0.73640167 0.760181 0.75446429 0.76851852 0.75471698 0.72961373 0.73931624 0.74774775 0.73390558 0.74666667] mean value: 0.7471532421515537 key: test_recall value: [0.95238095 0.85714286 0.71428571 0.95238095 0.9047619 0.95238095 0.75 0.85 0.9 0.85 ] mean value: 0.8683333333333334 key: train_recall value: [0.95135135 0.90810811 0.91351351 0.8972973 0.86486486 0.91891892 0.93010753 0.89247312 0.91935484 0.90322581] mean value: 0.9099215344376634 key: test_accuracy value: [0.75 0.65625 0.58064516 0.74193548 0.77419355 0.67741935 0.5483871 0.64516129 0.67741935 0.64516129] mean value: 0.6696572580645161 key: train_accuracy value: [0.74285714 0.75 0.74733096 0.7544484 0.72597865 0.72241993 0.7366548 0.72953737 0.72597865 0.73309609] mean value: 0.7368301982714794 key: test_roc_auc value: [0.65800866 0.56493506 0.50714286 0.62619048 0.70238095 0.52619048 0.46590909 0.56136364 0.58636364 0.56136364] mean value: 0.5759848484848484 key: train_roc_auc value: [0.64409673 0.67510669 0.67029842 0.68823198 0.6615991 0.63133446 0.64400113 0.65149972 0.63336163 0.6516129 ] mean value: 0.6551142759907616 key: test_jcc value: [0.71428571 0.62068966 0.53571429 0.71428571 0.73076923 0.66666667 0.51724138 0.60714286 0.64285714 0.60714286] mean value: 0.6356795503347227 key: train_jcc value: [0.70967742 0.70588235 0.70416667 0.70638298 0.67510549 0.68548387 0.70040486 0.68595041 0.68951613 0.69135802] mean value: 0.6953928199132248 key: TN value: 30 mean value: 30.0 key: FP value: 27 mean value: 27.0 key: FN value: 76 mean value: 76.0 key: TP value: 179 mean value: 179.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.31 Accuracy on Blind test: 0.7 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00896025 0.01062131 0.00921178 0.01032758 0.0118413 0.01038861 0.00920725 0.01012421 0.00997972 0.01029325] mean value: 0.010095524787902831 key: score_time value: [0.04918337 0.01206517 0.01528883 0.01191854 0.01781154 0.01341414 0.01296878 0.01386476 0.01706791 0.01706243] mean value: 0.018064546585083007 key: test_mcc value: [ 0.13261933 0.12434118 0.01471225 0.1667388 0.44786837 0.12245134 0.11978324 -0.02874798 0.41684569 0.17892962] mean value: 0.16955418600116953 key: train_mcc value: [0.48779683 0.58110662 0.51362016 0.50738455 0.48727637 0.52197598 0.46868404 0.52014954 0.50198868 0.52763661] mean value: 0.5117619381388325 key: test_fscore value: [0.72727273 0.7755102 0.69767442 0.74418605 0.85106383 0.75555556 0.71428571 0.66666667 0.80952381 0.74418605] mean value: 0.7485925018801247 key: train_fscore value: [0.84020619 0.86821705 0.85063291 0.84615385 0.84343434 0.85353535 0.83756345 0.85279188 0.84848485 0.85642317] mean value: 0.8497443046584138 key: test_precision value: [0.69565217 0.67857143 0.68181818 0.72727273 0.76923077 0.70833333 0.68181818 0.63636364 0.77272727 0.69565217] mean value: 0.7047439878961617 key: train_precision value: [0.80295567 0.83168317 0.8 0.80487805 0.79146919 0.80094787 0.79326923 0.80769231 0.8 0.8056872 ] mean value: 0.8038582685986333 key: test_recall value: [0.76190476 0.9047619 0.71428571 0.76190476 0.95238095 0.80952381 0.75 0.7 0.85 0.8 ] mean value: 0.8004761904761905 key: train_recall value: [0.88108108 0.90810811 0.90810811 0.89189189 0.9027027 0.91351351 0.88709677 0.90322581 0.90322581 0.91397849] mean value: 0.9012932287125835 key: test_accuracy value: [0.625 0.65625 0.58064516 0.64516129 0.77419355 0.64516129 0.61290323 0.5483871 0.74193548 0.64516129] mean value: 0.6474798387096775 key: train_accuracy value: [0.77857143 0.81785714 0.79003559 0.78647687 0.77935943 0.79359431 0.77224199 0.79359431 0.78647687 0.79715302] mean value: 0.7895360955770209 key: test_roc_auc value: [0.56277056 0.54329004 0.50714286 0.58095238 0.67619048 0.5547619 0.55681818 0.48636364 0.69772727 0.58181818] mean value: 0.5747835497835497 key: train_roc_auc value: [0.73001422 0.77510669 0.73530405 0.73761261 0.72218468 0.73800676 0.7172326 0.74108659 0.73056027 0.74119977] mean value: 0.7368308248826076 key: test_jcc value: [0.57142857 0.63333333 0.53571429 0.59259259 0.74074074 0.60714286 0.55555556 0.5 0.68 0.59259259] mean value: 0.6009100529100528 key: train_jcc value: [0.72444444 0.76712329 0.74008811 0.73333333 0.72925764 0.74449339 0.72052402 0.74336283 0.73684211 0.74889868] mean value: 0.7388367838170675 key: TN value: 37 mean value: 37.0 key: FP value: 41 mean value: 41.0 key: FN value: 69 mean value: 69.0 key: TP value: 165 mean value: 165.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.29 Accuracy on Blind test: 0.7 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.01540208 0.01376987 0.01407671 0.01378703 0.0139339 0.01437092 0.01446509 0.01371384 0.01400924 0.01391745] mean value: 0.014144611358642579 key: score_time value: [0.01087761 0.0103426 0.0101378 0.01008773 0.01020432 0.01038265 0.01006603 0.01003528 0.01035857 0.01014781] mean value: 0.010264039039611816 key: test_mcc value: [0.57196944 0.49517597 0.55777335 0.47079191 0.53526436 0.26024784 0.4870862 0.31927949 0.44136741 0.56697057] mean value: 0.4705926550629849 key: train_mcc value: [0.61185172 0.67361652 0.61915671 0.63587521 0.61345251 0.62176784 0.60608952 0.6620156 0.60390614 0.58699634] mean value: 0.6234728084389831 key: test_fscore value: [0.86956522 0.85106383 0.875 0.8372093 0.86956522 0.80851064 0.8372093 0.8 0.83333333 0.86363636] mean value: 0.8445093204488575 key: train_fscore value: [0.88235294 0.89876543 0.88279302 0.88721805 0.8817734 0.88395062 0.88135593 0.8960396 0.88077859 0.87651332] mean value: 0.8851540894305963 key: test_precision value: [0.8 0.76923077 0.77777778 0.81818182 0.8 0.73076923 0.7826087 0.72 0.71428571 0.79166667] mean value: 0.7704520672564151 key: train_precision value: [0.80717489 0.82727273 0.81944444 0.8271028 0.80995475 0.81363636 0.80176211 0.83027523 0.80444444 0.79735683] mean value: 0.8138424594648971 key: test_recall value: [0.95238095 0.95238095 1. 0.85714286 0.95238095 0.9047619 0.9 0.9 1. 0.95 ] mean value: 0.9369047619047619 key: train_recall value: [0.97297297 0.98378378 0.95675676 0.95675676 0.96756757 0.96756757 0.97849462 0.97311828 0.97311828 0.97311828] mean value: 0.9703254867770996 key: test_accuracy value: [0.8125 0.78125 0.80645161 0.77419355 0.80645161 0.70967742 0.77419355 0.70967742 0.74193548 0.80645161] mean value: 0.7722782258064516 key: train_accuracy value: [0.82857143 0.85357143 0.83274021 0.83985765 0.82918149 0.83274021 0.82562278 0.85053381 0.82562278 0.81850534] mean value: 0.8336947127605491 key: test_roc_auc value: [0.74891775 0.7034632 0.7 0.72857143 0.72619048 0.60238095 0.72272727 0.63181818 0.63636364 0.74772727] mean value: 0.6948160173160174 key: train_roc_auc value: [0.7601707 0.79189189 0.77525338 0.78567005 0.76503378 0.77024212 0.75240521 0.7918223 0.75498019 0.74445388] mean value: 0.7691923486517078 key: test_jcc value: [0.76923077 0.74074074 0.77777778 0.72 0.76923077 0.67857143 0.72 0.66666667 0.71428571 0.76 ] mean value: 0.7316503866503867 key: train_jcc value: [0.78947368 0.8161435 0.79017857 0.7972973 0.78854626 0.7920354 0.78787879 0.81165919 0.78695652 0.78017241] mean value: 0.7940341620667072 key: TN value: 48 mean value: 48.0 key: FP value: 13 mean value: 13.0 key: FN value: 58 mean value: 58.0 key: TP value: 193 mean value: 193.0 key: trainingY_neg value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.55 Accuracy on Blind test: 0.81 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.16458178 1.28447604 1.16362858 1.27272463 1.20178652 1.13651323 1.22490501 1.13492298 1.25703454 1.13588333] mean value: 1.197645664215088 key: score_time value: [0.01479721 0.01402593 0.01684952 0.01273608 0.01289916 0.01262927 0.01277757 0.01286125 0.01281571 0.01280928] mean value: 0.013520097732543946 key: test_mcc value: [0.79772404 0.6457766 0.85238095 0.53924646 1. 0.61758068 0.71818182 0.65635466 0.93048421 0.79524277] mean value: 0.7552972189336914 key: train_mcc value: [0.99204533 0.99204533 1. 0.99210029 1. 0.99210029 1. 1. 1. 0.99205967] mean value: 0.9960350909981239 key: test_fscore value: [0.93333333 0.88888889 0.95238095 0.86363636 1. 0.88888889 0.9 0.87179487 0.97560976 0.93023256] mean value: 0.9204765613160395 key: train_fscore value: [0.99730458 0.99730458 1. 0.99730458 1. 0.99730458 1. 1. 1. 0.99731903] mean value: 0.9986537363693516 key: test_precision value: [0.875 0.83333333 0.95238095 0.82608696 1. 0.83333333 0.9 0.89473684 0.95238095 0.86956522] mean value: 0.8936817587446878 key: train_precision value: [0.99462366 0.99462366 1. 0.99462366 1. 0.99462366 1. 1. 1. 0.99465241] mean value: 0.9973147030073026 key: test_recall value: [1. 0.95238095 0.95238095 0.9047619 1. 0.95238095 0.9 0.85 1. 1. ] mean value: 0.9511904761904763 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.90625 0.84375 0.93548387 0.80645161 1. 0.83870968 0.87096774 0.83870968 0.96774194 0.90322581] mean value: 0.8911290322580644 key: train_accuracy value: [0.99642857 0.99642857 1. 0.99644128 1. 0.99644128 1. 1. 1. 0.99644128] mean value: 0.9982180986273512 key: test_roc_auc value: [0.86363636 0.79437229 0.92619048 0.75238095 1. 0.77619048 0.85909091 0.83409091 0.95454545 0.86363636] mean value: 0.8624134199134199 key: train_roc_auc value: [0.99473684 0.99473684 1. 0.99479167 1. 0.99479167 1. 1. 1. 0.99473684] mean value: 0.9973793859649123 key: test_jcc value: [0.875 0.8 0.90909091 0.76 1. 0.8 0.81818182 0.77272727 0.95238095 0.86956522] mean value: 0.8556946169772257 key: train_jcc value: [0.99462366 0.99462366 1. 0.99462366 1. 0.99462366 1. 1. 1. 0.99465241] mean value: 0.9973147030073026 key: TN value: 82 mean value: 82.0 key: FP value: 10 mean value: 10.0 key: FN value: 24 mean value: 24.0 key: TP value: 196 mean value: 196.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.77 Accuracy on Blind test: 0.9 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02068901 0.02152038 0.01389027 0.01603794 0.01353812 0.01289558 0.015836 0.01678038 0.01749015 0.01312494] mean value: 0.01618027687072754 key: score_time value: [0.01225066 0.00925589 0.00909591 0.00866771 0.00862551 0.00864029 0.00862956 0.00868511 0.00961447 0.00894046] mean value: 0.009240555763244628 key: test_mcc value: [0.87496729 0.93435318 0.93048421 0.78625916 1. 0.86831345 0.93048421 0.79476958 0.85909091 0.72821908] mean value: 0.8706941085318691 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95 0.97560976 0.97560976 0.92682927 1. 0.95 0.97560976 0.92307692 0.95 0.90909091] mean value: 0.9535826368753197 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 0.95 1. 1. 0.95238095 0.94736842 0.95 0.83333333] mean value: 0.9633082706766917 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.9047619 0.95238095 0.95238095 0.9047619 1. 0.9047619 1. 0.9 0.95 1. ] mean value: 0.9469047619047618 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9375 0.96875 0.96774194 0.90322581 1. 0.93548387 0.96774194 0.90322581 0.93548387 0.87096774] mean value: 0.9390120967741936 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95238095 0.97619048 0.97619048 0.90238095 1. 0.95238095 0.95454545 0.90454545 0.92954545 0.81818182] mean value: 0.936634199134199 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.9047619 0.95238095 0.95238095 0.86363636 1. 0.9047619 0.95238095 0.85714286 0.9047619 0.83333333] mean value: 0.9125541125541126 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 98 mean value: 98.0 key: FP value: 11 mean value: 11.0 key: FN value: 8 mean value: 8.0 key: TP value: 195 mean value: 195.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.87 Accuracy on Blind test: 0.94 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.10506797 0.1029923 0.10362291 0.10310459 0.10460377 0.10338831 0.10361099 0.10267019 0.105057 0.10298061] mean value: 0.10370986461639405 key: score_time value: [0.01752758 0.01787734 0.01751494 0.01744723 0.01748729 0.01749158 0.01751399 0.01741982 0.01745582 0.01747465] mean value: 0.017521023750305176 key: test_mcc value: [0.78959188 0.57163505 0.61758068 0.36059915 0.47079191 0.36059915 0.56537691 0.49780905 0.5913124 0.57727273] mean value: 0.5402568904601063 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.93023256 0.86363636 0.88888889 0.82608696 0.8372093 0.82608696 0.85714286 0.82926829 0.86956522 0.85 ] mean value: 0.8578117393250937 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.90909091 0.82608696 0.83333333 0.76 0.81818182 0.76 0.81818182 0.80952381 0.76923077 0.85 ] mean value: 0.8153629414064196 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95238095 0.9047619 0.95238095 0.9047619 0.85714286 0.9047619 0.9 0.85 1. 0.85 ] mean value: 0.9076190476190475 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.90625 0.8125 0.83870968 0.74193548 0.77419355 0.74193548 0.80645161 0.77419355 0.80645161 0.80645161] mean value: 0.8009072580645162 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.88528139 0.77056277 0.77619048 0.65238095 0.72857143 0.65238095 0.76818182 0.74318182 0.72727273 0.78863636] mean value: 0.7492640692640691 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.86956522 0.76 0.8 0.7037037 0.72 0.7037037 0.75 0.70833333 0.76923077 0.73913043] mean value: 0.7523667162145423 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 63 mean value: 63.0 key: FP value: 19 mean value: 19.0 key: FN value: 43 mean value: 43.0 key: TP value: 187 mean value: 187.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.53 Accuracy on Blind test: 0.79 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00934625 0.00909376 0.00920677 0.00956249 0.00934076 0.00916028 0.00926018 0.00925374 0.00932503 0.00914741] mean value: 0.00926966667175293 key: score_time value: [0.00877452 0.00852442 0.00861669 0.00886846 0.0085597 0.00864863 0.00862837 0.00879931 0.00861359 0.00859666] mean value: 0.008663034439086914 key: test_mcc value: [ 0.52663543 0.50569367 -0.20763488 -0.09157015 0.62281846 0.28749445 0.51793973 0.3261463 0.24110987 0.15454545] mean value: 0.2883178338157986 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.82926829 0.8372093 0.66666667 0.68181818 0.88372093 0.8 0.82051282 0.73684211 0.71794872 0.7 ] mean value: 0.7673987017450612 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85 0.81818182 0.625 0.65217391 0.86363636 0.75 0.84210526 0.77777778 0.73684211 0.7 ] mean value: 0.761571724106049 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.80952381 0.85714286 0.71428571 0.71428571 0.9047619 0.85714286 0.8 0.7 0.7 0.7 ] mean value: 0.7757142857142857 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.78125 0.78125 0.51612903 0.5483871 0.83870968 0.70967742 0.77419355 0.67741935 0.64516129 0.61290323] mean value: 0.6885080645161291 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.76839827 0.74675325 0.40714286 0.45714286 0.80238095 0.62857143 0.76363636 0.66818182 0.62272727 0.57727273] mean value: 0.6442207792207792 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.70833333 0.72 0.5 0.51724138 0.79166667 0.66666667 0.69565217 0.58333333 0.56 0.53846154] mean value: 0.6281355091684926 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 55 mean value: 55.0 key: FP value: 46 mean value: 46.0 key: FN value: 51 mean value: 51.0 key: TP value: 160 mean value: 160.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.29 Accuracy on Blind test: 0.68 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [1.42102885 1.41763186 1.43122268 1.39822125 1.40961742 1.39854026 1.42449594 1.49090695 1.44167662 1.44908071] mean value: 1.4282422542572022 key: score_time value: [0.094944 0.09802151 0.15033007 0.09156108 0.09057498 0.09051704 0.09883428 0.09939861 0.09202886 0.09227395] mean value: 0.09984843730926514 key: test_mcc value: [0.93154098 0.71797362 0.92687157 0.69695062 0.85238095 0.77484502 0.78625916 0.71390814 0.86243936 0.66057826] mean value: 0.7923747679070615 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97674419 0.90909091 0.97674419 0.90909091 0.95238095 0.93023256 0.92682927 0.9047619 0.95238095 0.88888889] mean value: 0.9327144715119757 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.95454545 0.86956522 0.95454545 0.86956522 0.95238095 0.90909091 0.9047619 0.86363636 0.90909091 0.8 ] mean value: 0.8987182382834555 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.95238095 1. 0.95238095 0.95238095 0.95238095 0.95 0.95 1. 1. ] mean value: 0.9709523809523809 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.96875 0.875 0.96774194 0.87096774 0.93548387 0.90322581 0.90322581 0.87096774 0.93548387 0.83870968] mean value: 0.9069556451612902 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95454545 0.83982684 0.95 0.82619048 0.92619048 0.87619048 0.88409091 0.83863636 0.90909091 0.77272727] mean value: 0.8777489177489176 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95454545 0.83333333 0.95454545 0.83333333 0.90909091 0.86956522 0.86363636 0.82608696 0.90909091 0.8 ] mean value: 0.8753227931488802 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 83 mean value: 83.0 key: FP value: 6 mean value: 6.0 key: FN value: 23 mean value: 23.0 key: TP value: 200 mean value: 200.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.84 Accuracy on Blind test: 0.93 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.89417028 0.92537498 0.97537518 0.91410828 0.87176323 0.92093062 0.91085744 0.92615676 0.87906098 0.91220188] mean value: 0.912999963760376 key: score_time value: [0.21468759 0.18967795 0.22059464 0.181077 0.24509573 0.19168997 0.2172718 0.18608809 0.19562244 0.18895364] mean value: 0.20307588577270508 key: test_mcc value: [0.8643122 0.6457766 0.78262379 0.61758068 0.77484502 0.61758068 0.78625916 0.71390814 0.66057826 0.66057826] mean value: 0.7124042779002867 key: train_mcc value: [0.95258202 0.94474539 0.94513672 0.95291644 0.93737406 0.94513672 0.95266247 0.96836384 0.95266247 0.95266247] mean value: 0.9504242616765979 key: test_fscore value: [0.95454545 0.88888889 0.93333333 0.88888889 0.93023256 0.88888889 0.92682927 0.9047619 0.88888889 0.88888889] mean value: 0.9094146963517356 key: train_fscore value: [0.98404255 0.98143236 0.98143236 0.98404255 0.97883598 0.98143236 0.98412698 0.9893617 0.98412698 0.98412698] mean value: 0.9832960821955685 key: test_precision value: [0.91304348 0.83333333 0.875 0.83333333 0.90909091 0.83333333 0.9047619 0.86363636 0.8 0.8 ] mean value: 0.8565532655750048 key: train_precision value: [0.96858639 0.96354167 0.96354167 0.96858639 0.95854922 0.96354167 0.96875 0.97894737 0.96875 0.96875 ] mean value: 0.9671544366088091 key: test_recall value: [1. 0.95238095 1. 0.95238095 0.95238095 0.95238095 0.95 0.95 1. 1. ] mean value: 0.9709523809523809 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9375 0.84375 0.90322581 0.83870968 0.90322581 0.83870968 0.90322581 0.87096774 0.83870968 0.83870968] mean value: 0.8716733870967742 key: train_accuracy value: [0.97857143 0.975 0.97508897 0.97864769 0.97153025 0.97508897 0.97864769 0.98576512 0.97864769 0.97864769] mean value: 0.9775635485510931 key: test_roc_auc value: [0.90909091 0.79437229 0.85 0.77619048 0.87619048 0.77619048 0.88409091 0.83863636 0.77272727 0.77272727] mean value: 0.8250216450216449 key: train_roc_auc value: [0.96842105 0.96315789 0.96354167 0.96875 0.95833333 0.96354167 0.96842105 0.97894737 0.96842105 0.96842105] mean value: 0.9669956140350877 key: test_jcc value: [0.91304348 0.8 0.875 0.8 0.86956522 0.8 0.86363636 0.82608696 0.8 0.8 ] mean value: 0.8347332015810277 key: train_jcc value: [0.96858639 0.96354167 0.96354167 0.96858639 0.95854922 0.96354167 0.96875 0.97894737 0.96875 0.96875 ] mean value: 0.9671544366088091 key: TN value: 72 mean value: 72.0 key: FP value: 6 mean value: 6.0 key: FN value: 34 mean value: 34.0 key: TP value: 200 mean value: 200.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.77 Accuracy on Blind test: 0.9 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.13504362 0.05386043 0.17046118 0.04772305 0.05267906 0.05315638 0.05455399 0.05298352 0.05491114 0.05289721] mean value: 0.07282695770263672 key: score_time value: [0.01084018 0.01215386 0.01093411 0.01053691 0.01060319 0.0104351 0.01052928 0.01054263 0.0104084 0.01039553] mean value: 0.010737919807434082 key: test_mcc value: [1. 1. 1. 0.85238095 0.93048421 0.93048421 0.79476958 0.93048421 0.85909091 0.72821908] mean value: 0.902591315825731 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 0.95238095 0.97560976 0.97560976 0.92307692 0.97560976 0.95 0.90909091] mean value: 0.9661378052841467 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 0.95238095 1. 1. 0.94736842 0.95238095 0.95 0.83333333] mean value: 0.963546365914787 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.95238095 0.95238095 0.95238095 0.9 1. 0.95 1. ] mean value: 0.9707142857142858 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 0.93548387 0.96774194 0.96774194 0.90322581 0.96774194 0.93548387 0.87096774] mean value: 0.9548387096774194 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 0.92619048 0.97619048 0.97619048 0.90454545 0.95454545 0.92954545 0.81818182] mean value: 0.9485389610389611 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 0.90909091 0.95238095 0.95238095 0.85714286 0.95238095 0.9047619 0.83333333] mean value: 0.9361471861471863 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 98 mean value: 98.0 key: FP value: 6 mean value: 6.0 key: FN value: 8 mean value: 8.0 key: TP value: 200 mean value: 200.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.9 Accuracy on Blind test: 0.95 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03230834 0.04915643 0.02894878 0.03100872 0.06014895 0.04913616 0.02832913 0.02888393 0.03995156 0.07339001] mean value: 0.04212620258331299 key: score_time value: [0.02405763 0.01220226 0.01222754 0.01217437 0.02302003 0.01225495 0.01228237 0.01220059 0.02282333 0.01247096] mean value: 0.015571403503417968 key: test_mcc value: [0.8643122 0.74100101 0.64203411 0.55714286 0.78625916 0.55714286 0.68174942 0.599404 0.79524277 0.56697057] mean value: 0.6791258953421403 key: train_mcc value: [0.96809668 0.96809668 0.96831892 0.97623798 0.96057359 0.97636634 0.96815373 0.96819468 0.97611544 0.97611544] mean value: 0.9706269497086195 key: test_fscore value: [0.95454545 0.9 0.87804878 0.85714286 0.92682927 0.85714286 0.86486486 0.84210526 0.93023256 0.86363636] mean value: 0.8874548267410315 key: train_fscore value: [0.98924731 0.98924731 0.98924731 0.99191375 0.98644986 0.9919571 0.98930481 0.98924731 0.9919571 0.9919571 ] mean value: 0.9900528984948347 key: test_precision value: [0.91304348 0.94736842 0.9 0.85714286 0.95 0.85714286 0.94117647 0.88888889 0.86956522 0.79166667] mean value: 0.891599485713431 key: train_precision value: [0.98395722 0.98395722 0.98395722 0.98924731 0.98913043 0.98404255 0.98404255 0.98924731 0.98930481 0.98930481] mean value: 0.9866191448243959 key: test_recall value: [1. 0.85714286 0.85714286 0.85714286 0.9047619 0.85714286 0.8 0.8 1. 0.95 ] mean value: 0.8883333333333333 key: train_recall value: [0.99459459 0.99459459 0.99459459 0.99459459 0.98378378 1. 0.99462366 0.98924731 0.99462366 0.99462366] mean value: 0.9935280441732054 key: test_accuracy value: [0.9375 0.875 0.83870968 0.80645161 0.90322581 0.80645161 0.83870968 0.80645161 0.90322581 0.80645161] mean value: 0.8522177419354838 key: train_accuracy value: [0.98571429 0.98571429 0.98576512 0.98932384 0.98220641 0.98932384 0.98576512 0.98576512 0.98932384 0.98932384] mean value: 0.9868225724453483 key: test_roc_auc value: [0.90909091 0.88311688 0.82857143 0.77857143 0.90238095 0.77857143 0.85454545 0.80909091 0.86363636 0.74772727] mean value: 0.8355303030303031 key: train_roc_auc value: [0.98150782 0.98150782 0.9816723 0.98688063 0.98147523 0.984375 0.98152235 0.98409734 0.98678551 0.98678551] mean value: 0.983660951911164 key: test_jcc value: [0.91304348 0.81818182 0.7826087 0.75 0.86363636 0.75 0.76190476 0.72727273 0.86956522 0.76 ] mean value: 0.7996213062300018 key: train_jcc value: [0.9787234 0.9787234 0.9787234 0.98395722 0.97326203 0.98404255 0.97883598 0.9787234 0.98404255 0.98404255] mean value: 0.9803076506768622 key: TN value: 83 mean value: 83.0 key: FP value: 23 mean value: 23.0 key: FN value: 23 mean value: 23.0 key: TP value: 183 mean value: 183.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.81 Accuracy on Blind test: 0.92 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.0126524 0.0095458 0.00931358 0.0091176 0.00899625 0.00920296 0.00903535 0.00899458 0.00897455 0.00904608] mean value: 0.0094879150390625 key: score_time value: [0.02371144 0.00912809 0.00880551 0.00858068 0.00853944 0.00854635 0.00848079 0.00854325 0.00861645 0.00864959] mean value: 0.010160160064697266 key: test_mcc value: [0.33910216 0.13261933 0.53526436 0.06513389 0.49780905 0.35192842 0.33300791 0.01363636 0.40800555 0.33300791] mean value: 0.30095149600575316 key: train_mcc value: [0.31603146 0.3455391 0.3767181 0.39130863 0.34815112 0.3313273 0.29256975 0.35900377 0.37052503 0.33144271] mean value: 0.3462616963153834 key: test_fscore value: [0.75 0.72727273 0.86956522 0.68292683 0.82926829 0.83333333 0.79069767 0.65 0.82608696 0.79069767] mean value: 0.7749848705307533 key: train_fscore value: [0.78974359 0.79586563 0.81683168 0.80927835 0.79691517 0.79177378 0.77720207 0.79792746 0.80512821 0.79695431] mean value: 0.7977620256045503 key: test_precision value: [0.78947368 0.69565217 0.8 0.7 0.85 0.74074074 0.73913043 0.65 0.73076923 0.73913043] mean value: 0.7434896699198758 key: train_precision value: [0.75121951 0.76237624 0.75342466 0.77339901 0.75980392 0.75490196 0.75 0.77 0.76960784 0.75480769] mean value: 0.7599540839929344 key: test_recall value: [0.71428571 0.76190476 0.95238095 0.66666667 0.80952381 0.95238095 0.85 0.65 0.95 0.85 ] mean value: 0.8157142857142858 key: train_recall value: [0.83243243 0.83243243 0.89189189 0.84864865 0.83783784 0.83243243 0.80645161 0.82795699 0.84408602 0.84408602] mean value: 0.8398256320836965 key: test_accuracy value: [0.6875 0.625 0.80645161 0.58064516 0.77419355 0.74193548 0.70967742 0.5483871 0.74193548 0.70967742] mean value: 0.6925403225806452 key: train_accuracy value: [0.70714286 0.71785714 0.7366548 0.7366548 0.71886121 0.71174377 0.69395018 0.72241993 0.72953737 0.71530249] mean value: 0.7190124555160142 key: test_roc_auc value: [0.67532468 0.56277056 0.72619048 0.53333333 0.7547619 0.62619048 0.65227273 0.50681818 0.65681818 0.65227273] mean value: 0.6346753246753247 key: train_roc_auc value: [0.64779516 0.66358464 0.66469595 0.68474099 0.66371059 0.65579955 0.64006791 0.67187323 0.67467459 0.65362196] mean value: 0.6620564563927254 key: test_jcc value: [0.6 0.57142857 0.76923077 0.51851852 0.70833333 0.71428571 0.65384615 0.48148148 0.7037037 0.65384615] mean value: 0.63746743996744 key: train_jcc value: [0.65254237 0.66094421 0.69037657 0.67965368 0.66239316 0.65531915 0.63559322 0.6637931 0.67381974 0.66244726] mean value: 0.6636882462571104 key: TN value: 48 mean value: 48.0 key: FP value: 38 mean value: 38.0 key: FN value: 58 mean value: 58.0 key: TP value: 168 mean value: 168.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.33 Accuracy on Blind test: 0.71 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01832533 0.01673365 0.02225184 0.02035999 0.02189851 0.02132702 0.01896715 0.02250695 0.01972532 0.02366352] mean value: 0.020575928688049316 key: score_time value: [0.01003265 0.01122832 0.01144624 0.01167583 0.0117116 0.01166582 0.01175475 0.01174879 0.01169896 0.01169562] mean value: 0.011465859413146973 key: test_mcc value: [1. 0.79772404 0.93048421 0.77484502 1. 0.85238095 0.6310315 0.79524277 0.93048421 0.85909091] mean value: 0.8571283609822942 key: train_mcc value: [0.96009907 0.93692544 0.98422269 0.96835586 0.97635661 0.97623798 0.88905141 0.97624243 0.96819468 0.96847885] mean value: 0.9604165025930188 key: test_fscore value: [1. 0.93333333 0.97560976 0.93023256 1. 0.95238095 0.83333333 0.93023256 0.97560976 0.95 ] mean value: 0.948073224752181 key: train_fscore value: [0.98652291 0.97883598 0.99462366 0.98918919 0.99186992 0.99191375 0.95821727 0.992 0.98924731 0.98918919] mean value: 0.9861609171532406 key: test_precision value: [1. 0.875 1. 0.90909091 1. 0.95238095 0.9375 0.86956522 0.95238095 0.95 ] mean value: 0.9445918031244117 key: train_precision value: [0.98387097 0.95854922 0.98930481 0.98918919 0.99456522 0.98924731 0.99421965 0.98412698 0.98924731 0.99456522] mean value: 0.9866885888307972 key: test_recall value: [1. 1. 0.95238095 0.95238095 1. 0.95238095 0.75 1. 1. 0.95 ] mean value: 0.9557142857142857 key: train_recall value: [0.98918919 1. 1. 0.98918919 0.98918919 0.99459459 0.92473118 1. 0.98924731 0.98387097] mean value: 0.9860011624527754 key: test_accuracy value: [1. 0.90625 0.96774194 0.90322581 1. 0.93548387 0.80645161 0.90322581 0.96774194 0.93548387] mean value: 0.9325604838709678 key: train_accuracy value: [0.98214286 0.97142857 0.99288256 0.98576512 0.98932384 0.98932384 0.94661922 0.98932384 0.98576512 0.98576512] mean value: 0.9818340111845449 key: test_roc_auc value: [1. 0.86363636 0.97619048 0.87619048 1. 0.92619048 0.82954545 0.86363636 0.95454545 0.92954545] mean value: 0.921948051948052 key: train_roc_auc value: [0.97880512 0.95789474 0.98958333 0.98417793 0.98938626 0.98688063 0.95710243 0.98421053 0.98409734 0.98667233] mean value: 0.979881063682528 key: test_jcc value: [1. 0.875 0.95238095 0.86956522 1. 0.90909091 0.71428571 0.86956522 0.95238095 0.9047619 ] mean value: 0.9047030867683041 key: train_jcc value: [0.97340426 0.95854922 0.98930481 0.97860963 0.98387097 0.98395722 0.9197861 0.98412698 0.9787234 0.97860963] mean value: 0.972894221392046 key: TN value: 94 mean value: 94.0 key: FP value: 9 mean value: 9.0 key: FN value: 12 mean value: 12.0 key: TP value: 197 mean value: 197.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.64 Accuracy on Blind test: 0.8 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01601458 0.01648951 0.01605535 0.01551676 0.01533937 0.01700306 0.01540875 0.01559615 0.01406908 0.01540995] mean value: 0.015690255165100097 key: score_time value: [0.01169801 0.01167488 0.01167607 0.01165557 0.011724 0.01172376 0.01167727 0.01172972 0.01162696 0.01171517] mean value: 0.011690139770507812 key: test_mcc value: [0.6798418 0.8643122 0.81199794 0.47434165 0.93048421 0.69695062 0.599404 0.58316015 0.35410712 0.79476958] mean value: 0.6789369269903351 key: train_mcc value: [0.73826562 0.96830875 0.9213983 0.42421821 0.90645458 0.9218965 0.94451335 0.94577703 0.52778998 0.86687445] mean value: 0.8165496770553766 key: test_fscore value: [0.83333333 0.95454545 0.92307692 0.85714286 0.97560976 0.90909091 0.84210526 0.8 0.81632653 0.92307692] mean value: 0.88343079501341 key: train_fscore value: [0.87951807 0.98930481 0.97282609 0.83710407 0.96721311 0.97368421 0.98113208 0.98092643 0.86111111 0.94972067] mean value: 0.9392540657250088 key: test_precision value: [1. 0.91304348 1. 0.75 1. 0.86956522 0.88888889 0.93333333 0.68965517 0.94736842] mean value: 0.8991854511340822 key: train_precision value: [0.99319728 0.97883598 0.97814208 0.71984436 0.97790055 0.94871795 0.98378378 0.99447514 0.75609756 0.98837209] mean value: 0.9319366769335261 key: test_recall value: [0.71428571 1. 0.85714286 1. 0.95238095 0.95238095 0.8 0.7 1. 0.9 ] mean value: 0.8876190476190476 key: train_recall value: [0.78918919 1. 0.96756757 1. 0.95675676 1. 0.97849462 0.96774194 1. 0.91397849] mean value: 0.9573728567276956 key: test_accuracy value: [0.8125 0.9375 0.90322581 0.77419355 0.96774194 0.87096774 0.80645161 0.77419355 0.70967742 0.90322581] mean value: 0.8459677419354839 key: train_accuracy value: [0.85714286 0.98571429 0.96441281 0.74377224 0.95729537 0.96441281 0.97508897 0.97508897 0.78647687 0.93594306] mean value: 0.9145348246059989 key: test_roc_auc value: [0.85714286 0.90909091 0.92857143 0.65 0.97619048 0.82619048 0.80909091 0.80454545 0.59090909 0.90454545] mean value: 0.8256277056277057 key: train_roc_auc value: [0.88933144 0.97894737 0.96295045 0.625 0.95754505 0.94791667 0.97345784 0.97860781 0.68421053 0.94646293] mean value: 0.8944430073112162 key: test_jcc value: [0.71428571 0.91304348 0.85714286 0.75 0.95238095 0.83333333 0.72727273 0.66666667 0.68965517 0.85714286] mean value: 0.7960923758899772 key: train_jcc value: [0.78494624 0.97883598 0.94708995 0.71984436 0.93650794 0.94871795 0.96296296 0.96256684 0.75609756 0.90425532] mean value: 0.89018250936949 key: TN value: 81 mean value: 81.0 key: FP value: 23 mean value: 23.0 key: FN value: 25 mean value: 25.0 key: TP value: 183 mean value: 183.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.76 Accuracy on Blind test: 0.89 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.14809275 0.13004065 0.13075185 0.13108635 0.1305635 0.1305747 0.12959647 0.13134789 0.1296742 0.1307199 ] mean value: 0.13224482536315918 key: score_time value: [0.01494169 0.01492929 0.01494741 0.01512766 0.01498914 0.01503229 0.01501012 0.01628828 0.01516294 0.01494336] mean value: 0.015137219429016113 key: test_mcc value: [1. 1. 0.93048421 0.92687157 1. 0.86831345 0.93048421 0.85909091 0.93048421 0.72821908] mean value: 0.9173947646273607 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 0.97560976 0.97674419 1. 0.95 0.97560976 0.95 0.97560976 0.90909091] mean value: 0.9712664363430102 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 0.95454545 1. 1. 0.95238095 0.95 0.95238095 0.83333333] mean value: 0.9642640692640694 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.95238095 1. 1. 0.9047619 1. 0.95 1. 1. ] mean value: 0.9807142857142856 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 0.96774194 0.96774194 1. 0.93548387 0.96774194 0.93548387 0.96774194 0.87096774] mean value: 0.9612903225806452 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.97619048 0.95 1. 0.95238095 0.95454545 0.92954545 0.95454545 0.81818182] mean value: 0.9535389610389611 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 0.95238095 0.95454545 1. 0.9047619 0.95238095 0.9047619 0.95238095 0.83333333] mean value: 0.9454545454545455 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 98 mean value: 98.0 key: FP value: 4 mean value: 4.0 key: FN value: 8 mean value: 8.0 key: TP value: 202 mean value: 202.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.9 Accuracy on Blind test: 0.95 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.04230762 0.04330516 0.04764128 0.036551 0.05268097 0.04338908 0.04194522 0.05751896 0.04093218 0.03783798] mean value: 0.04441094398498535 key: score_time value: [0.02528048 0.02137256 0.01939631 0.0182085 0.03135848 0.02629328 0.02537656 0.02307057 0.02730846 0.0283308 ] mean value: 0.024599599838256835 key: test_mcc value: [0.93435318 1. 1. 0.92687157 0.93048421 0.93048421 0.93048421 0.79476958 0.85909091 0.72821908] mean value: 0.9034756961158659 key: train_mcc value: [0.9920858 0.98411246 0.99213963 1. 1. 0.99210029 1. 1. 0.99205967 0.99205967] mean value: 0.9944557518725569 key: test_fscore value: [0.97560976 1. 1. 0.97674419 0.97560976 0.97560976 0.97560976 0.92307692 0.95 0.90909091] mean value: 0.9661351042604587 key: train_fscore value: [0.99728997 0.99462366 0.99728997 1. 1. 0.99730458 1. 1. 0.99731903 0.99731903] mean value: 0.9981146253628772 key: test_precision value: [1. 1. 1. 0.95454545 1. 1. 0.95238095 0.94736842 0.95 0.83333333] mean value: 0.9637628161312373 key: train_precision value: [1. 0.98930481 1. 1. 1. 0.99462366 1. 1. 0.99465241 0.99465241] mean value: 0.9973233281582428 key: test_recall value: [0.95238095 1. 1. 1. 0.95238095 0.95238095 1. 0.9 0.95 1. ] mean value: 0.9707142857142858 key: train_recall value: [0.99459459 1. 0.99459459 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.998918918918919 key: test_accuracy value: [0.96875 1. 1. 0.96774194 0.96774194 0.96774194 0.96774194 0.90322581 0.93548387 0.87096774] mean value: 0.9549395161290322 key: train_accuracy value: [0.99642857 0.99285714 0.99644128 1. 1. 0.99644128 1. 1. 0.99644128 0.99644128] mean value: 0.9975050838840873 key: test_roc_auc value: [0.97619048 1. 1. 0.95 0.97619048 0.97619048 0.95454545 0.90454545 0.92954545 0.81818182] mean value: 0.9485389610389611 key: train_roc_auc value: [0.9972973 0.98947368 0.9972973 1. 1. 0.99479167 1. 1. 0.99473684 0.99473684] mean value: 0.9968333629682314 key: test_jcc value: [0.95238095 1. 1. 0.95454545 0.95238095 0.95238095 0.95238095 0.85714286 0.9047619 0.83333333] mean value: 0.9359307359307361 key: train_jcc value: [0.99459459 0.98930481 0.99459459 1. 1. 0.99462366 1. 1. 0.99465241 0.99465241] mean value: 0.9962422470771616 key: TN value: 98 mean value: 98.0 key: FP value: 6 mean value: 6.0 key: FN value: 8 mean value: 8.0 key: TP value: 200 mean value: 200.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.84 Accuracy on Blind test: 0.93 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.06104779 0.05568504 0.03996634 0.04052663 0.08026958 0.09496188 0.0754683 0.08375978 0.0394938 0.04188108] mean value: 0.06130602359771729 key: score_time value: [0.01644206 0.01353455 0.01360631 0.01330638 0.02197981 0.01357412 0.02960944 0.01343012 0.01336837 0.02463293] mean value: 0.017348408699035645 key: test_mcc value: [0.15803489 0.4133805 0.12245134 0.01126872 0.55777335 0.18593394 0.43636364 0.02485134 0.40800555 0.33300791] mean value: 0.26510711743317406 key: train_mcc value: [0.88063607 0.92018324 0.85676692 0.84883567 0.87460224 0.88093695 0.8816558 0.88870395 0.88870395 0.87386742] mean value: 0.8794892204012807 key: test_fscore value: [0.76595745 0.83333333 0.75555556 0.73913043 0.875 0.7826087 0.8 0.69767442 0.82608696 0.79069767] mean value: 0.7866044515677177 key: train_fscore value: [0.96062992 0.97340426 0.95263158 0.95013123 0.95833333 0.96042216 0.96103896 0.96335079 0.96335079 0.95854922] mean value: 0.9601842240561401 key: test_precision value: [0.69230769 0.74074074 0.70833333 0.68 0.77777778 0.72 0.8 0.65217391 0.73076923 0.73913043] mean value: 0.7241233122754862 key: train_precision value: [0.93367347 0.95811518 0.92820513 0.92346939 0.92462312 0.93814433 0.92964824 0.93877551 0.93877551 0.925 ] mean value: 0.933842987568305 key: test_recall value: [0.85714286 0.95238095 0.80952381 0.80952381 1. 0.85714286 0.8 0.75 0.95 0.85 ] mean value: 0.8635714285714287 key: train_recall value: [0.98918919 0.98918919 0.97837838 0.97837838 0.99459459 0.98378378 0.99462366 0.98924731 0.98924731 0.99462366] mean value: 0.9881255448997385 key: test_accuracy value: [0.65625 0.75 0.64516129 0.61290323 0.80645161 0.67741935 0.74193548 0.58064516 0.74193548 0.70967742] mean value: 0.6922379032258065 key: train_accuracy value: [0.94642857 0.96428571 0.93594306 0.93238434 0.9430605 0.94661922 0.94661922 0.95017794 0.95017794 0.9430605 ] mean value: 0.945875699034062 key: test_roc_auc value: [0.56493506 0.65800866 0.5547619 0.5047619 0.7 0.57857143 0.71818182 0.51136364 0.65681818 0.65227273] mean value: 0.6099675324675324 key: train_roc_auc value: [0.92617354 0.95248933 0.91627252 0.91106419 0.9191723 0.92939189 0.92362762 0.93146576 0.93146576 0.91836446] mean value: 0.92594873736215 key: test_jcc value: [0.62068966 0.71428571 0.60714286 0.5862069 0.77777778 0.64285714 0.66666667 0.53571429 0.7037037 0.65384615] mean value: 0.650889085371844 key: train_jcc value: [0.92424242 0.94818653 0.90954774 0.905 0.92 0.92385787 0.925 0.92929293 0.92929293 0.92039801] mean value: 0.9234818427989714 key: TN value: 38 mean value: 38.0 key: FP value: 28 mean value: 28.0 key: FN value: 68 mean value: 68.0 key: TP value: 178 mean value: 178.0 key: trainingY_neg value: 106 mean value: 106.0 key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.28 Accuracy on Blind test: 0.7 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.46115923 0.44185972 0.44269156 0.44909668 0.44474816 0.43808627 0.43469429 0.43570971 0.43906617 0.4296422 ] mean value: 0.44167540073394773 key: score_time value: [0.0092442 0.00915384 0.00920796 0.00919032 0.00921798 0.00926757 0.00912905 0.00921392 0.00900984 0.00932336] mean value: 0.009195804595947266 key: test_mcc value: [1. 1. 1. 0.85238095 1. 0.93048421 0.93048421 0.93048421 0.85909091 0.72821908] mean value: 0.9231143573921694 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 0.95238095 1. 0.97560976 0.97560976 0.97560976 0.95 0.90909091] mean value: 0.9738301129764544 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 0.95238095 1. 1. 0.95238095 0.95238095 0.95 0.83333333] mean value: 0.964047619047619 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.95238095 1. 0.95238095 1. 1. 0.95 1. ] mean value: 0.9854761904761904 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 0.93548387 1. 0.96774194 0.96774194 0.96774194 0.93548387 0.87096774] mean value: 0.964516129032258 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 0.92619048 1. 0.97619048 0.95454545 0.95454545 0.92954545 0.81818182] mean value: 0.9559199134199134 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 0.90909091 1. 0.95238095 0.95238095 0.95238095 0.9047619 0.83333333] mean value: 0.9504329004329005 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 98 mean value: 98.0 key: FP value: 3 mean value: 3.0 key: FN value: 8 mean value: 8.0 key: TP value: 203 mean value: 203.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.87 Accuracy on Blind test: 0.94 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.0233779 0.02502084 0.02472115 0.03441906 0.02718186 0.03686452 0.0269208 0.0285058 0.02501702 0.0264864 ] mean value: 0.02785153388977051 key: score_time value: [0.01227784 0.01208687 0.01251578 0.01264119 0.01323295 0.0203855 0.01288342 0.01299381 0.01248217 0.01287174] mean value: 0.013437128067016602 key: test_mcc value: [ 0.21867346 0.0849412 -0.26560636 0.09967105 -0.05976143 0.00752923 0.01363636 -0.23927198 0.14863011 0.22469871] mean value: 0.023314035374443605 key: train_mcc value: [0.34354378 0.33200663 0.37383194 0.35226764 0.35226764 0.34110438 0.34382047 0.39766525 0.32040778 0.35507261] mean value: 0.3511988107242974 key: test_fscore value: [0.8 0.78431373 0.70833333 0.8 0.75 0.7755102 0.65 0.63636364 0.75555556 0.7826087 ] mean value: 0.7442685150476528 key: train_fscore value: [0.82405345 0.82222222 0.82774049 0.82405345 0.82405345 0.82222222 0.8248337 0.83408072 0.82119205 0.82666667] mean value: 0.8251118432979977 key: test_precision value: [0.68965517 0.66666667 0.62962963 0.68965517 0.66666667 0.67857143 0.65 0.58333333 0.68 0.69230769] mean value: 0.6626485762003004 key: train_precision value: [0.70075758 0.69811321 0.70610687 0.70075758 0.70075758 0.69811321 0.70188679 0.71538462 0.69662921 0.70454545] mean value: 0.7023052088462121 key: test_recall value: [0.95238095 0.95238095 0.80952381 0.95238095 0.85714286 0.9047619 0.65 0.7 0.85 0.9 ] mean value: 0.8528571428571429 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6875 0.65625 0.5483871 0.67741935 0.61290323 0.64516129 0.5483871 0.48387097 0.64516129 0.67741935] mean value: 0.6182459677419355 key: train_accuracy value: [0.71785714 0.71428571 0.72597865 0.71886121 0.71886121 0.71530249 0.71886121 0.7366548 0.71174377 0.72241993] mean value: 0.7200826131164211 key: test_roc_auc value: [0.56709957 0.52164502 0.4047619 0.52619048 0.47857143 0.50238095 0.50681818 0.39545455 0.56136364 0.58636364] mean value: 0.505064935064935 key: train_roc_auc value: [0.58421053 0.57894737 0.59895833 0.58854167 0.58854167 0.58333333 0.58421053 0.61052632 0.57368421 0.58947368] mean value: 0.5880427631578947 key: test_jcc value: [0.66666667 0.64516129 0.5483871 0.66666667 0.6 0.63333333 0.48148148 0.46666667 0.60714286 0.64285714] mean value: 0.5958363201911588 key: train_jcc value: [0.70075758 0.69811321 0.70610687 0.70075758 0.70075758 0.69811321 0.70188679 0.71538462 0.69662921 0.70454545] mean value: 0.7023052088462121 key: TN value: 17 mean value: 17.0 key: FP value: 30 mean value: 30.0 key: FN value: 89 mean value: 89.0 key: TP value: 176 mean value: 176.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.17 Accuracy on Blind test: 0.68 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02277946 0.03577924 0.03494143 0.03556728 0.03561592 0.03502131 0.03542185 0.03545427 0.03555131 0.03549528] mean value: 0.03416273593902588 key: score_time value: [0.02342391 0.02337527 0.02280545 0.02236032 0.02176046 0.02052379 0.02406287 0.0236938 0.0235455 0.02287507] mean value: 0.0228426456451416 key: test_mcc value: [1. 0.86147186 0.93048421 0.69695062 1. 0.85238095 0.85909091 0.79476958 0.93048421 0.72821908] mean value: 0.8653851428550935 key: train_mcc value: [0.94408115 0.95208091 0.95253998 0.95241514 0.95241514 0.95253998 0.96017122 0.96815373 0.96021134 0.96021134] mean value: 0.9554819935656209 key: test_fscore value: [1. 0.95238095 0.97560976 0.90909091 1. 0.95238095 0.95 0.92307692 0.97560976 0.90909091] mean value: 0.9547240158215766 key: train_fscore value: [0.98123324 0.98387097 0.98395722 0.98387097 0.98387097 0.98395722 0.98659517 0.98930481 0.98666667 0.98666667] mean value: 0.9849993906126601 key: test_precision value: [1. 0.95238095 1. 0.86956522 1. 0.95238095 0.95 0.94736842 0.95238095 0.83333333] mean value: 0.9457409828920126 key: train_precision value: [0.97340426 0.97860963 0.97354497 0.97860963 0.97860963 0.97354497 0.98395722 0.98404255 0.97883598 0.97883598] mean value: 0.9781994809529229 key: test_recall value: [1. 0.95238095 0.95238095 0.95238095 1. 0.95238095 0.95 0.9 1. 1. ] mean value: 0.9659523809523808 key: train_recall value: [0.98918919 0.98918919 0.99459459 0.98918919 0.98918919 0.99459459 0.98924731 0.99462366 0.99462366 0.99462366] mean value: 0.9919064225515838 key: test_accuracy value: [1. 0.9375 0.96774194 0.87096774 1. 0.93548387 0.93548387 0.90322581 0.96774194 0.87096774] mean value: 0.9389112903225808 key: train_accuracy value: [0.975 0.97857143 0.97864769 0.97864769 0.97864769 0.97864769 0.98220641 0.98576512 0.98220641 0.98220641] mean value: 0.9800546517539402 key: test_roc_auc value: [1. 0.93073593 0.97619048 0.82619048 1. 0.92619048 0.92954545 0.90454545 0.95454545 0.81818182] mean value: 0.9266125541125542 key: train_roc_auc value: [0.96827881 0.97354196 0.97125563 0.97376126 0.97376126 0.97125563 0.97883418 0.98152235 0.9762592 0.9762592 ] mean value: 0.9744729481178972 key: test_jcc value: [1. 0.90909091 0.95238095 0.83333333 1. 0.90909091 0.9047619 0.85714286 0.95238095 0.83333333] mean value: 0.9151515151515153 key: train_jcc value: [0.96315789 0.96825397 0.96842105 0.96825397 0.96825397 0.96842105 0.97354497 0.97883598 0.97368421 0.97368421] mean value: 0.9704511278195488 key: TN value: 94 mean value: 94.0 key: FP value: 7 mean value: 7.0 key: FN value: 12 mean value: 12.0 key: TP value: 199 mean value: 199.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.87 Accuracy on Blind test: 0.94 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.23733401 0.24284005 0.25065804 0.2886591 0.24323916 0.2424953 0.43217683 0.28001761 0.36137295 0.34959149] mean value: 0.292838454246521 key: score_time value: [0.0211041 0.02119303 0.02349997 0.01739216 0.02394438 0.02036405 0.02439165 0.0242722 0.02366924 0.02450585] mean value: 0.022433662414550783 key: test_mcc value: [1. 0.86147186 0.93048421 0.69695062 1. 0.85238095 0.85909091 0.79476958 0.93048421 0.66057826] mean value: 0.8586210606372333 key: train_mcc value: [0.94408115 0.95208091 0.95253998 0.95241514 0.95241514 0.95253998 0.96017122 0.96815373 0.96021134 0.97611544] mean value: 0.9570724042956436 key: test_fscore value: [1. 0.95238095 0.97560976 0.90909091 1. 0.95238095 0.95 0.92307692 0.97560976 0.88888889] mean value: 0.9527038138013747 key: train_fscore value: [0.98123324 0.98387097 0.98395722 0.98387097 0.98387097 0.98395722 0.98659517 0.98930481 0.98666667 0.9919571 ] mean value: 0.9855284344017574 key: test_precision value: [1. 0.95238095 1. 0.86956522 1. 0.95238095 0.95 0.94736842 0.95238095 0.8 ] mean value: 0.9424076495586793 key: train_precision value: [0.97340426 0.97860963 0.97354497 0.97860963 0.97860963 0.97354497 0.98395722 0.98404255 0.97883598 0.98930481] mean value: 0.9792463643527473 key: test_recall value: [1. 0.95238095 0.95238095 0.95238095 1. 0.95238095 0.95 0.9 1. 1. ] mean value: 0.9659523809523808 key: train_recall value: [0.98918919 0.98918919 0.99459459 0.98918919 0.98918919 0.99459459 0.98924731 0.99462366 0.99462366 0.99462366] mean value: 0.9919064225515838 key: test_accuracy value: [1. 0.9375 0.96774194 0.87096774 1. 0.93548387 0.93548387 0.90322581 0.96774194 0.83870968] mean value: 0.9356854838709678 key: train_accuracy value: [0.975 0.97857143 0.97864769 0.97864769 0.97864769 0.97864769 0.98220641 0.98576512 0.98220641 0.98932384] mean value: 0.9807663955261822 key: test_roc_auc value: [1. 0.93073593 0.97619048 0.82619048 1. 0.92619048 0.92954545 0.90454545 0.95454545 0.77272727] mean value: 0.9220670995670996 key: train_roc_auc value: [0.96827881 0.97354196 0.97125563 0.97376126 0.97376126 0.97125563 0.97883418 0.98152235 0.9762592 0.98678551] mean value: 0.9755255796968445 key: test_jcc value: [1. 0.90909091 0.95238095 0.83333333 1. 0.90909091 0.9047619 0.85714286 0.95238095 0.8 ] mean value: 0.9118181818181819 key: train_jcc value: [0.96315789 0.96825397 0.96842105 0.96825397 0.96825397 0.96842105 0.97354497 0.97883598 0.97368421 0.98404255] mean value: 0.9714869620860662 key: TN value: 93 mean value: 93.0 key: FP value: 7 mean value: 7.0 key: FN value: 13 mean value: 13.0 key: TP value: 199 mean value: 199.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:130: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:131: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_CV['Resampling'] = rs_none /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:136: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:137: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_BT['Resampling'] = rs_none /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.87 Accuracy on Blind test: 0.94 PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.03308296 0.03610563 0.03306627 0.03525186 0.03636479 0.03501225 0.03478789 0.0367043 0.03446531 0.03514433] mean value: 0.03499855995178223 key: score_time value: [0.01234603 0.01233053 0.01335764 0.01235819 0.01245904 0.01250768 0.01237798 0.01265287 0.01228142 0.01239014] mean value: 0.01250615119934082 key: test_mcc value: [0.95346259 0.90889326 0.85441771 0.76500781 0.85441771 0.8547619 0.80817439 0.70714286 0.90238095 0.85441771] mean value: 0.846307688531032 key: train_mcc value: [0.94066423 0.95136525 0.93025158 0.94614468 0.9355233 0.95160448 0.956873 0.940826 0.940826 0.94071491] mean value: 0.9434793423451845 key: test_fscore value: [0.97674419 0.95 0.93023256 0.87179487 0.93023256 0.92682927 0.89473684 0.85 0.95 0.92307692] mean value: 0.9203647207595322 key: train_fscore value: [0.97002725 0.97560976 0.96438356 0.97282609 0.96721311 0.97547684 0.97849462 0.9701897 0.9701897 0.9703504 ] mean value: 0.9714761038408097 key: test_precision value: [0.95454545 1. 0.90909091 0.94444444 0.90909091 0.95 0.94444444 0.85 0.95 0.94736842] mean value: 0.9358984582668792 key: train_precision value: [0.97802198 0.97826087 0.97777778 0.97814208 0.97790055 0.98351648 0.97849462 0.97814208 0.97814208 0.97297297] mean value: 0.978137148750473 key: test_recall value: [1. 0.9047619 0.95238095 0.80952381 0.95238095 0.9047619 0.85 0.85 0.95 0.9 ] mean value: 0.9073809523809523 key: train_recall value: [0.96216216 0.97297297 0.95135135 0.96756757 0.95675676 0.96756757 0.97849462 0.96236559 0.96236559 0.96774194] mean value: 0.9649346120313862 key: test_accuracy value: [0.97619048 0.95238095 0.92682927 0.87804878 0.92682927 0.92682927 0.90243902 0.85365854 0.95121951 0.92682927] mean value: 0.9221254355400698 key: train_accuracy value: [0.97027027 0.97567568 0.96495957 0.97304582 0.96765499 0.97574124 0.97843666 0.9703504 0.9703504 0.9703504 ] mean value: 0.9716835433816566 key: test_roc_auc value: [0.97619048 0.95238095 0.92619048 0.8797619 0.92619048 0.92738095 0.90119048 0.85357143 0.95119048 0.92619048] mean value: 0.9220238095238095 key: train_roc_auc value: [0.97027027 0.97567568 0.96492299 0.9730311 0.96762569 0.97571927 0.9784365 0.97037198 0.97037198 0.97035745] mean value: 0.9716782911944202 key: test_jcc value: [0.95454545 0.9047619 0.86956522 0.77272727 0.86956522 0.86363636 0.80952381 0.73913043 0.9047619 0.85714286] mean value: 0.8545360436664785 key: train_jcc value: [0.94179894 0.95238095 0.93121693 0.94708995 0.93650794 0.95212766 0.95789474 0.94210526 0.94210526 0.94240838] mean value: 0.9445636008690423 key: TN value: 193 mean value: 193.0 key: FP value: 19 mean value: 19.0 key: FN value: 13 mean value: 13.0 key: TP value: 187 mean value: 187.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.75 Accuracy on Blind test: 0.88 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.76303077 0.8300209 0.73767257 0.85287404 0.74666619 0.72793317 0.82305479 0.78377914 0.7454052 0.87989378] mean value: 0.789033055305481 key: score_time value: [0.01280284 0.01379657 0.01262498 0.01349235 0.01266551 0.01262546 0.01262879 0.01256895 0.01259089 0.0126822 ] mean value: 0.01284785270690918 key: test_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.9047619 0.9047619 0.90692382 0.90238095 0.95227002 0.90692382 0.95227002 0.7565654 1. 0.95238095] mean value: 0.9139238793157022 key: train_mcc value: [1. 0.99460913 1. 1. 0.99462366 1. 0.9946235 0.9946235 0.98927544 1. ] mean value: 0.9967755223131768 key: test_fscore value: [0.95238095 0.95238095 0.95 0.95238095 0.97674419 0.95 0.97435897 0.87179487 1. 0.97560976] mean value: 0.9555650645440774 key: train_fscore value: [1. 0.99730458 1. 1. 0.99730458 1. 0.99731903 0.99731903 0.99465241 1. ] mean value: 0.998389964054269 key: test_precision value: [0.95238095 0.95238095 1. 0.95238095 0.95454545 1. 1. 0.89473684 1. 0.95238095] mean value: 0.9658806106174527 key: train_precision value: [1. 0.99462366 1. 1. 0.99462366 1. 0.99465241 0.99465241 0.9893617 1. ] mean value: 0.9967913826789842 key: test_recall value: [0.95238095 0.95238095 0.9047619 0.95238095 1. 0.9047619 0.95 0.85 1. 1. ] mean value: 0.9466666666666667 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95238095 0.95238095 0.95121951 0.95121951 0.97560976 0.95121951 0.97560976 0.87804878 1. 0.97560976] mean value: 0.9563298490127756 key: train_accuracy value: [1. 0.9972973 1. 1. 0.99730458 1. 0.99730458 0.99730458 0.99460916 1. ] mean value: 0.998382020834851 key: test_roc_auc value: [0.95238095 0.95238095 0.95238095 0.95119048 0.975 0.95238095 0.975 0.87738095 1. 0.97619048] mean value: 0.9564285714285713 key: train_roc_auc value: [1. 0.9972973 1. 1. 0.99731183 1. 0.9972973 0.9972973 0.99459459 1. ] mean value: 0.9983798314443476 key: test_jcc value: [0.90909091 0.90909091 0.9047619 0.90909091 0.95454545 0.9047619 0.95 0.77272727 1. 0.95238095] mean value: 0.9166450216450215 key: train_jcc value: [1. 0.99462366 1. 1. 0.99462366 1. 0.99465241 0.99465241 0.9893617 1. ] mean value: 0.9967913826789842 key: TN value: 199 mean value: 199.0 key: FP value: 11 mean value: 11.0 key: FN value: 7 mean value: 7.0 key: TP value: 195 mean value: 195.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.86 Accuracy on Blind test: 0.94 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01330519 0.01353216 0.01044512 0.00971842 0.00956607 0.00976133 0.00986981 0.00953341 0.00945997 0.00939202] mean value: 0.01045835018157959 key: score_time value: [0.01211691 0.01043463 0.01057625 0.00960898 0.00906467 0.008955 0.00923419 0.00883865 0.00894046 0.00897145] mean value: 0.00967411994934082 key: test_mcc value: [0.52380952 0.3478328 0.41963703 0.36718832 0.65871309 0.51966679 0.41766229 0.26904762 0.56836003 0.56836003] mean value: 0.46602775272823205 key: train_mcc value: [0.49994094 0.51360753 0.57218883 0.55700213 0.53457148 0.57456037 0.54451453 0.57340242 0.5491086 0.58606107] mean value: 0.550495789809396 key: test_fscore value: [0.76190476 0.70833333 0.73913043 0.71111111 0.8372093 0.7826087 0.71428571 0.63414634 0.79069767 0.79069767] mean value: 0.7470125043695909 key: train_fscore value: [0.76092545 0.77078086 0.79487179 0.78880407 0.77749361 0.7979798 0.7826087 0.7979798 0.78350515 0.805 ] mean value: 0.7859949224802306 key: test_precision value: [0.76190476 0.62962963 0.68 0.66666667 0.81818182 0.72 0.68181818 0.61904762 0.73913043 0.73913043] mean value: 0.7055509546813894 key: train_precision value: [0.7254902 0.72169811 0.75609756 0.74519231 0.73786408 0.74881517 0.74634146 0.75238095 0.75247525 0.75233645] mean value: 0.7438691533419045 key: test_recall value: [0.76190476 0.80952381 0.80952381 0.76190476 0.85714286 0.85714286 0.75 0.65 0.85 0.85 ] mean value: 0.7957142857142856 key: train_recall value: [0.8 0.82702703 0.83783784 0.83783784 0.82162162 0.85405405 0.82258065 0.84946237 0.8172043 0.8655914 ] mean value: 0.8333217088055799 key: test_accuracy value: [0.76190476 0.66666667 0.70731707 0.68292683 0.82926829 0.75609756 0.70731707 0.63414634 0.7804878 0.7804878 ] mean value: 0.7306620209059232 key: train_accuracy value: [0.74864865 0.75405405 0.78436658 0.77628032 0.76549865 0.78436658 0.77088949 0.78436658 0.77358491 0.78975741] mean value: 0.7731813214832083 key: test_roc_auc value: [0.76190476 0.66666667 0.7047619 0.68095238 0.82857143 0.75357143 0.70833333 0.63452381 0.78214286 0.78214286] mean value: 0.7303571428571429 key: train_roc_auc value: [0.74864865 0.75405405 0.78451032 0.7764458 0.76564952 0.78455391 0.77074978 0.78419064 0.77346702 0.78955246] mean value: 0.7731822144725371 key: test_jcc value: [0.61538462 0.5483871 0.5862069 0.55172414 0.72 0.64285714 0.55555556 0.46428571 0.65384615 0.65384615] mean value: 0.5992093467032289 key: train_jcc value: [0.61410788 0.62704918 0.65957447 0.6512605 0.63598326 0.66386555 0.64285714 0.66386555 0.6440678 0.67364017] mean value: 0.6476271499298714 key: TN value: 137 mean value: 137.0 key: FP value: 42 mean value: 42.0 key: FN value: 69 mean value: 69.0 key: TP value: 164 mean value: 164.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.43 Accuracy on Blind test: 0.74 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01092005 0.01027369 0.00994992 0.00966907 0.00953984 0.00965834 0.00972724 0.00973678 0.00973964 0.00964236] mean value: 0.009885692596435547 key: score_time value: [0.00965691 0.00880075 0.00904751 0.00887823 0.00878286 0.00878644 0.00877023 0.00896668 0.00879884 0.00876403] mean value: 0.008925247192382812 key: test_mcc value: [0.19611614 0.24253563 0.35038478 0.37171226 0.59982886 0.62325386 0.62048368 0.21957752 0.27179142 0.46300848] mean value: 0.3958692609699289 key: train_mcc value: [0.5197894 0.47073308 0.4803209 0.47192026 0.46367088 0.47192026 0.45388122 0.48220059 0.47119158 0.49005654] mean value: 0.4775684717659203 key: test_fscore value: [0.54054054 0.57894737 0.58823529 0.66666667 0.74285714 0.78947368 0.70967742 0.55555556 0.57142857 0.71794872] mean value: 0.646133096110126 key: train_fscore value: [0.72507553 0.67515924 0.69538462 0.68535826 0.67507886 0.68535826 0.6809816 0.70658683 0.7005988 0.71764706] mean value: 0.694722903766912 key: test_precision value: [0.625 0.64705882 0.76923077 0.72222222 0.92857143 0.88235294 1. 0.625 0.66666667 0.73684211] mean value: 0.7602944956660127 key: train_precision value: [0.82191781 0.82170543 0.80714286 0.80882353 0.81060606 0.80882353 0.79285714 0.7972973 0.79054054 0.79220779] mean value: 0.8051921984050987 key: test_recall value: [0.47619048 0.52380952 0.47619048 0.61904762 0.61904762 0.71428571 0.55 0.5 0.5 0.7 ] mean value: 0.567857142857143 key: train_recall value: [0.64864865 0.57297297 0.61081081 0.59459459 0.57837838 0.59459459 0.59677419 0.6344086 0.62903226 0.65591398] mean value: 0.6116129032258065 key: test_accuracy value: [0.5952381 0.61904762 0.65853659 0.68292683 0.7804878 0.80487805 0.7804878 0.6097561 0.63414634 0.73170732] mean value: 0.6897212543554007 key: train_accuracy value: [0.75405405 0.72432432 0.73315364 0.7277628 0.72237197 0.7277628 0.71967655 0.73584906 0.73045822 0.74123989] mean value: 0.7316653310992934 key: test_roc_auc value: [0.5952381 0.61904762 0.66309524 0.68452381 0.78452381 0.80714286 0.775 0.60714286 0.63095238 0.73095238] mean value: 0.6897619047619047 key: train_roc_auc value: [0.75405405 0.72432432 0.73282476 0.72740482 0.72198489 0.72740482 0.72000872 0.73612322 0.73073235 0.7414705 ] mean value: 0.7316332461493751 key: test_jcc value: [0.37037037 0.40740741 0.41666667 0.5 0.59090909 0.65217391 0.55 0.38461538 0.4 0.56 ] mean value: 0.48321428330123994 key: train_jcc value: [0.56872038 0.50961538 0.53301887 0.52132701 0.50952381 0.52132701 0.51627907 0.5462963 0.53917051 0.55963303] mean value: 0.5324911370145777 key: TN value: 167 mean value: 167.0 key: FP value: 89 mean value: 89.0 key: FN value: 39 mean value: 39.0 key: TP value: 117 mean value: 117.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.28 Accuracy on Blind test: 0.63 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00904441 0.00994539 0.00996995 0.00994349 0.00990462 0.00975704 0.00993729 0.00986505 0.00999761 0.00940776] mean value: 0.009777259826660157 key: score_time value: [0.01108098 0.0159514 0.01206779 0.01446748 0.01251292 0.01263952 0.01265025 0.01304054 0.01275277 0.01227689] mean value: 0.01294405460357666 key: test_mcc value: [0.43052839 0.53357838 0.27338837 0.41766229 0.6133669 0.51190476 0.51320273 0.42916625 0.51190476 0.31655495] mean value: 0.4551257782268342 key: train_mcc value: [0.64509306 0.67193129 0.66110925 0.6784174 0.65112849 0.68828497 0.66576577 0.65242388 0.66119778 0.68278387] mean value: 0.6658135765240021 key: test_fscore value: [0.7 0.73684211 0.61538462 0.7 0.8 0.76190476 0.73684211 0.64705882 0.75 0.63157895] mean value: 0.7079611358713526 key: train_fscore value: [0.81460674 0.82913165 0.82548476 0.83146067 0.81792717 0.83888889 0.83333333 0.81690141 0.82644628 0.83746556] mean value: 0.8271646480205639 key: test_precision value: [0.73684211 0.82352941 0.66666667 0.73684211 0.84210526 0.76190476 0.77777778 0.78571429 0.75 0.66666667] mean value: 0.7548049044179075 key: train_precision value: [0.84795322 0.86046512 0.84659091 0.86549708 0.84883721 0.86285714 0.83333333 0.85798817 0.84745763 0.85875706] mean value: 0.8529736858206451 key: test_recall value: [0.66666667 0.66666667 0.57142857 0.66666667 0.76190476 0.76190476 0.7 0.55 0.75 0.6 ] mean value: 0.6695238095238094 key: train_recall value: [0.78378378 0.8 0.80540541 0.8 0.78918919 0.81621622 0.83333333 0.77956989 0.80645161 0.8172043 ] mean value: 0.803115373437954 key: test_accuracy value: [0.71428571 0.76190476 0.63414634 0.70731707 0.80487805 0.75609756 0.75609756 0.70731707 0.75609756 0.65853659] mean value: 0.7256678281068524 key: train_accuracy value: [0.82162162 0.83513514 0.83018868 0.83827493 0.82479784 0.84366577 0.8328841 0.82479784 0.83018868 0.84097035] mean value: 0.8322524950826837 key: test_roc_auc value: [0.71428571 0.76190476 0.63571429 0.70833333 0.80595238 0.75595238 0.7547619 0.70357143 0.75595238 0.65714286] mean value: 0.7253571428571429 key: train_roc_auc value: [0.82162162 0.83513514 0.83012206 0.83817204 0.82470212 0.84359198 0.83288288 0.82492008 0.83025283 0.84103458] mean value: 0.832243533856437 key: test_jcc value: [0.53846154 0.58333333 0.44444444 0.53846154 0.66666667 0.61538462 0.58333333 0.47826087 0.6 0.46153846] mean value: 0.5509884801189149 key: train_jcc value: [0.68720379 0.70813397 0.70283019 0.71153846 0.69194313 0.72248804 0.71428571 0.69047619 0.70422535 0.72037915] mean value: 0.7053503983012377 key: TN value: 161 mean value: 161.0 key: FP value: 68 mean value: 68.0 key: FN value: 45 mean value: 45.0 key: TP value: 138 mean value: 138.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.37 Accuracy on Blind test: 0.7 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.02168798 0.01971412 0.01750493 0.0180738 0.01735806 0.01955819 0.01806736 0.02039933 0.01962757 0.02065182] mean value: 0.01926431655883789 key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( score_time value: [0.01141286 0.01185846 0.01218891 0.01214862 0.01135635 0.01124072 0.01116204 0.0116775 0.01220202 0.01134872] mean value: 0.011659622192382812 key: test_mcc value: [0.66742381 0.62187434 0.56086079 0.65952381 0.66432098 0.7098505 0.7197263 0.51190476 0.76500781 0.71121921] mean value: 0.6591712308091895 key: train_mcc value: [0.78382959 0.81626392 0.79519893 0.80055217 0.80596972 0.81143372 0.83298455 0.81132843 0.79514676 0.80063366] mean value: 0.8053341444808453 key: test_fscore value: [0.8372093 0.8 0.79069767 0.82926829 0.84444444 0.86363636 0.83333333 0.75 0.88372093 0.85714286] mean value: 0.828945319821667 key: train_fscore value: [0.89130435 0.9076087 0.89784946 0.90026954 0.90217391 0.90616622 0.91733333 0.90616622 0.89784946 0.90133333] mean value: 0.9028054529376849 key: test_precision value: [0.81818182 0.84210526 0.77272727 0.85 0.79166667 0.82608696 0.9375 0.75 0.82608696 0.81818182] mean value: 0.8232536751958948 key: train_precision value: [0.89617486 0.91256831 0.89304813 0.89784946 0.90710383 0.89893617 0.91005291 0.90374332 0.89784946 0.89417989] mean value: 0.9011506337562538 key: test_recall value: [0.85714286 0.76190476 0.80952381 0.80952381 0.9047619 0.9047619 0.75 0.75 0.95 0.9 ] mean value: 0.8397619047619047 key: train_recall value: [0.88648649 0.9027027 0.9027027 0.9027027 0.8972973 0.91351351 0.92473118 0.90860215 0.89784946 0.90860215] mean value: 0.9045190351641965 key: test_accuracy value: [0.83333333 0.80952381 0.7804878 0.82926829 0.82926829 0.85365854 0.85365854 0.75609756 0.87804878 0.85365854] mean value: 0.8277003484320558 key: train_accuracy value: [0.89189189 0.90810811 0.89757412 0.90026954 0.90296496 0.90566038 0.91644205 0.90566038 0.89757412 0.90026954] mean value: 0.9026415094339623 key: test_roc_auc value: [0.83333333 0.80952381 0.7797619 0.8297619 0.82738095 0.85238095 0.85119048 0.75595238 0.8797619 0.8547619 ] mean value: 0.8273809523809523 key: train_roc_auc value: [0.89189189 0.90810811 0.89758791 0.90027608 0.90294972 0.90568149 0.91641965 0.90565243 0.89757338 0.90024702] mean value: 0.9026387678000581 key: test_jcc value: [0.72 0.66666667 0.65384615 0.70833333 0.73076923 0.76 0.71428571 0.6 0.79166667 0.75 ] mean value: 0.7095567765567765 key: train_jcc value: [0.80392157 0.83084577 0.81463415 0.81862745 0.82178218 0.82843137 0.84729064 0.82843137 0.81463415 0.82038835] mean value: 0.8228986996659561 key: TN value: 168 mean value: 168.0 key: FP value: 33 mean value: 33.0 key: FN value: 38 mean value: 38.0 key: TP value: 173 mean value: 173.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.55 Accuracy on Blind test: 0.8 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.29383063 1.44016743 1.28876901 1.44384265 1.41407895 1.30052304 1.70417809 1.71098447 1.61445165 1.84017086] mean value: 1.5050996780395507 key: score_time value: [0.01451826 0.01390624 0.01395798 0.01398897 0.01401591 0.01408267 0.01418805 0.01403236 0.02189183 0.01375771] mean value: 0.014833998680114747 key: test_mcc value: [0.95346259 0.81322028 0.86333169 0.80907152 0.90649828 0.8547619 0.86240942 0.65871309 0.95238095 0.85441771] mean value: 0.852826743808685 key: train_mcc value: [1. 0.99460913 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9994609125148145 key: test_fscore value: [0.97674419 0.90909091 0.92307692 0.9 0.95454545 0.92682927 0.91891892 0.82051282 0.97560976 0.92307692] mean value: 0.9228405159658705 key: train_fscore value: [1. 0.99730458 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9997304582210242 key: test_precision value: [0.95454545 0.86956522 1. 0.94736842 0.91304348 0.95 1. 0.84210526 0.95238095 0.94736842] mean value: 0.9376377207841738 key: train_precision value: [1. 0.99462366 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9994623655913978 key: test_recall value: [1. 0.95238095 0.85714286 0.85714286 1. 0.9047619 0.85 0.8 1. 0.9 ] mean value: 0.9121428571428571 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97619048 0.9047619 0.92682927 0.90243902 0.95121951 0.92682927 0.92682927 0.82926829 0.97560976 0.92682927] mean value: 0.9246806039488968 key: train_accuracy value: [1. 0.9972973 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9997297297297297 key: test_roc_auc value: [0.97619048 0.9047619 0.92857143 0.90357143 0.95 0.92738095 0.925 0.82857143 0.97619048 0.92619048] mean value: 0.924642857142857 key: train_roc_auc value: [1. 0.9972973 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9997297297297297 key: test_jcc value: [0.95454545 0.83333333 0.85714286 0.81818182 0.91304348 0.86363636 0.85 0.69565217 0.95238095 0.85714286] mean value: 0.859505928853755 key: train_jcc value: [1. 0.99462366 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9994623655913978 key: TN value: 193 mean value: 193.0 key: FP value: 18 mean value: 18.0 key: FN value: 13 mean value: 13.0 key: TP value: 188 mean value: 188.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.59 Accuracy on Blind test: 0.8 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02476811 0.02039862 0.01846981 0.01775837 0.0182929 0.01789904 0.01828575 0.01807499 0.01761913 0.01760459] mean value: 0.018917131423950195 key: score_time value: [0.0121634 0.00941205 0.00896049 0.00882649 0.00887442 0.00888562 0.00931406 0.00897431 0.00980282 0.00914001] mean value: 0.009435367584228516 key: test_mcc value: [0.82462113 0.95346259 1. 0.70714286 0.90692382 0.8547619 0.86240942 0.95238095 0.90238095 0.90238095] mean value: 0.8866464572934346 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.89473684 0.97674419 1. 0.85714286 0.95 0.92682927 0.91891892 0.97560976 0.95 0.95 ] mean value: 0.9399981828603794 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.95454545 1. 0.85714286 1. 0.95 1. 0.95238095 0.95 0.95 ] mean value: 0.9614069264069263 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.80952381 1. 1. 0.85714286 0.9047619 0.9047619 0.85 1. 0.95 0.95 ] mean value: 0.9226190476190477 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9047619 0.97619048 1. 0.85365854 0.95121951 0.92682927 0.92682927 0.97560976 0.95121951 0.95121951] mean value: 0.9417537746806038 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9047619 0.97619048 1. 0.85357143 0.95238095 0.92738095 0.925 0.97619048 0.95119048 0.95119048] mean value: 0.9417857142857142 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.80952381 0.95454545 1. 0.75 0.9047619 0.86363636 0.85 0.95238095 0.9047619 0.9047619 ] mean value: 0.8894372294372295 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 198 mean value: 198.0 key: FP value: 16 mean value: 16.0 key: FN value: 8 mean value: 8.0 key: TP value: 190 mean value: 190.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.86 Accuracy on Blind test: 0.94 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.11411691 0.11291766 0.11341381 0.113482 0.11605811 0.11524677 0.1146853 0.11328244 0.1139667 0.11258531] mean value: 0.11397550106048585 key: score_time value: [0.01812983 0.01800847 0.01816654 0.01818466 0.01819277 0.0194211 0.01798368 0.01814198 0.01803756 0.01890779] mean value: 0.018317437171936034 key: test_mcc value: [0.76277007 0.80952381 0.65871309 0.75714286 0.8547619 0.7098505 0.7197263 0.70714286 0.85441771 0.7565654 ] mean value: 0.7590614509822682 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.88372093 0.9047619 0.8372093 0.87804878 0.92682927 0.86363636 0.83333333 0.85 0.92307692 0.87179487] mean value: 0.8772411677942025 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.86363636 0.9047619 0.81818182 0.9 0.95 0.82608696 0.9375 0.85 0.94736842 0.89473684] mean value: 0.8892272306259722 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.9047619 0.9047619 0.85714286 0.85714286 0.9047619 0.9047619 0.75 0.85 0.9 0.85 ] mean value: 0.8683333333333334 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.88095238 0.9047619 0.82926829 0.87804878 0.92682927 0.85365854 0.85365854 0.85365854 0.92682927 0.87804878] mean value: 0.8785714285714284 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.88095238 0.9047619 0.82857143 0.87857143 0.92738095 0.85238095 0.85119048 0.85357143 0.92619048 0.87738095] mean value: 0.8780952380952382 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.79166667 0.82608696 0.72 0.7826087 0.86363636 0.76 0.71428571 0.73913043 0.85714286 0.77272727] mean value: 0.7827284961415396 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 183 mean value: 183.0 key: FP value: 27 mean value: 27.0 key: FN value: 23 mean value: 23.0 key: TP value: 179 mean value: 179.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.59 Accuracy on Blind test: 0.82 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00981283 0.010782 0.0110085 0.01046014 0.00998545 0.00999331 0.00993228 0.00992513 0.0098927 0.00979471] mean value: 0.010158705711364745 key: score_time value: [0.00894189 0.00929785 0.00908732 0.00908136 0.00908279 0.00907326 0.00922275 0.00893164 0.00892639 0.0089097 ] mean value: 0.009055495262145996 key: test_mcc value: [0.47673129 0.48112522 0.38060103 0.75714286 0.46623254 0.21823107 0.31960727 0.44466675 0.37171226 0.16945156] mean value: 0.40855018487405237 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.74418605 0.75555556 0.64864865 0.87804878 0.75555556 0.63636364 0.61111111 0.625 0.69767442 0.56410256] mean value: 0.6916246316941156 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.72727273 0.70833333 0.75 0.9 0.70833333 0.60869565 0.6875 0.83333333 0.65217391 0.57894737] mean value: 0.7154589660911173 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.76190476 0.80952381 0.57142857 0.85714286 0.80952381 0.66666667 0.55 0.5 0.75 0.55 ] mean value: 0.6826190476190476 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.73809524 0.73809524 0.68292683 0.87804878 0.73170732 0.6097561 0.65853659 0.70731707 0.68292683 0.58536585] mean value: 0.7012775842044133 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.73809524 0.73809524 0.68571429 0.87857143 0.7297619 0.60833333 0.65595238 0.70238095 0.68452381 0.58452381] mean value: 0.7005952380952382 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.59259259 0.60714286 0.48 0.7826087 0.60714286 0.46666667 0.44 0.45454545 0.53571429 0.39285714] mean value: 0.535927055231403 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 148 mean value: 148.0 key: FP value: 65 mean value: 65.0 key: FN value: 58 mean value: 58.0 key: TP value: 141 mean value: 141.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.12 Accuracy on Blind test: 0.59 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.66840959 1.6835866 1.67349887 1.60515428 1.62686896 1.62213206 1.61394548 1.60469913 1.62373829 1.62986398] mean value: 1.6351897239685058 key: score_time value: [0.15618062 0.0988071 0.09145093 0.09110022 0.09156513 0.09141111 0.09112048 0.09319663 0.0919323 0.09166527] mean value: 0.09884297847747803 key: test_mcc value: [0.90889326 1. 0.80817439 0.90649828 1. 0.80817439 0.90238095 0.8547619 0.90692382 0.95238095] mean value: 0.9048187955773696 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95454545 1. 0.90909091 0.95454545 1. 0.90909091 0.95 0.92682927 0.95238095 0.97560976] mean value: 0.9532092704043922 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.91304348 1. 0.86956522 0.91304348 1. 0.86956522 0.95 0.9047619 0.90909091 0.95238095] mean value: 0.9281451157538113 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.95238095 1. 1. 0.95238095 0.95 0.95 1. 1. ] mean value: 0.9804761904761904 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95238095 1. 0.90243902 0.95121951 1. 0.90243902 0.95121951 0.92682927 0.95121951 0.97560976] mean value: 0.9513356562137052 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95238095 1. 0.90119048 0.95 1. 0.90119048 0.95119048 0.92738095 0.95238095 0.97619048] mean value: 0.9511904761904763 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.91304348 1. 0.83333333 0.91304348 1. 0.83333333 0.9047619 0.86363636 0.90909091 0.95238095] mean value: 0.9122623753058535 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 190 mean value: 190.0 key: FP value: 4 mean value: 4.0 key: FN value: 16 mean value: 16.0 key: TP value: 202 mean value: 202.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.88 Accuracy on Blind test: 0.95 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.96576428 1.03775167 0.95333004 0.91306329 0.939183 0.94196749 0.9377737 0.94258761 0.93521285 0.94724011] mean value: 0.9513874053955078 key: score_time value: [0.22685552 0.20241451 0.17469525 0.20963097 0.21572113 0.19469857 0.20701981 0.23400688 0.21420932 0.19970131] mean value: 0.20789532661437987 key: test_mcc value: [0.90889326 0.95346259 0.80817439 0.80817439 0.90238095 0.7565654 0.85441771 0.8047619 0.86333169 0.90692382] mean value: 0.8567086116301835 key: train_mcc value: [0.98391316 0.97860715 0.98395676 0.98395676 0.97866529 0.97866529 0.97866283 0.98395537 0.97866283 0.9946235 ] mean value: 0.9823668931195029 key: test_fscore value: [0.95454545 0.97560976 0.90909091 0.90909091 0.95238095 0.88372093 0.92307692 0.9 0.93023256 0.95238095] mean value: 0.9290129345035755 key: train_fscore value: [0.9919571 0.98930481 0.9919571 0.9919571 0.98930481 0.98930481 0.9893617 0.992 0.9893617 0.99731903] mean value: 0.9911828191283462 key: test_precision value: [0.91304348 1. 0.86956522 0.86956522 0.95238095 0.86363636 0.94736842 0.9 0.86956522 0.90909091] mean value: 0.9094215776595638 key: train_precision value: [0.98404255 0.97883598 0.98404255 0.98404255 0.97883598 0.97883598 0.97894737 0.98412698 0.97894737 0.99465241] mean value: 0.9825309723468607 key: test_recall value: [1. 0.95238095 0.95238095 0.95238095 0.95238095 0.9047619 0.9 0.9 1. 1. ] mean value: 0.9514285714285714 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95238095 0.97619048 0.90243902 0.90243902 0.95121951 0.87804878 0.92682927 0.90243902 0.92682927 0.95121951] mean value: 0.9270034843205576 key: train_accuracy value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.99189189 0.98918919 0.99191375 0.99191375 0.98921833 0.98921833 0.98921833 0.99191375 0.98921833 0.99730458] mean value: 0.9911000218547388 key: test_roc_auc value: [0.95238095 0.97619048 0.90119048 0.90119048 0.95119048 0.87738095 0.92619048 0.90238095 0.92857143 0.95238095] mean value: 0.9269047619047619 key: train_roc_auc value: [0.99189189 0.98918919 0.99193548 0.99193548 0.98924731 0.98924731 0.98918919 0.99189189 0.98918919 0.9972973 ] mean value: 0.9911014240046498 key: test_jcc value: [0.91304348 0.95238095 0.83333333 0.83333333 0.90909091 0.79166667 0.85714286 0.81818182 0.86956522 0.90909091] mean value: 0.8686829474872952 key: train_jcc value: [0.98404255 0.97883598 0.98404255 0.98404255 0.97883598 0.97883598 0.97894737 0.98412698 0.97894737 0.99465241] mean value: 0.9825309723468607 key: TN value: 186 mean value: 186.0 key: FP value: 10 mean value: 10.0 key: FN value: 20 mean value: 20.0 key: TP value: 196 mean value: 196.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.87 Accuracy on Blind test: 0.94 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.10359287 0.05772686 0.06652761 0.057899 0.05960846 0.05904317 0.06005931 0.20866203 0.05684566 0.05950522] mean value: 0.07894701957702636 key: score_time value: [0.01138711 0.01046109 0.01090193 0.01055264 0.01067448 0.0106988 0.01056552 0.01120353 0.01074457 0.01049185] mean value: 0.01076815128326416 key: test_mcc value: [1. 0.95346259 0.95238095 0.85441771 0.95238095 0.95238095 0.90649828 0.86333169 0.90238095 0.95238095] mean value: 0.928961503294655 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.97674419 0.97560976 0.93023256 0.97560976 0.97560976 0.94736842 0.93023256 0.95 0.97560976] mean value: 0.9637016747768457 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.95454545 1. 0.90909091 1. 1. 1. 0.86956522 0.95 0.95238095] mean value: 0.963558253340862 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.95238095 0.95238095 0.95238095 0.95238095 0.9 1. 0.95 1. ] mean value: 0.9659523809523808 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.97619048 0.97560976 0.92682927 0.97560976 0.97560976 0.95121951 0.92682927 0.95121951 0.97560976] mean value: 0.963472706155633 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.97619048 0.97619048 0.92619048 0.97619048 0.97619048 0.95 0.92857143 0.95119048 0.97619048] mean value: 0.9636904761904761 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.95454545 0.95238095 0.86956522 0.95238095 0.95238095 0.9 0.86956522 0.9047619 0.95238095] mean value: 0.9307961603613778 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 198 mean value: 198.0 key: FP value: 7 mean value: 7.0 key: FN value: 8 mean value: 8.0 key: TP value: 199 mean value: 199.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.9 Accuracy on Blind test: 0.95 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03956175 0.06763196 0.06599092 0.04733467 0.07237792 0.04720092 0.08412266 0.0399518 0.10612369 0.05623484] mean value: 0.06265311241149903 key: score_time value: [0.02280021 0.02403569 0.02058625 0.01286125 0.01242781 0.02172351 0.0125792 0.02332878 0.03291178 0.02500701] mean value: 0.020826148986816406 key: test_mcc value: [1. 0.90889326 0.76500781 0.80907152 0.8547619 0.8547619 0.90649828 0.60952381 0.90238095 0.7098505 ] mean value: 0.8320749938182688 key: train_mcc value: [0.97843556 0.98379816 0.98384191 0.98384144 0.9784365 0.98384191 0.97305937 0.97306016 0.96227841 0.98384191] mean value: 0.9784435309419832 key: test_fscore value: [1. 0.95 0.87179487 0.9 0.92682927 0.92682927 0.94736842 0.8 0.95 0.84210526] mean value: 0.9114927092590766 key: train_fscore value: [0.98924731 0.99191375 0.99191375 0.99186992 0.98918919 0.99191375 0.98659517 0.98652291 0.98113208 0.99191375] mean value: 0.9892211567024891 key: test_precision value: [1. 1. 0.94444444 0.94736842 0.95 0.95 1. 0.8 0.95 0.88888889] mean value: 0.9430701754385964 key: train_precision value: [0.98395722 0.98924731 0.98924731 0.99456522 0.98918919 0.98924731 0.98395722 0.98918919 0.98378378 0.99459459] mean value: 0.9886978348134606 key: test_recall value: [1. 0.9047619 0.80952381 0.85714286 0.9047619 0.9047619 0.9 0.8 0.95 0.8 ] mean value: 0.883095238095238 key: train_recall value: [0.99459459 0.99459459 0.99459459 0.98918919 0.98918919 0.99459459 0.98924731 0.98387097 0.97849462 0.98924731] mean value: 0.9897616971810521 key: test_accuracy value: [1. 0.95238095 0.87804878 0.90243902 0.92682927 0.92682927 0.95121951 0.80487805 0.95121951 0.85365854] mean value: 0.9147502903600465 key: train_accuracy value: [0.98918919 0.99189189 0.99191375 0.99191375 0.98921833 0.99191375 0.98652291 0.98652291 0.98113208 0.99191375] mean value: 0.9892132294019087 key: test_roc_auc value: [1. 0.95238095 0.8797619 0.90357143 0.92738095 0.92738095 0.95 0.8047619 0.95119048 0.85238095] mean value: 0.9148809523809524 key: train_roc_auc value: [0.98918919 0.99189189 0.99192095 0.99190642 0.98921825 0.99192095 0.98651555 0.98653008 0.9811392 0.99192095] mean value: 0.9892153443766347 key: test_jcc value: [1. 0.9047619 0.77272727 0.81818182 0.86363636 0.86363636 0.9 0.66666667 0.9047619 0.72727273] mean value: 0.8421645021645021 key: train_jcc value: [0.9787234 0.98395722 0.98395722 0.98387097 0.97860963 0.98395722 0.97354497 0.97340426 0.96296296 0.98395722] mean value: 0.9786945066498138 key: TN value: 195 mean value: 195.0 key: FP value: 24 mean value: 24.0 key: FN value: 11 mean value: 11.0 key: TP value: 182 mean value: 182.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.77 Accuracy on Blind test: 0.9 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01337481 0.01335168 0.00990558 0.00938869 0.01046014 0.01006222 0.00962472 0.00944638 0.00971985 0.0095315 ] mean value: 0.010486555099487305 key: score_time value: [0.01207018 0.01088357 0.00918961 0.00873971 0.00949311 0.00893283 0.00898933 0.00877428 0.00887108 0.00888586] mean value: 0.009482955932617188 key: test_mcc value: [0.38138504 0.43052839 0.16909989 0.12803277 0.56190476 0.51320273 0.31960727 0.16945156 0.47003614 0.41428571] mean value: 0.355753425845874 key: train_mcc value: [0.37851665 0.42162778 0.48790839 0.46638766 0.43403959 0.47709968 0.4555575 0.46631793 0.41295769 0.48248903] mean value: 0.4482901898810191 key: test_fscore value: [0.69767442 0.72727273 0.62222222 0.52631579 0.7804878 0.77272727 0.61111111 0.56410256 0.74418605 0.7 ] mean value: 0.6746099956903909 key: train_fscore value: [0.69333333 0.7115903 0.74114441 0.73458445 0.71849866 0.73854447 0.73066667 0.73315364 0.69972452 0.74331551] mean value: 0.724455595971974 key: test_precision value: [0.68181818 0.69565217 0.58333333 0.58823529 0.8 0.73913043 0.6875 0.57894737 0.69565217 0.7 ] mean value: 0.675026896029891 key: train_precision value: [0.68421053 0.70967742 0.74725275 0.7287234 0.71276596 0.73655914 0.72486772 0.73513514 0.71751412 0.7393617 ] mean value: 0.7236067880834754 key: test_recall value: [0.71428571 0.76190476 0.66666667 0.47619048 0.76190476 0.80952381 0.55 0.55 0.8 0.7 ] mean value: 0.6790476190476191 key: train_recall value: [0.7027027 0.71351351 0.73513514 0.74054054 0.72432432 0.74054054 0.73655914 0.7311828 0.6827957 0.74731183] mean value: 0.7254606219122348 key: test_accuracy value: [0.69047619 0.71428571 0.58536585 0.56097561 0.7804878 0.75609756 0.65853659 0.58536585 0.73170732 0.70731707] mean value: 0.6770615563298489 key: train_accuracy value: [0.68918919 0.71081081 0.74393531 0.73315364 0.71698113 0.73854447 0.7277628 0.73315364 0.70619946 0.74123989] mean value: 0.7240970350404312 key: test_roc_auc value: [0.69047619 0.71428571 0.58333333 0.56309524 0.78095238 0.7547619 0.65595238 0.58452381 0.73333333 0.70714286] mean value: 0.6767857142857143 key: train_roc_auc value: [0.68918919 0.71081081 0.74391165 0.7331735 0.71700087 0.73854984 0.72773903 0.73315897 0.70626271 0.74122348] mean value: 0.7241020052310374 key: test_jcc value: [0.53571429 0.57142857 0.4516129 0.35714286 0.64 0.62962963 0.44 0.39285714 0.59259259 0.53846154] mean value: 0.5149439521052425 key: train_jcc value: [0.53061224 0.55230126 0.58874459 0.58050847 0.56066946 0.58547009 0.57563025 0.5787234 0.53813559 0.59148936] mean value: 0.5682284716264602 key: TN value: 139 mean value: 139.0 key: FP value: 66 mean value: 66.0 key: FN value: 67 mean value: 67.0 key: TP value: 140 mean value: 140.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.43 Accuracy on Blind test: 0.72 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01812387 0.01968002 0.02329946 0.02299213 0.02618003 0.02688098 0.02359438 0.02325869 0.02430868 0.02280593] mean value: 0.02311241626739502 key: score_time value: [0.00875401 0.01142693 0.01191044 0.01212454 0.01197791 0.01205945 0.0119586 0.0119884 0.01208591 0.0119648 ] mean value: 0.011625099182128906 key: test_mcc value: [1. 0.95346259 0.95238095 0.90238095 1. 0.95238095 0.90649828 0.76500781 0.95238095 0.90238095] mean value: 0.9286873437646441 key: train_mcc value: [0.97837838 0.97310093 0.98927606 0.978494 0.98384191 0.98384191 0.9784365 0.98927544 0.9784365 0.97317407] mean value: 0.9806255690715325 key: test_fscore value: [1. 0.97560976 0.97560976 0.95238095 1. 0.97560976 0.94736842 0.88372093 0.97560976 0.95 ] mean value: 0.9635909328056386 key: train_fscore value: [0.98918919 0.98659517 0.99462366 0.98924731 0.99191375 0.99191375 0.98924731 0.99465241 0.98924731 0.98644986] mean value: 0.9903079719026987 key: test_precision value: [1. 1. 1. 0.95238095 1. 1. 1. 0.82608696 0.95238095 0.95 ] mean value: 0.9680848861283643 key: train_precision value: [0.98918919 0.9787234 0.98930481 0.98395722 0.98924731 0.98924731 0.98924731 0.9893617 0.98924731 0.99453552] mean value: 0.9882061094095242 key: test_recall value: [1. 0.95238095 0.95238095 0.95238095 1. 0.95238095 0.9 0.95 1. 0.95 ] mean value: 0.9609523809523808 key: train_recall value: [0.98918919 0.99459459 1. 0.99459459 0.99459459 0.99459459 0.98924731 1. 0.98924731 0.97849462] mean value: 0.9924556814879397 key: test_accuracy value: [1. 0.97619048 0.97560976 0.95121951 1. 0.97560976 0.95121951 0.87804878 0.97560976 0.95121951] mean value: 0.963472706155633 key: train_accuracy value: [0.98918919 0.98648649 0.99460916 0.98921833 0.99191375 0.99191375 0.98921833 0.99460916 0.98921833 0.98652291] mean value: 0.9902899395352224 key: test_roc_auc value: [1. 0.97619048 0.97619048 0.95119048 1. 0.97619048 0.95 0.8797619 0.97619048 0.95119048] mean value: 0.9636904761904761 key: train_roc_auc value: [0.98918919 0.98648649 0.99462366 0.98923278 0.99192095 0.99192095 0.98921825 0.99459459 0.98921825 0.98654461] mean value: 0.9902949723917466 key: test_jcc value: [1. 0.95238095 0.95238095 0.90909091 1. 0.95238095 0.9 0.79166667 0.95238095 0.9047619 ] mean value: 0.931504329004329 key: train_jcc value: [0.97860963 0.97354497 0.98930481 0.9787234 0.98395722 0.98395722 0.9787234 0.9893617 0.9787234 0.97326203] mean value: 0.9808167797529499 key: TN value: 199 mean value: 199.0 key: FP value: 8 mean value: 8.0 key: FN value: 7 mean value: 7.0 key: TP value: 198 mean value: 198.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.81 Accuracy on Blind test: 0.92 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01709509 0.01672006 0.01696658 0.01680875 0.01753879 0.01735544 0.01576948 0.01762867 0.01622939 0.01648998] mean value: 0.016860222816467284 key: score_time value: [0.01177645 0.01180029 0.01178408 0.01206207 0.01212597 0.01212263 0.01181698 0.01177001 0.0121057 0.01177335] mean value: 0.011913752555847168 key: test_mcc value: [0.8660254 0.82462113 0.74124932 0.90238095 0.95238095 0.85441771 0.90238095 0.7565654 0.86333169 0.67700771] mean value: 0.8340361214926171 key: train_mcc value: [0.84285241 0.87540087 0.90229328 0.978494 0.92586351 0.90487588 0.96294605 0.97317407 0.90722239 0.80791198] mean value: 0.908103443769595 key: test_fscore value: [0.92307692 0.89473684 0.83333333 0.95238095 0.97560976 0.93023256 0.95 0.87179487 0.93023256 0.8 ] mean value: 0.9061397795067976 key: train_fscore value: [0.90909091 0.93142857 0.94586895 0.98924731 0.96111111 0.95287958 0.98153034 0.98644986 0.95384615 0.88288288] mean value: 0.9494335674714925 key: test_precision value: [1. 1. 1. 0.95238095 1. 0.90909091 0.95 0.89473684 0.86956522 0.93333333] mean value: 0.9509107254301762 key: train_precision value: [0.99358974 0.98787879 1. 0.98395722 0.98857143 0.92385787 0.96373057 0.99453552 0.91176471 1. ] mean value: 0.9747885842267824 key: test_recall value: [0.85714286 0.80952381 0.71428571 0.95238095 0.95238095 0.95238095 0.95 0.85 1. 0.7 ] mean value: 0.8738095238095237 key: train_recall value: [0.83783784 0.88108108 0.8972973 0.99459459 0.93513514 0.98378378 1. 0.97849462 1. 0.79032258] mean value: 0.9298546934030807 key: test_accuracy value: [0.92857143 0.9047619 0.85365854 0.95121951 0.97560976 0.92682927 0.95121951 0.87804878 0.92682927 0.82926829] mean value: 0.9126016260162603 key: train_accuracy value: [0.91621622 0.93513514 0.94878706 0.98921833 0.96226415 0.95148248 0.98113208 0.98652291 0.95148248 0.89487871] mean value: 0.9517119545421433 key: test_roc_auc value: [0.92857143 0.9047619 0.85714286 0.95119048 0.97619048 0.92619048 0.95119048 0.87738095 0.92857143 0.82619048] mean value: 0.9127380952380951 key: train_roc_auc value: [0.91621622 0.93513514 0.94864865 0.98923278 0.96219122 0.95156931 0.98108108 0.98654461 0.95135135 0.89516129] mean value: 0.9517131647776809 key: test_jcc value: [0.85714286 0.80952381 0.71428571 0.90909091 0.95238095 0.86956522 0.9047619 0.77272727 0.86956522 0.66666667] mean value: 0.8325710521362695 key: train_jcc value: [0.83333333 0.87165775 0.8972973 0.9787234 0.92513369 0.91 0.96373057 0.97326203 0.91176471 0.79032258] mean value: 0.9055225367297479 key: TN value: 196 mean value: 196.0 key: FP value: 26 mean value: 26.0 key: FN value: 10 mean value: 10.0 key: TP value: 180 mean value: 180.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.81 Accuracy on Blind test: 0.92 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.18844795 0.16931653 0.1712935 0.17028832 0.17033982 0.16963387 0.16861916 0.16729665 0.17221189 0.16930819] mean value: 0.17167558670043945 key: score_time value: [0.0153811 0.01510787 0.01513457 0.01511717 0.01520443 0.01527405 0.01510739 0.01514196 0.01515222 0.01510954] mean value: 0.015173029899597169 key: test_mcc value: [1. 0.95346259 1. 0.85441771 0.95238095 0.95238095 0.90238095 0.75714286 1. 0.95238095] mean value: 0.9324546963771422 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.97674419 1. 0.93023256 0.97560976 0.97560976 0.95 0.87804878 1. 0.97560976] mean value: 0.9661854792966533 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.95454545 1. 0.90909091 1. 1. 0.95 0.85714286 1. 0.95238095] mean value: 0.9623160173160172 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.95238095 0.95238095 0.95238095 0.95 0.9 1. 1. ] mean value: 0.9707142857142858 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.97619048 1. 0.92682927 0.97560976 0.97560976 0.95121951 0.87804878 1. 0.97560976] mean value: 0.9659117305458768 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.97619048 1. 0.92619048 0.97619048 0.97619048 0.95119048 0.87857143 1. 0.97619048] mean value: 0.9660714285714287 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.95454545 1. 0.86956522 0.95238095 0.95238095 0.9047619 0.7826087 1. 0.95238095] mean value: 0.9368624129493694 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 198 mean value: 198.0 key: FP value: 6 mean value: 6.0 key: FN value: 8 mean value: 8.0 key: TP value: 200 mean value: 200.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.87 Accuracy on Blind test: 0.94 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.04259586 0.0584991 0.04867768 0.05368423 0.04338527 0.05776286 0.04904056 0.05466557 0.06025934 0.04896426] mean value: 0.05175347328186035 key: score_time value: [0.02193522 0.02411842 0.02880406 0.02554083 0.02484798 0.02451158 0.0243988 0.0265336 0.03643608 0.02784085] mean value: 0.026496744155883788 key: test_mcc value: [0.90889326 0.9047619 0.95238095 0.81975606 0.90692382 0.90238095 0.90649828 0.95238095 0.8547619 0.95238095] mean value: 0.906111904278896 key: train_mcc value: [0.98918919 0.989247 0.98921825 0.98927544 0.99462366 0.98921825 1. 0.9946235 1. 0.99462366] mean value: 0.9930018943340556 key: test_fscore value: [0.95 0.95238095 0.97560976 0.91304348 0.95 0.95238095 0.94736842 0.97560976 0.92682927 0.97560976] mean value: 0.9518832340660772 key: train_fscore value: [0.99459459 0.99462366 0.99459459 0.99456522 0.99730458 0.99459459 1. 0.99731903 1. 0.99730458] mean value: 0.9964900856362098 key: test_precision value: [1. 0.95238095 1. 0.84 1. 0.95238095 1. 0.95238095 0.9047619 0.95238095] mean value: 0.9554285714285715 key: train_precision value: [0.99459459 0.98930481 0.99459459 1. 0.99462366 0.99459459 1. 0.99465241 1. 1. ] mean value: 0.9962364658949099 key: test_recall value: [0.9047619 0.95238095 0.95238095 1. 0.9047619 0.95238095 0.9 1. 0.95 1. ] mean value: 0.9516666666666665 key: train_recall value: [0.99459459 1. 0.99459459 0.98918919 1. 0.99459459 1. 1. 1. 0.99462366] mean value: 0.9967596628886952 key: test_accuracy value: [0.95238095 0.95238095 0.97560976 0.90243902 0.95121951 0.95121951 0.95121951 0.97560976 0.92682927 0.97560976] mean value: 0.951451800232288 key: train_accuracy value: [0.99459459 0.99459459 0.99460916 0.99460916 0.99730458 0.99460916 1. 0.99730458 1. 0.99730458] mean value: 0.9964930429081372 key: test_roc_auc value: [0.95238095 0.95238095 0.97619048 0.9 0.95238095 0.95119048 0.95 0.97619048 0.92738095 0.97619048] mean value: 0.9514285714285714 key: train_roc_auc value: [0.99459459 0.99459459 0.99460913 0.99459459 0.99731183 0.99460913 1. 0.9972973 1. 0.99731183] mean value: 0.9964922987503634 key: test_jcc value: [0.9047619 0.90909091 0.95238095 0.84 0.9047619 0.90909091 0.9 0.95238095 0.86363636 0.95238095] mean value: 0.9088484848484848 key: train_jcc value: [0.98924731 0.98930481 0.98924731 0.98918919 0.99462366 0.98924731 1. 0.99465241 1. 0.99462366] mean value: 0.9930135655752353 key: TN value: 196 mean value: 196.0 key: FP value: 10 mean value: 10.0 key: FN value: 10 mean value: 10.0 key: TP value: 196 mean value: 196.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.9 Accuracy on Blind test: 0.95 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.12224102 0.10535049 0.1016345 0.0726614 0.08337069 0.09313035 0.13533998 0.11137009 0.06969023 0.06248879] mean value: 0.09572775363922119 key: score_time value: [0.02381134 0.01407385 0.02311468 0.01469684 0.02323651 0.01405168 0.02257681 0.028687 0.01425099 0.01401615] mean value: 0.019251585006713867 key: test_mcc value: [0.57735027 0.57207755 0.31666667 0.37171226 0.70714286 0.61152662 0.53206577 0.51320273 0.65871309 0.51190476] mean value: 0.5372362570684607 key: train_mcc value: [0.93039599 0.93039599 0.91978391 0.89264025 0.90846996 0.92500526 0.91926062 0.91947678 0.90901177 0.91423181] mean value: 0.9168672355180574 key: test_fscore value: [0.76923077 0.7804878 0.66666667 0.66666667 0.85714286 0.81818182 0.70588235 0.73684211 0.82051282 0.75 ] mean value: 0.7571613861483981 key: train_fscore value: [0.96418733 0.96418733 0.95867769 0.94505495 0.95367847 0.96153846 0.95934959 0.95912807 0.95342466 0.95628415] mean value: 0.9575510691736385 key: test_precision value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") [0.83333333 0.8 0.66666667 0.72222222 0.85714286 0.7826087 0.85714286 0.77777778 0.84210526 0.75 ] mean value: 0.7888999673095782 key: train_precision value: [0.98314607 0.98314607 0.97752809 0.96089385 0.96153846 0.97765363 0.96721311 0.97237569 0.97206704 0.97222222] mean value: 0.9727784238981283 key: test_recall value: [0.71428571 0.76190476 0.66666667 0.61904762 0.85714286 0.85714286 0.6 0.7 0.8 0.75 ] mean value: 0.7326190476190476 key: train_recall value: [0.94594595 0.94594595 0.94054054 0.92972973 0.94594595 0.94594595 0.9516129 0.94623656 0.93548387 0.94086022] mean value: 0.942824760244115 key: test_accuracy value: [0.78571429 0.78571429 0.65853659 0.68292683 0.85365854 0.80487805 0.75609756 0.75609756 0.82926829 0.75609756] mean value: 0.7668989547038327 key: train_accuracy value: [0.96486486 0.96486486 0.95956873 0.94609164 0.9541779 0.96226415 0.95956873 0.95956873 0.9541779 0.95687332] mean value: 0.9582020834851024 key: test_roc_auc value: [0.78571429 0.78571429 0.65833333 0.68452381 0.85357143 0.80357143 0.75238095 0.7547619 0.82857143 0.75595238] mean value: 0.7663095238095238 key: train_roc_auc value: [0.96486486 0.96486486 0.95951758 0.94604766 0.95415577 0.96222028 0.95959024 0.95960477 0.95422842 0.95691659] mean value: 0.9582011043301366 key: test_jcc value: [0.625 0.64 0.5 0.5 0.75 0.69230769 0.54545455 0.58333333 0.69565217 0.6 ] mean value: 0.6131747745008613 key: train_jcc value: [0.93085106 0.93085106 0.92063492 0.89583333 0.91145833 0.92592593 0.921875 0.92146597 0.91099476 0.91623037] mean value: 0.9186120740363529 key: TN value: 165 mean value: 165.0 key: FP value: 55 mean value: 55.0 key: FN value: 41 mean value: 41.0 key: TP value: 151 mean value: 151.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.34 Accuracy on Blind test: 0.7 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.67548418 0.65576053 0.66834664 0.65980339 0.65404487 0.61867952 0.65014267 0.65180707 0.64954734 0.66304302] mean value: 0.6546659231185913 key: score_time value: [0.0093596 0.00954175 0.00971174 0.00954843 0.00953078 0.00948763 0.01077414 0.00926828 0.00934982 0.00933218] mean value: 0.009590435028076171 key: test_mcc value: [1. 0.9047619 1. 0.86240942 0.95238095 0.95238095 0.90649828 0.95238095 0.90238095 0.95238095] mean value: 0.9385574361533285 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.95238095 1. 0.93333333 0.97560976 0.97560976 0.94736842 0.97560976 0.95 0.97560976] mean value: 0.968552173115716 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.95238095 1. 0.875 1. 1. 1. 0.95238095 0.95 0.95238095] mean value: 0.9682142857142857 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.95238095 1. 1. 0.95238095 0.95238095 0.9 1. 0.95 1. ] mean value: 0.9707142857142858 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.95238095 1. 0.92682927 0.97560976 0.97560976 0.95121951 0.97560976 0.95121951 0.97560976] mean value: 0.9684088269454122 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.95238095 1. 0.925 0.97619048 0.97619048 0.95 0.97619048 0.95119048 0.97619048] mean value: 0.9683333333333332 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.90909091 1. 0.875 0.95238095 0.95238095 0.9 0.95238095 0.9047619 0.95238095] mean value: 0.9398376623376624 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 199 mean value: 199.0 key: FP value: 6 mean value: 6.0 key: FN value: 7 mean value: 7.0 key: TP value: 200 mean value: 200.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.91 Accuracy on Blind test: 0.96 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02539349 0.02820396 0.02839875 0.02784157 0.02765083 0.02783728 0.02735615 0.02679372 0.02673268 0.02854919] mean value: 0.027475762367248534 key: score_time value: [0.01260948 0.01275754 0.01296639 0.01299047 0.01315665 0.01306176 0.01306844 0.01392341 0.01316857 0.01312613] mean value: 0.0130828857421875 key: test_mcc value: [0.8660254 0.71428571 0.59335232 0.7633652 0.56190476 0.73786479 0.62325386 0.56086079 0.76500781 0.7098505 ] mean value: 0.6895771149319809 key: train_mcc value: [0.88737794 0.88252261 0.95692987 0.92023091 0.92993316 0.95215551 0.92586351 0.940826 0.95261005 0.91637608] mean value: 0.9264825641731319 key: test_fscore value: [0.92307692 0.85714286 0.81632653 0.88888889 0.7804878 0.875 0.81818182 0.76923077 0.88372093 0.84210526] mean value: 0.8454161785402003 key: train_fscore value: [0.93678161 0.93371758 0.97849462 0.96042216 0.96495957 0.97520661 0.96335079 0.9701897 0.97520661 0.95530726] mean value: 0.9613636517371242 key: test_precision value: [1. 0.85714286 0.71428571 0.83333333 0.8 0.77777778 0.75 0.78947368 0.82608696 0.88888889] mean value: 0.8236989212160838 key: train_precision value: [1. 1. 0.97326203 0.93814433 0.96236559 0.99438202 0.93877551 0.97814208 1. 0.99418605] mean value: 0.9779257609070671 key: test_recall value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") [0.85714286 0.85714286 0.95238095 0.95238095 0.76190476 1. 0.9 0.75 0.95 0.8 ] mean value: 0.878095238095238 key: train_recall value: [0.88108108 0.87567568 0.98378378 0.98378378 0.96756757 0.95675676 0.98924731 0.96236559 0.9516129 0.91935484] mean value: 0.947122929380994 key: test_accuracy value: [0.92857143 0.85714286 0.7804878 0.87804878 0.7804878 0.85365854 0.80487805 0.7804878 0.87804878 0.85365854] mean value: 0.8395470383275262 key: train_accuracy value: [0.94054054 0.93783784 0.97843666 0.95956873 0.96495957 0.97574124 0.96226415 0.9703504 0.97574124 0.95687332] mean value: 0.9622313688351424 key: test_roc_auc value: [0.92857143 0.85714286 0.77619048 0.87619048 0.78095238 0.85 0.80714286 0.7797619 0.8797619 0.85238095] mean value: 0.8388095238095239 key: train_roc_auc value: [0.94054054 0.93783784 0.97845103 0.95963383 0.96496658 0.97569021 0.96219122 0.97037198 0.97580645 0.95697472] mean value: 0.9622464399883756 key: test_jcc value: [0.85714286 0.75 0.68965517 0.8 0.64 0.77777778 0.69230769 0.625 0.79166667 0.72727273] mean value: 0.7350822893581515 key: train_jcc value: [0.88108108 0.87567568 0.95789474 0.92385787 0.93229167 0.9516129 0.92929293 0.94210526 0.9516129 0.9144385 ] mean value: 0.9259863529862067 key: TN value: 171 mean value: 171.0 key: FP value: 32 mean value: 32.0 key: FN value: 35 mean value: 35.0 key: TP value: 174 mean value: 174.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.13 Accuracy on Blind test: 0.61 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02332497 0.03366351 0.03370738 0.03406048 0.03355503 0.03292322 0.03243351 0.03156877 0.05635238 0.03871083] mean value: 0.03503000736236572 key: score_time value: [0.02165461 0.02171969 0.02165103 0.02171063 0.02163982 0.02164435 0.02162695 0.02164865 0.02405787 0.02173615] mean value: 0.02190897464752197 key: test_mcc value: [1. 0.9047619 0.90692382 0.8547619 0.90238095 0.95238095 0.90649828 0.60952381 0.95238095 0.95238095] mean value: 0.8941993531889617 key: train_mcc value: [0.96762411 0.96228869 0.96771194 0.97306016 0.956873 0.97317407 0.96771006 0.9784365 0.96771006 0.97305937] mean value: 0.9687647972123978 key: test_fscore value: [1. 0.95238095 0.95 0.92682927 0.95238095 0.97560976 0.94736842 0.8 0.97560976 0.97560976] mean value: 0.9455788862399903 key: train_fscore value: [0.98387097 0.98123324 0.98387097 0.98652291 0.97837838 0.98659517 0.98395722 0.98924731 0.98395722 0.98659517] mean value: 0.9844228567737392 key: test_precision value: [1. 0.95238095 1. 0.95 0.95238095 1. 1. 0.8 0.95238095 0.95238095] mean value: 0.955952380952381 key: train_precision value: [0.97860963 0.97340426 0.97860963 0.98387097 0.97837838 0.9787234 0.9787234 0.98924731 0.9787234 0.98395722] mean value: 0.9802247596621612 key: test_recall value: [1. 0.95238095 0.9047619 0.9047619 0.95238095 0.95238095 0.9 0.8 1. 1. ] mean value: 0.9366666666666668 key: train_recall value: [0.98918919 0.98918919 0.98918919 0.98918919 0.97837838 0.99459459 0.98924731 0.98924731 0.98924731 0.98924731] mean value: 0.9886718977041558 key: test_accuracy value: [1. 0.95238095 0.95121951 0.92682927 0.95121951 0.97560976 0.95121951 0.80487805 0.97560976 0.97560976] mean value: 0.9464576074332172 key: train_accuracy value: [0.98378378 0.98108108 0.98382749 0.98652291 0.97843666 0.98652291 0.98382749 0.98921833 0.98382749 0.98652291] mean value: 0.9843571064325781 key: test_roc_auc value: [1. 0.95238095 0.95238095 0.92738095 0.95119048 0.97619048 0.95 0.8047619 0.97619048 0.97619048] mean value: 0.9466666666666667 key: train_roc_auc value: [0.98378378 0.98108108 0.98384191 0.98653008 0.9784365 0.98654461 0.98381285 0.98921825 0.98381285 0.98651555] mean value: 0.9843577448416159 key: test_jcc value: [1. 0.90909091 0.9047619 0.86363636 0.90909091 0.95238095 0.9 0.66666667 0.95238095 0.95238095] mean value: 0.9010389610389611 key: train_jcc value: [0.96825397 0.96315789 0.96825397 0.97340426 0.95767196 0.97354497 0.96842105 0.9787234 0.96842105 0.97354497] mean value: 0.969339750084431 key: TN value: 197 mean value: 197.0 key: FP value: 13 mean value: 13.0 key: FN value: 9 mean value: 9.0 key: TP value: 193 mean value: 193.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.86 Accuracy on Blind test: 0.94 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.24888968 0.24928164 0.25829434 0.29429793 0.29187274 0.25702906 0.25375938 0.25846148 0.25591302 0.25412965] mean value: 0.26219289302825927 key: score_time value: [0.02372575 0.02388597 0.02393842 0.0240922 0.02259684 0.02371073 0.02040315 0.02021837 0.02421808 0.02299547] mean value: 0.022978496551513673 key: test_mcc value: [1. 0.9047619 0.90692382 0.8547619 0.90238095 0.95238095 0.90649828 0.65952381 0.95238095 0.95238095] mean value: 0.8991993531889616 key: train_mcc value: [0.96762411 0.96228869 0.96771194 0.97306016 0.956873 0.97317407 0.96771006 0.9784365 0.96771006 0.97305937] mean value: 0.9687647972123978 key: test_fscore value: [1. 0.95238095 0.95 0.92682927 0.95238095 0.97560976 0.94736842 0.82926829 0.97560976 0.97560976] mean value: 0.9485057155082831 key: train_fscore value: [0.98387097 0.98123324 0.98387097 0.98652291 0.97837838 0.98659517 0.98395722 0.98924731 0.98395722 0.98659517] mean value: 0.9844228567737392 key: test_precision value: [1. 0.95238095 1. 0.95 0.95238095 1. 1. 0.80952381 0.95238095 0.95238095] mean value: 0.9569047619047619 key: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:206: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:207: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_CV['Resampling'] = rs_smnc /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:212: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:213: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_BT['Resampling'] = rs_smnc /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( train_precision value: [0.97860963 0.97340426 0.97860963 0.98387097 0.97837838 0.9787234 0.9787234 0.98924731 0.9787234 0.98395722] mean value: 0.9802247596621612 key: test_recall value: [1. 0.95238095 0.9047619 0.9047619 0.95238095 0.95238095 0.9 0.85 1. 1. ] mean value: 0.9416666666666667 key: train_recall value: [0.98918919 0.98918919 0.98918919 0.98918919 0.97837838 0.99459459 0.98924731 0.98924731 0.98924731 0.98924731] mean value: 0.9886718977041558 key: test_accuracy value: [1. 0.95238095 0.95121951 0.92682927 0.95121951 0.97560976 0.95121951 0.82926829 0.97560976 0.97560976] mean value: 0.948896631823461 key: train_accuracy value: [0.98378378 0.98108108 0.98382749 0.98652291 0.97843666 0.98652291 0.98382749 0.98921833 0.98382749 0.98652291] mean value: 0.9843571064325781 key: test_roc_auc value: [1. 0.95238095 0.95238095 0.92738095 0.95119048 0.97619048 0.95 0.8297619 0.97619048 0.97619048] mean value: 0.9491666666666665 key: train_roc_auc value: [0.98378378 0.98108108 0.98384191 0.98653008 0.9784365 0.98654461 0.98381285 0.98921825 0.98381285 0.98651555] mean value: 0.9843577448416159 key: test_jcc value: [1. 0.90909091 0.9047619 0.86363636 0.90909091 0.95238095 0.9 0.70833333 0.95238095 0.95238095] mean value: 0.9052056277056277 key: train_jcc value: [0.96825397 0.96315789 0.96825397 0.97340426 0.95767196 0.97354497 0.96842105 0.9787234 0.96842105 0.97354497] mean value: 0.969339750084431 key: TN value: 197 mean value: 197.0 key: FP value: 12 mean value: 12.0 key: FN value: 9 mean value: 9.0 key: TP value: 194 mean value: 194.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.86 Accuracy on Blind test: 0.94 PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.03052306 0.03408241 0.03512359 0.03415132 0.03537679 0.03478885 0.03490472 0.03314066 0.03462148 0.06600475] mean value: 0.037271761894226076 key: score_time value: [0.01202464 0.01207113 0.01203728 0.01235628 0.01250768 0.01239419 0.01239467 0.01211047 0.01261497 0.01401401] mean value: 0.012452530860900878 key: test_mcc value: [0.95346259 0.90889326 0.7565654 0.76500781 0.95238095 0.86333169 0.80817439 0.60952381 0.8547619 0.8047619 ] mean value: 0.8276863714013631 key: train_mcc value: [0.91925472 0.94055428 0.90316864 0.91379661 0.89789222 0.92473841 0.92458368 0.91396351 0.91947678 0.91947678] mean value: 0.91769056480883 key: test_fscore value: [0.97674419 0.95 0.88372093 0.87179487 0.97560976 0.92307692 0.89473684 0.8 0.92682927 0.9 ] mean value: 0.9102512777646373 key: train_fscore value: [0.95890411 0.9701897 0.95081967 0.95652174 0.94794521 0.96174863 0.96216216 0.95652174 0.95912807 0.95912807] mean value: 0.9583069094189665 key: test_precision value: [0.95454545 1. 0.86363636 0.94444444 1. 1. 0.94444444 0.8 0.9047619 0.9 ] mean value: 0.9311832611832612 key: train_precision value: [0.97222222 0.97282609 0.96132597 0.96174863 0.96111111 0.97237569 0.9673913 0.96703297 0.97237569 0.97237569] mean value: 0.968078536422446 key: test_recall value: [1. 0.9047619 0.9047619 0.80952381 0.95238095 0.85714286 0.85 0.8 0.95 0.9 ] mean value: 0.8928571428571429 key: train_recall value: [0.94594595 0.96756757 0.94054054 0.95135135 0.93513514 0.95135135 0.95698925 0.94623656 0.94623656 0.94623656] mean value: 0.9487590816623076 key: test_accuracy value: [0.97619048 0.95238095 0.87804878 0.87804878 0.97560976 0.92682927 0.90243902 0.80487805 0.92682927 0.90243902] mean value: 0.9123693379790941 key: train_accuracy value: [0.95945946 0.97027027 0.95148248 0.95687332 0.94878706 0.96226415 0.96226415 0.95687332 0.95956873 0.95956873] mean value: 0.9587411670430537 key: test_roc_auc value: [0.97619048 0.95238095 0.87738095 0.8797619 0.97619048 0.92857143 0.90119048 0.8047619 0.92738095 0.90238095] mean value: 0.9126190476190474 key: train_roc_auc value: [0.95945946 0.97027027 0.95145307 0.95685847 0.94875036 0.96223482 0.96227841 0.95690206 0.95960477 0.95960477] mean value: 0.9587416448706773 key: test_jcc value: [0.95454545 0.9047619 0.79166667 0.77272727 0.95238095 0.85714286 0.80952381 0.66666667 0.86363636 0.81818182] mean value: 0.8391233766233765 key: train_jcc value: [0.92105263 0.94210526 0.90625 0.91666667 0.90104167 0.92631579 0.92708333 0.91666667 0.92146597 0.92146597] mean value: 0.9200113954716634 key: TN value: 192 mean value: 192.0 key: FP value: 22 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( mean value: 22.0 key: FN value: 14 mean value: 14.0 key: TP value: 184 mean value: 184.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.77 Accuracy on Blind test: 0.9 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.8054328 0.84636688 1.01926517 1.05817366 0.7309134 0.71366811 0.91611481 0.73690271 0.72525883 0.915658 ] mean value: 0.8467754364013672 key: score_time value: [0.01364899 0.01264262 0.01263046 0.01277018 0.01270843 0.01261044 0.01264334 0.01270008 0.01384187 0.01266003] mean value: 0.012885642051696778 key: test_mcc value: [0.95346259 1. 0.90692382 0.95238095 1. 0.90692382 0.90649828 0.70714286 0.95238095 0.90238095] mean value: 0.9188094230834079 key: train_mcc value: [1. 0.98379816 1. 0.98384191 0.98921825 0.98384191 1. 0.9946235 0.98384144 1. ] mean value: 0.9919165157084897 key: test_fscore value: [0.97674419 1. 0.95 0.97560976 1. 0.95 0.94736842 0.85 0.97560976 0.95 ] mean value: 0.9575332119294264 key: train_fscore value: [1. 0.99191375 1. 0.99191375 0.99459459 0.99191375 1. 0.99731903 0.9919571 1. ] mean value: 0.9959611973896966 key: test_precision value: [0.95454545 1. 1. 1. 1. 1. 1. 0.85 0.95238095 0.95 ] mean value: 0.9706926406926406 key: train_precision value: [1. 0.98924731 1. 0.98924731 0.99459459 0.98924731 1. 0.99465241 0.98930481 1. ] mean value: 0.9946293749329802 key: test_recall value: [1. 1. 0.9047619 0.95238095 1. 0.9047619 0.9 0.85 1. 0.95 ] mean value: 0.9461904761904762 key: train_recall value: [1. 0.99459459 1. 0.99459459 0.99459459 0.99459459 1. 1. 0.99462366 1. ] mean value: 0.9973002034292356 key: test_accuracy value: [0.97619048 1. 0.95121951 0.97560976 1. 0.95121951 0.95121951 0.85365854 0.97560976 0.95121951] mean value: 0.9585946573751452 key: train_accuracy value: [1. 0.99189189 1. 0.99191375 0.99460916 0.99191375 1. 0.99730458 0.99191375 1. ] mean value: 0.9959546878414802 key: test_roc_auc value: [0.97619048 1. 0.95238095 0.97619048 1. 0.95238095 0.95 0.85357143 0.97619048 0.95119048] mean value: 0.9588095238095237 key: train_roc_auc value: [1. 0.99189189 1. 0.99192095 0.99460913 0.99192095 1. 0.9972973 0.99190642 1. ] mean value: 0.995954664341761 key: test_jcc value: [0.95454545 1. 0.9047619 0.95238095 1. 0.9047619 0.9 0.73913043 0.95238095 0.9047619 ] mean value: 0.9212723508375683 key: train_jcc value: [1. 0.98395722 1. 0.98395722 0.98924731 0.98395722 1. 0.99465241 0.98404255 1. ] mean value: 0.991981392919057 key: TN value: 200 mean value: 200.0 key: FP value: 12 mean value: 12.0 key: FN value: 6 mean value: 6.0 key: TP value: 194 mean value: 194.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.86 Accuracy on Blind test: 0.94 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01342845 0.01186204 0.01067162 0.00947356 0.00962114 0.00934386 0.00946069 0.00932765 0.00938511 0.00942278] mean value: 0.010199689865112304 key: score_time value: [0.01247454 0.00964475 0.00987816 0.00903606 0.00877929 0.00873184 0.00876999 0.0087111 0.00880551 0.00896764] mean value: 0.009379887580871582 key: test_mcc value: [0.42857143 0.47673129 0.23018043 0.26730386 0.57570364 0.56086079 0.46428571 0.26904762 0.62325386 0.56836003] mean value: 0.44642986720816974 key: train_mcc value: [0.50978658 0.47746734 0.47521493 0.49836503 0.45863438 0.50506482 0.52229262 0.51472381 0.45731256 0.498771 ] mean value: 0.491763305975787 key: test_fscore value: [0.71428571 0.74418605 0.68 0.65116279 0.80851064 0.79069767 0.73170732 0.63414634 0.81818182 0.79069767] mean value: 0.7363576015348501 key: train_fscore value: [0.74366197 0.75621891 0.75 0.76262626 0.74168798 0.76691729 0.77120823 0.77192982 0.7403599 0.765 ] mean value: 0.7569610360657331 key: test_precision value: [0.71428571 0.72727273 0.5862069 0.63636364 0.73076923 0.77272727 0.71428571 0.61904762 0.75 0.73913043] mean value: 0.6990089246086246 key: train_precision value: [0.77647059 0.70046083 0.71014493 0.71563981 0.7038835 0.71495327 0.73891626 0.72300469 0.70935961 0.71495327] mean value: 0.7207786749797507 key: test_recall value: [0.71428571 0.76190476 0.80952381 0.66666667 0.9047619 0.80952381 0.75 0.65 0.9 0.85 ] mean value: 0.7816666666666666 key: train_recall value: [0.71351351 0.82162162 0.79459459 0.81621622 0.78378378 0.82702703 0.80645161 0.82795699 0.77419355 0.82258065] mean value: 0.7987939552455681 key: test_accuracy value: [0.71428571 0.73809524 0.6097561 0.63414634 0.7804878 0.7804878 0.73170732 0.63414634 0.80487805 0.7804878 ] mean value: 0.7208478513356562 key: train_accuracy value: [0.75405405 0.73513514 0.73584906 0.74663073 0.7277628 0.74932615 0.76010782 0.75471698 0.7277628 0.74663073] mean value: 0.7437976251183799 key: test_roc_auc value: [0.71428571 0.73809524 0.6047619 0.63333333 0.77738095 0.7797619 0.73214286 0.63452381 0.80714286 0.78214286] mean value: 0.7203571428571429 key: train_roc_auc value: [0.75405405 0.73513514 0.73600697 0.74681779 0.7279134 0.74953502 0.75998256 0.75451904 0.72763731 0.74642546] mean value: 0.7438026736413834 key: test_jcc value: [0.55555556 0.59259259 0.51515152 0.48275862 0.67857143 0.65384615 0.57692308 0.46428571 0.69230769 0.65384615] mean value: 0.5865838503769538 key: train_jcc value: [0.59192825 0.608 0.6 0.61632653 0.58943089 0.62195122 0.62761506 0.62857143 0.5877551 0.6194332 ] mean value: 0.6091011687308778 key: TN value: 136 mean value: 136.0 key: FP value: 45 mean value: 45.0 key: FN value: 70 mean value: 70.0 key: TP value: 161 mean value: 161.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.38 Accuracy on Blind test: 0.72 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01096463 0.00961113 0.00975704 0.00960565 0.00958586 0.00966382 0.0096786 0.0097096 0.00990868 0.0098772 ] mean value: 0.009836220741271972 key: score_time value: [0.00957084 0.0090189 0.00870156 0.00873661 0.00881624 0.00886679 0.00888467 0.00873137 0.00875568 0.00878763] mean value: 0.008887028694152832 key: test_mcc value: [ 0. 0.30304576 0.41327851 0.29113032 0.45524446 0.63994524 0.62048368 -0.04029115 0.18976803 0.41428571] mean value: 0.32868905707398166 key: train_mcc value: [0.46108397 0.40209243 0.41598338 0.41863013 0.42311032 0.36261286 0.43869287 0.425884 0.46299486 0.42319846] mean value: 0.4234283285039922 key: test_fscore value: [0.43243243 0.57142857 0.60606061 0.57142857 0.64705882 0.77777778 0.70967742 0.32258065 0.4137931 0.7 ] mean value: 0.5752237950621776 key: train_fscore value: [0.66666667 0.60338983 0.65420561 0.64984227 0.64308682 0.6038961 0.65822785 0.65408805 0.66878981 0.67069486] mean value: 0.647288786794278 key: test_precision value: [0.5 0.71428571 0.83333333 0.71428571 0.84615385 0.93333333 1. 0.45454545 0.66666667 0.7 ] mean value: 0.7362604062604063 key: train_precision value: [0.81889764 0.80909091 0.77205882 0.78030303 0.79365079 0.75609756 0.8 0.78787879 0.8203125 0.76551724] mean value: 0.7903807284603128 key: test_recall value: [0.38095238 0.47619048 0.47619048 0.47619048 0.52380952 0.66666667 0.55 0.25 0.3 0.7 ] mean value: 0.48 key: train_recall value: [0.56216216 0.48108108 0.56756757 0.55675676 0.54054054 0.5027027 0.55913978 0.55913978 0.56451613 0.59677419] mean value: 0.5490380703283929 key: test_accuracy value: [0.5 0.64285714 0.68292683 0.63414634 0.70731707 0.80487805 0.7804878 0.48780488 0.58536585 0.70731707] mean value: 0.6533101045296166 key: train_accuracy value: [0.71891892 0.68378378 0.70080863 0.70080863 0.70080863 0.67115903 0.70889488 0.70350404 0.71967655 0.70619946] mean value: 0.7014562540977635 key: test_roc_auc value: [0.5 0.64285714 0.68809524 0.63809524 0.71190476 0.80833333 0.775 0.48214286 0.57857143 0.70714286] mean value: 0.6532142857142857 key: train_roc_auc value: [0.71891892 0.68378378 0.70045045 0.70042139 0.7003778 0.67070619 0.70929962 0.70389422 0.7200959 0.7064952 ] mean value: 0.7014443475733797 key: test_jcc value: [0.27586207 0.4 0.43478261 0.4 0.47826087 0.63636364 0.55 0.19230769 0.26086957 0.53846154] mean value: 0.41669079795766456 key: train_jcc value: [0.5 0.43203883 0.48611111 0.48130841 0.47393365 0.43255814 0.49056604 0.48598131 0.50239234 0.50454545] mean value: 0.478943529129163 key: TN value: 170 mean value: 170.0 key: FP value: 107 mean value: 107.0 key: FN value: 36 mean value: 36.0 key: TP value: 99 mean value: 99.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.25 Accuracy on Blind test: 0.61 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00891542 0.00999284 0.00998735 0.00997591 0.00999928 0.0100472 0.00998139 0.0099628 0.00964546 0.00991631] mean value: 0.009842395782470703 key: score_time value: [0.01492429 0.01553607 0.01342964 0.01545405 0.01206732 0.01232624 0.01210999 0.01499987 0.01185274 0.01187205] mean value: 0.013457226753234863 key: test_mcc value: [0.43052839 0.38490018 0.17142857 0.37171226 0.6133669 0.56086079 0.51320273 0.07159925 0.57570364 0.37309549] mean value: 0.40663981862522663 key: train_mcc value: [0.57917445 0.56736651 0.59587155 0.60158874 0.5713147 0.5850851 0.5912125 0.63077465 0.59830079 0.59830079] mean value: 0.59189897754378 key: test_fscore value: [0.7 0.66666667 0.58536585 0.66666667 0.8 0.79069767 0.73684211 0.51282051 0.74285714 0.62857143] mean value: 0.6830488050922717 key: train_fscore value: [0.7696793 0.76521739 0.79452055 0.79558011 0.77272727 0.7890411 0.79005525 0.8056338 0.78873239 0.78873239] mean value: 0.78599195588241 key: test_precision value: [0.73684211 0.72222222 0.6 0.72222222 0.84210526 0.77272727 0.77777778 0.52631579 0.86666667 0.73333333] mean value: 0.7300212652844231 key: train_precision value: [0.83544304 0.825 0.80555556 0.81355932 0.81437126 0.8 0.8125 0.84615385 0.82840237 0.82840237] mean value: 0.8209387752930825 key: test_recall value: [0.66666667 0.61904762 0.57142857 0.61904762 0.76190476 0.80952381 0.7 0.5 0.65 0.55 ] mean value: 0.6447619047619048 key: train_recall value: [0.71351351 0.71351351 0.78378378 0.77837838 0.73513514 0.77837838 0.7688172 0.7688172 0.75268817 0.75268817] mean value: 0.7545713455390876 key: test_accuracy value: [0.71428571 0.69047619 0.58536585 0.68292683 0.80487805 0.7804878 0.75609756 0.53658537 0.7804878 0.68292683] mean value: 0.7014518002322879 key: train_accuracy value: [0.78648649 0.78108108 0.79784367 0.80053908 0.78436658 0.79245283 0.79514825 0.81401617 0.79784367 0.79784367] mean value: 0.7947621475923363 key: test_roc_auc value: [0.71428571 0.69047619 0.58571429 0.68452381 0.80595238 0.7797619 0.7547619 0.53571429 0.77738095 0.6797619 ] mean value: 0.7008333333333333 key: train_roc_auc value: [0.78648649 0.78108108 0.79780587 0.80047951 0.78423423 0.792415 0.79521941 0.81413833 0.79796571 0.79796571] mean value: 0.7947791339726823 key: test_jcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( [0.53846154 0.5 0.4137931 0.5 0.66666667 0.65384615 0.58333333 0.34482759 0.59090909 0.45833333] mean value: 0.5250170806205288 key: train_jcc value: [0.62559242 0.61971831 0.65909091 0.66055046 0.62962963 0.65158371 0.65296804 0.6745283 0.65116279 0.65116279] mean value: 0.6475987354575963 key: TN value: 156 mean value: 156.0 key: FP value: 73 mean value: 73.0 key: FN value: 50 mean value: 50.0 key: TP value: 133 mean value: 133.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.31 Accuracy on Blind test: 0.65 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.0182035 0.01881266 0.01938367 0.01898909 0.01740909 0.0181179 0.01810026 0.01760936 0.0182271 0.01885557] mean value: 0.018370819091796876 key: score_time value: [0.01155233 0.01083374 0.01057696 0.01137233 0.01090479 0.01087809 0.01130199 0.01125836 0.01051879 0.01087332] mean value: 0.011007070541381836 key: test_mcc value: [0.62187434 0.71754731 0.60952381 0.66668392 0.8547619 0.65871309 0.7633652 0.51190476 0.70714286 0.65871309] mean value: 0.6770230281474807 key: train_mcc value: [0.76946971 0.80658462 0.78453492 0.77396856 0.76871644 0.74663605 0.77898224 0.80112842 0.78512038 0.78553828] mean value: 0.780067963437501 key: test_fscore value: [0.81818182 0.85 0.80952381 0.82051282 0.92682927 0.8372093 0.86486486 0.75 0.85 0.82051282] mean value: 0.8347634704214398 key: train_fscore value: [0.87955182 0.9 0.89071038 0.88461538 0.8815427 0.87262873 0.89008043 0.89863014 0.89010989 0.88950276] mean value: 0.8877372232350673 key: test_precision value: [0.7826087 0.89473684 0.80952381 0.88888889 0.95 0.81818182 0.94117647 0.75 0.85 0.84210526] mean value: 0.8527221788098082 key: train_precision value: [0.9127907 0.92571429 0.90055249 0.89944134 0.8988764 0.875 0.88770053 0.91620112 0.91011236 0.91477273] mean value: 0.9041161953754138 key: test_recall value: [0.85714286 0.80952381 0.80952381 0.76190476 0.9047619 0.85714286 0.8 0.75 0.85 0.8 ] mean value: 0.82 key: train_recall value: [0.84864865 0.87567568 0.88108108 0.87027027 0.86486486 0.87027027 0.89247312 0.88172043 0.87096774 0.8655914 ] mean value: 0.8721563498982853 key: test_accuracy value: [0.80952381 0.85714286 0.80487805 0.82926829 0.92682927 0.82926829 0.87804878 0.75609756 0.85365854 0.82926829] mean value: 0.83739837398374 key: train_accuracy value: [0.88378378 0.9027027 0.89218329 0.88679245 0.88409704 0.87331536 0.88948787 0.90026954 0.89218329 0.89218329] mean value: 0.8896998615866542 key: test_roc_auc value: [0.80952381 0.85714286 0.8047619 0.83095238 0.92738095 0.82857143 0.87619048 0.75595238 0.85357143 0.82857143] mean value: 0.8372619047619047 key: train_roc_auc value: [0.88378378 0.9027027 0.89215344 0.88674804 0.88404534 0.87330718 0.8894798 0.90031967 0.89224063 0.89225516] mean value: 0.8897035745422844 key: test_jcc value: [0.69230769 0.73913043 0.68 0.69565217 0.86363636 0.72 0.76190476 0.6 0.73913043 0.69565217] mean value: 0.7187414035240123 key: train_jcc value: [0.785 0.81818182 0.80295567 0.79310345 0.78817734 0.77403846 0.80193237 0.8159204 0.8019802 0.80099502] mean value: 0.7982284720977383 key: TN value: 176 mean value: 176.0 key: FP value: 37 mean value: 37.0 key: FN value: 30 mean value: 30.0 key: TP value: 169 mean value: 169.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.57 Accuracy on Blind test: 0.81 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.38255119 1.43847704 1.32005191 1.51834154 1.42000461 1.32521057 1.5029707 1.40174365 1.32761645 1.41051412] mean value: 1.4047481775283814 key: score_time value: [0.01294279 0.01267171 0.01273942 0.01282597 0.01268029 0.01274633 0.01404238 0.01446939 0.0127387 0.01268911] mean value: 0.013054609298706055 key: test_mcc value: [0.90889326 0.9047619 0.71121921 0.86333169 1. 0.90692382 0.86240942 0.65952381 0.95238095 0.8547619 ] mean value: 0.8624205974031269 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95454545 0.95238095 0.85 0.92307692 1. 0.95 0.91891892 0.82926829 0.97560976 0.92682927] mean value: 0.9280629565995419 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.91304348 0.95238095 0.89473684 1. 1. 1. 1. 0.80952381 0.95238095 0.9047619 ] mean value: 0.9426827939413751 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.95238095 0.80952381 0.85714286 1. 0.9047619 0.85 0.85 1. 0.95 ] mean value: 0.9173809523809522 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95238095 0.95238095 0.85365854 0.92682927 1. 0.95121951 0.92682927 0.82926829 0.97560976 0.92682927] mean value: 0.929500580720093 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95238095 0.95238095 0.8547619 0.92857143 1. 0.95238095 0.925 0.8297619 0.97619048 0.92738095] mean value: 0.9298809523809524 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.91304348 0.90909091 0.73913043 0.85714286 1. 0.9047619 0.85 0.70833333 0.95238095 0.86363636] mean value: 0.8697520233389799 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 194 mean value: 194.0 key: FP value: 17 mean value: 17.0 key: FN value: 12 mean value: 12.0 key: TP value: 189 mean value: 189.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.75 Accuracy on Blind test: 0.89 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02127624 0.02067518 0.01595592 0.01767468 0.01548076 0.01587391 0.01521254 0.0156157 0.01792765 0.01475978] mean value: 0.017045235633850096 key: score_time value: [0.01265335 0.00976419 0.00935817 0.0089314 0.00892782 0.00888157 0.00895119 0.00915241 0.00912976 0.00949645] mean value: 0.00952463150024414 key: test_mcc value: [0.90889326 0.90889326 1. 0.8047619 1. 0.90692382 1. 0.86333169 0.95227002 0.95238095] mean value: 0.929745490920746 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95 0.95 1. 0.9047619 1. 0.95 1. 0.93023256 0.97435897 0.97560976] mean value: 0.9634963193357976 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 0.9047619 1. 1. 1. 0.86956522 1. 0.95238095] mean value: 0.9726708074534163 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.9047619 0.9047619 1. 0.9047619 1. 0.9047619 1. 1. 0.95 1. ] mean value: 0.9569047619047618 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95238095 0.95238095 1. 0.90243902 1. 0.95121951 1. 0.92682927 0.97560976 0.97560976] mean value: 0.9636469221835074 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95238095 0.95238095 1. 0.90238095 1. 0.95238095 1. 0.92857143 0.975 0.97619048] mean value: 0.9639285714285715 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.9047619 0.9047619 1. 0.82608696 1. 0.9047619 1. 0.86956522 0.95 0.95238095] mean value: 0.931231884057971 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 200 mean value: 200.0 key: FP value: 9 mean value: 9.0 key: FN value: 6 mean value: 6.0 key: TP value: 197 mean value: 197.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.9 Accuracy on Blind test: 0.95 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.10961461 0.10851312 0.109653 0.11035824 0.11034012 0.11315393 0.10982227 0.10867882 0.10954762 0.10860085] mean value: 0.10982825756072997 key: score_time value: [0.01763701 0.01784396 0.0181098 0.01774406 0.01787066 0.0188036 0.01838684 0.01768804 0.01775908 0.01774549] mean value: 0.017958855628967284 key: test_mcc value: [0.71754731 0.81322028 0.75714286 0.8547619 0.90692382 0.90238095 0.80817439 0.56190476 0.8547619 0.70714286] mean value: 0.7883961048324287 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.86363636 0.90909091 0.87804878 0.92682927 0.95 0.95238095 0.89473684 0.7804878 0.92682927 0.85 ] mean value: 0.8932040189164707 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.82608696 0.86956522 0.9 0.95 1. 0.95238095 0.94444444 0.76190476 0.9047619 0.85 ] mean value: 0.8959144237405108 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.9047619 0.95238095 0.85714286 0.9047619 0.9047619 0.95238095 0.85 0.8 0.95 0.85 ] mean value: 0.8926190476190475 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.85714286 0.9047619 0.87804878 0.92682927 0.95121951 0.95121951 0.90243902 0.7804878 0.92682927 0.85365854] mean value: 0.8932636469221835 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.85714286 0.9047619 0.87857143 0.92738095 0.95238095 0.95119048 0.90119048 0.78095238 0.92738095 0.85357143] mean value: 0.893452380952381 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.76 0.83333333 0.7826087 0.86363636 0.9047619 0.90909091 0.80952381 0.64 0.86363636 0.73913043] mean value: 0.8105721814417466 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 184 mean value: 184.0 key: FP value: 22 mean value: 22.0 key: FN value: 22 mean value: 22.0 key: TP value: 184 mean value: 184.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.54 Accuracy on Blind test: 0.8 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01006579 0.00969267 0.00944519 0.00950289 0.00946856 0.0098021 0.0096879 0.00964212 0.00958872 0.00959158] mean value: 0.009648752212524415 key: score_time value: [0.00878835 0.00860691 0.00863194 0.00864887 0.00862575 0.00864124 0.00870109 0.00867701 0.00868344 0.00871062] mean value: 0.00867152214050293 key: test_mcc value: [0.33954988 0.43656413 0.75714286 0.41766229 0.76500781 0.8047619 0.77831178 0.31960727 0.7098505 0.75714286] mean value: 0.6085601274524202 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.69565217 0.68421053 0.87804878 0.7 0.87179487 0.9047619 0.85714286 0.61111111 0.84210526 0.87804878] mean value: 0.7922876269173081 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.64 0.76470588 0.9 0.73684211 0.94444444 0.9047619 1. 0.6875 0.88888889 0.85714286] mean value: 0.8324286082854195 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.76190476 0.61904762 0.85714286 0.66666667 0.80952381 0.9047619 0.75 0.55 0.8 0.9 ] mean value: 0.761904761904762 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.71428571 0.87804878 0.70731707 0.87804878 0.90243902 0.87804878 0.65853659 0.85365854 0.87804878] mean value: 0.8015098722415797 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66666667 0.71428571 0.87857143 0.70833333 0.8797619 0.90238095 0.875 0.65595238 0.85238095 0.87857143] mean value: 0.8011904761904761 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.53333333 0.52 0.7826087 0.53846154 0.77272727 0.82608696 0.75 0.44 0.72727273 0.7826087 ] mean value: 0.6673099219620959 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 173 mean value: 173.0 key: FP value: 49 mean value: 49.0 key: FN value: 33 mean value: 33.0 key: TP value: 157 mean value: 157.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.41 Accuracy on Blind test: 0.73 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.51198244 1.54020786 1.51207709 1.50037861 1.5325613 1.51260614 1.51920581 1.49548864 1.51993775 1.50940585] mean value: 1.5153851509094238 key: score_time value: [0.10183477 0.09141374 0.0912416 0.09255338 0.09245515 0.09194899 0.09139752 0.09136343 0.09181547 0.09130526] mean value: 0.09273293018341064 key: test_mcc value: [0.90889326 0.85811633 0.85441771 0.85441771 0.95238095 0.90692382 0.95227002 0.76500781 0.95238095 0.90692382] mean value: 0.8911732380263115 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95454545 0.93023256 0.93023256 0.93023256 0.97560976 0.95 0.97435897 0.88372093 0.97560976 0.95238095] mean value: 0.9456923498131665 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.91304348 0.90909091 0.90909091 0.90909091 1. 1. 1. 0.82608696 0.95238095 0.90909091] mean value: 0.9327875023527197 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.95238095 0.95238095 0.95238095 0.95238095 0.9047619 0.95 0.95 1. 1. ] mean value: 0.9614285714285714 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95238095 0.92857143 0.92682927 0.92682927 0.97560976 0.95121951 0.97560976 0.87804878 0.97560976 0.95121951] mean value: 0.9441927990708479 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95238095 0.92857143 0.92619048 0.92619048 0.97619048 0.95238095 0.975 0.8797619 0.97619048 0.95238095] mean value: 0.9445238095238094 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.91304348 0.86956522 0.86956522 0.86956522 0.95238095 0.9047619 0.95 0.79166667 0.95238095 0.90909091] mean value: 0.8982020515716167 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 191 mean value: 191.0 key: FP value: 8 mean value: 8.0 key: FN value: 15 mean value: 15.0 key: TP value: 198 mean value: 198.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.85 Accuracy on Blind test: 0.94 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.92990637 0.95555329 0.89409733 0.94846439 0.97272491 0.92180276 0.9189589 0.93088937 0.8951056 1.03274155] mean value: 0.940024447441101 key: score_time value: [0.20952916 0.21944141 0.19545674 0.20378399 0.12587929 0.18719935 0.19421124 0.20344734 0.24252033 0.22045898] mean value: 0.20019278526306153 key: test_mcc value: [0.81322028 0.81322028 0.7565654 0.85441771 0.95238095 0.90692382 1. 0.65952381 0.90692382 0.86333169] mean value: 0.852650778186983 key: train_mcc value: [0.989247 0.98391316 0.97866529 0.98384191 0.98395676 0.98927606 0.98395537 0.98395537 0.97866283 0.98395537] mean value: 0.9839429109753077 key: test_fscore value: [0.90909091 0.90909091 0.88372093 0.93023256 0.97560976 0.95 1. 0.82926829 0.95238095 0.93023256] mean value: 0.9269626865854885 key: train_fscore value: [0.99462366 0.9919571 0.98930481 0.99191375 0.9919571 0.99462366 0.992 0.992 0.9893617 0.992 ] mean value: 0.9919741782535849 key: test_precision value: [0.86956522 0.86956522 0.86363636 0.90909091 1. 1. 1. 0.80952381 0.90909091 0.86956522] mean value: 0.9100037643515904 key: train_precision value: [0.98930481 0.98404255 0.97883598 0.98924731 0.98404255 0.98930481 0.98412698 0.98412698 0.97894737 0.98412698] mean value: 0.984610634351737 key: test_recall value: [0.95238095 0.95238095 0.9047619 0.95238095 0.95238095 0.9047619 1. 0.85 1. 1. ] mean value: 0.9469047619047618 key: train_recall value: [1. 1. 1. 0.99459459 1. 1. 1. 1. 1. 1. ] mean value: 0.9994594594594595 key: test_accuracy value: [0.9047619 0.9047619 0.87804878 0.92682927 0.97560976 0.95121951 1. 0.82926829 0.95121951 0.92682927] mean value: 0.9248548199767713 key: train_accuracy value: [0.99459459 0.99189189 0.98921833 0.99191375 0.99191375 0.99460916 0.99191375 0.99191375 0.98921833 0.99191375] mean value: 0.9919101041742551 key: test_roc_auc value: [0.9047619 0.9047619 0.87738095 0.92619048 0.97619048 0.95238095 1. 0.8297619 0.95238095 0.92857143] mean value: 0.9252380952380952 key: train_roc_auc value: [0.99459459 0.99189189 0.98924731 0.99192095 0.99193548 0.99462366 0.99189189 0.99189189 0.98918919 0.99189189] mean value: 0.991907875617553 key: test_jcc value: [0.83333333 0.83333333 0.79166667 0.86956522 0.95238095 0.9047619 1. 0.70833333 0.90909091 0.86956522] mean value: 0.8672030867683042 key: train_jcc value: [0.98930481 0.98404255 0.97883598 0.98395722 0.98404255 0.98930481 0.98412698 0.98412698 0.97894737 0.98412698] mean value: 0.9840816250940749 key: TN value: 186 mean value: 186.0 key: FP value: 11 mean value: 11.0 key: FN value: 20 mean value: 20.0 key: TP value: 195 mean value: 195.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.8 Accuracy on Blind test: 0.91 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.07739592 0.05289793 0.05677772 0.0575881 0.06224346 0.06622791 0.06729889 0.06369901 0.06968975 0.06554985] mean value: 0.06393685340881347 key: score_time value: [0.01081038 0.0105238 0.01094198 0.01074648 0.01077628 0.01114082 0.0110867 0.01144195 0.01085472 0.01093197] mean value: 0.010925507545471192 key: test_mcc value: [1. 1. 1. 1. 1. 0.95238095 0.90649828 0.86333169 0.95227002 0.95238095] mean value: 0.9626861893882752 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 1. 0.97560976 0.94736842 0.93023256 0.97435897 0.97560976] mean value: 0.9803179465746261 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 1. 1. 1. 1. 0.86956522 1. 0.95238095] mean value: 0.9821946169772258 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 0.95238095 0.9 1. 0.95 1. ] mean value: 0.9802380952380952 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 1. 0.97560976 0.95121951 0.92682927 0.97560976 0.97560976] mean value: 0.9804878048780488 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 1. 0.97619048 0.95 0.92857143 0.975 0.97619048] mean value: 0.9805952380952381 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 1. 0.95238095 0.9 0.86956522 0.95 0.95238095] mean value: 0.9624327122153209 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 202 mean value: 202.0 key: FP value: 4 mean value: 4.0 key: FN value: 4 mean value: 4.0 key: TP value: 202 mean value: 202.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.9 Accuracy on Blind test: 0.95 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02928782 0.04429269 0.09572411 0.04821539 0.03643346 0.03635001 0.09062386 0.06526732 0.07165194 0.07450461] mean value: 0.059235119819641115 key: score_time value: [0.01215029 0.02206373 0.03581309 0.0122366 0.01228285 0.01228428 0.02322578 0.02300858 0.02337956 0.02231598] mean value: 0.01987607479095459 key: test_mcc value: [1. 0.85811633 0.71121921 0.75714286 0.8547619 0.8213423 0.86240942 0.70714286 0.90238095 0.7197263 ] mean value: 0.819424213092508 key: train_mcc value: [0.98379816 0.98379816 0.978494 0.97317407 0.98384144 0.98384191 0.98384144 0.98384191 0.9784365 0.98384191] mean value: 0.9816909473768508 key: test_fscore value: [1. 0.92682927 0.85 0.87804878 0.92682927 0.89473684 0.91891892 0.85 0.95 0.83333333] mean value: 0.9028696411430687 key: train_fscore value: [0.99191375 0.99191375 0.98924731 0.98659517 0.99186992 0.99191375 0.9919571 0.99191375 0.98924731 0.99191375] mean value: 0.9908485554329116 key: test_precision value: [1. 0.95 0.89473684 0.9 0.95 1. 1. 0.85 0.95 0.9375 ] mean value: 0.9432236842105264 key: train_precision value: [0.98924731 0.98924731 0.98395722 0.9787234 0.99456522 0.98924731 0.98930481 0.99459459 0.98924731 0.99459459] mean value: 0.9892729090233203 key: test_recall value: [1. 0.9047619 0.80952381 0.85714286 0.9047619 0.80952381 0.85 0.85 0.95 0.75 ] mean value: 0.8685714285714287 key: train_recall value: [0.99459459 0.99459459 0.99459459 0.99459459 0.98918919 0.99459459 0.99462366 0.98924731 0.98924731 0.98924731] mean value: 0.9924527753560011 key: test_accuracy value: [1. 0.92857143 0.85365854 0.87804878 0.92682927 0.90243902 0.92682927 0.85365854 0.95121951 0.85365854] mean value: 0.9074912891986063 key: train_accuracy value: [0.99189189 0.99189189 0.98921833 0.98652291 0.99191375 0.99191375 0.99191375 0.99191375 0.98921833 0.99191375] mean value: 0.9908312085670575 key: test_roc_auc value: [1. 0.92857143 0.8547619 0.87857143 0.92738095 0.9047619 0.925 0.85357143 0.95119048 0.85119048] mean value: 0.9075 key: train_roc_auc value: [0.99189189 0.99189189 0.98923278 0.98654461 0.99190642 0.99192095 0.99190642 0.99192095 0.98921825 0.99192095] mean value: 0.9908355129322871 key: test_jcc value: [1. 0.86363636 0.73913043 0.7826087 0.86363636 0.80952381 0.85 0.73913043 0.9047619 0.71428571] mean value: 0.8266713721061547 key: train_jcc value: [0.98395722 0.98395722 0.9787234 0.97354497 0.98387097 0.98395722 0.98404255 0.98395722 0.9787234 0.98395722] mean value: 0.9818691399245723 key: TN value: 195 mean value: 195.0 key: FP value: 27 mean value: 27.0 key: FN value: 11 mean value: 11.0 key: TP value: 179 mean value: 179.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.68 Accuracy on Blind test: 0.86 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.00992703 0.00981212 0.009444 0.00925446 0.00927901 0.00920343 0.00935507 0.00949359 0.00937295 0.00925636] mean value: 0.009439802169799805 key: score_time value: [0.01170301 0.00905418 0.00861382 0.00861716 0.00858498 0.00860953 0.00862718 0.00857091 0.00866532 0.00863743] mean value: 0.008968353271484375 key: test_mcc value: [0.23918244 0.47673129 0.06905393 0.22195767 0.46300848 0.56190476 0.41487884 0.16945156 0.56190476 0.46428571] mean value: 0.3642359453605911 key: train_mcc value: [0.36235278 0.38387352 0.43399024 0.41274834 0.38004592 0.41782673 0.39622203 0.4018383 0.41258723 0.44486121] mean value: 0.40463463119473336 key: test_fscore value: [0.63636364 0.73170732 0.6122449 0.6 0.74418605 0.7804878 0.68421053 0.56410256 0.7804878 0.73170732] mean value: 0.686549791515524 key: train_fscore value: [0.68617021 0.69518717 0.71389646 0.71087533 0.68834688 0.70967742 0.69892473 0.70712401 0.70299728 0.72679045] mean value: 0.7039989938565314 key: test_precision value: [0.60869565 0.75 0.53571429 0.63157895 0.72727273 0.8 0.72222222 0.57894737 0.76190476 0.71428571] mean value: 0.6830621679363098 key: train_precision value: [0.67539267 0.68783069 0.71978022 0.69791667 0.69021739 0.70588235 0.69892473 0.69430052 0.71270718 0.71727749] mean value: 0.7000229907229114 key: test_recall value: [0.66666667 0.71428571 0.71428571 0.57142857 0.76190476 0.76190476 0.65 0.55 0.8 0.75 ] mean value: 0.694047619047619 key: train_recall value: [0.6972973 0.7027027 0.70810811 0.72432432 0.68648649 0.71351351 0.69892473 0.72043011 0.69354839 0.73655914] mean value: 0.708189479802383 key: test_accuracy value: [0.61904762 0.73809524 0.53658537 0.6097561 0.73170732 0.7804878 0.70731707 0.58536585 0.7804878 0.73170732] mean value: 0.6820557491289199 key: train_accuracy value: [0.68108108 0.69189189 0.71698113 0.70619946 0.69002695 0.70889488 0.69811321 0.70080863 0.70619946 0.72237197] mean value: 0.7022568660304509 key: test_roc_auc value: [0.61904762 0.73809524 0.53214286 0.61071429 0.73095238 0.78095238 0.70595238 0.58452381 0.78095238 0.73214286] mean value: 0.681547619047619 key: train_roc_auc value: [0.68108108 0.69189189 0.71695728 0.70624818 0.69001744 0.70890729 0.69811101 0.70075559 0.70623365 0.72233362] mean value: 0.7022537053182214 key: test_jcc value: [0.46666667 0.57692308 0.44117647 0.42857143 0.59259259 0.64 0.52 0.39285714 0.64 0.57692308] mean value: 0.5275710455122219 key: train_jcc value: [0.52226721 0.53278689 0.55508475 0.55144033 0.52479339 0.55 0.53719008 0.54693878 0.54201681 0.57083333] mean value: 0.543335155334506 key: TN value: 138 mean value: 138.0 key: FP value: 63 mean value: 63.0 key: FN value: 68 mean value: 68.0 key: TP value: 143 mean value: 143.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.33 Accuracy on Blind test: 0.68 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.02252197 0.01936436 0.02320266 0.01887608 0.02798271 0.02770257 0.02312875 0.02318501 0.0263114 0.02959681] mean value: 0.024187231063842775 key: score_time value: [0.00863934 0.01137352 0.01186848 0.01171231 0.01185107 0.01182008 0.01177526 0.01178193 0.0118196 0.0117321 ] mean value: 0.011437368392944337 key: test_mcc value: [0.95346259 1. 0.90692382 0.86333169 1. 0.95238095 0.95227002 0.76500781 0.90238095 0.95238095] mean value: 0.9248138787322764 key: train_mcc value: [0.98379816 0.98379816 0.98927606 0.9361732 0.98927606 0.98384191 0.98384144 0.97866283 0.98384144 0.9946235 ] mean value: 0.9807132751752643 key: test_fscore value: [0.97674419 1. 0.95 0.92307692 1. 0.97560976 0.97435897 0.88372093 0.95 0.97560976] mean value: 0.960912052591009 key: train_fscore value: [0.99191375 0.99191375 0.99462366 0.96685083 0.99462366 0.99191375 0.9919571 0.9893617 0.9919571 0.99731903] mean value: 0.990243432654491 key: test_precision value: [0.95454545 1. 1. 1. 1. 1. 1. 0.82608696 0.95 0.95238095] mean value: 0.9683013363448145 key: train_precision value: [0.98924731 0.98924731 0.98930481 0.98870056 0.98930481 0.98924731 0.98930481 0.97894737 0.98930481 0.99465241] mean value: 0.9887261526630686 key: test_recall value: [1. 1. 0.9047619 0.85714286 1. 0.95238095 0.95 0.95 0.95 1. ] mean value: 0.9564285714285713 key: train_recall value: [0.99459459 0.99459459 1. 0.94594595 1. 0.99459459 0.99462366 1. 0.99462366 1. ] mean value: 0.9918977041557687 key: test_accuracy value: [0.97619048 1. 0.95121951 0.92682927 1. 0.97560976 0.97560976 0.87804878 0.95121951 0.97560976] mean value: 0.9610336817653892 key: train_accuracy value: [0.99189189 0.99189189 0.99460916 0.96765499 0.99460916 0.99191375 0.99191375 0.98921833 0.99191375 0.99730458] mean value: 0.9902921250091061 key: test_roc_auc value: [0.97619048 1. 0.95238095 0.92857143 1. 0.97619048 0.975 0.8797619 0.95119048 0.97619048] mean value: 0.9615476190476191 key: train_roc_auc value: [0.99189189 0.99189189 0.99462366 0.96759663 0.99462366 0.99192095 0.99190642 0.98918919 0.99190642 0.9972973 ] mean value: 0.9902848009299621 key: test_jcc value: [0.95454545 1. 0.9047619 0.85714286 1. 0.95238095 0.95 0.79166667 0.9047619 0.95238095] mean value: 0.9267640692640693 key: train_jcc value: [0.98395722 0.98395722 0.98930481 0.93582888 0.98930481 0.98395722 0.98404255 0.97894737 0.98404255 0.99465241] mean value: 0.9807995041648951 key: TN value: 199 mean value: 199.0 key: FP value: 9 mean value: 9.0 key: FN value: 7 mean value: 7.0 key: TP value: 197 mean value: 197.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.86 Accuracy on Blind test: 0.94 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01611996 0.01744223 0.01719046 0.01823139 0.01707792 0.01627302 0.01734209 0.01785684 0.01796865 0.0183959 ] mean value: 0.017389845848083497 key: score_time value: [0.01184702 0.0118773 0.01190281 0.01194453 0.01188087 0.01207185 0.01205659 0.01236892 0.01192665 0.01186895] mean value: 0.011974549293518067 key: test_mcc value: [0.72760688 0.95346259 0.90692382 0.95227002 1. 0.90692382 0.66668392 0.75714286 0.77831178 0.80817439] mean value: 0.8457500078758784 key: train_mcc value: [0.69419957 0.97310093 0.97305937 0.9734012 0.97317407 0.95709306 0.90722239 0.98384191 0.73247456 0.94236768] mean value: 0.9109934736638234 key: test_fscore value: [0.84210526 0.97674419 0.95 0.97674419 1. 0.95 0.8372093 0.87804878 0.85714286 0.89473684] mean value: 0.9162731417312425 key: train_fscore value: [0.79220779 0.98637602 0.98644986 0.98666667 0.98659517 0.97814208 0.95384615 0.99191375 0.82278481 0.96952909] mean value: 0.9454511392412976 key: test_precision value: [0.94117647 0.95454545 1. 0.95454545 1. 1. 0.7826087 0.85714286 1. 0.94444444] mean value: 0.943446337691862 key: train_precision value: [0.99186992 0.99450549 0.98913043 0.97368421 0.9787234 0.98895028 0.91176471 0.99459459 1. 1. ] mean value: 0.9823223039488965 key: test_recall value: [0.76190476 1. 0.9047619 1. 1. 0.9047619 0.9 0.9 0.75 0.85 ] mean value: 0.8971428571428571 key: train_recall value: [0.65945946 0.97837838 0.98378378 1. 0.99459459 0.96756757 1. 0.98924731 0.69892473 0.94086022] mean value: 0.9212816041848301 key: test_accuracy value: [0.85714286 0.97619048 0.95121951 0.97560976 1. 0.95121951 0.82926829 0.87804878 0.87804878 0.90243902] mean value: 0.9199186991869919 key: train_accuracy value: [0.82702703 0.98648649 0.98652291 0.98652291 0.98652291 0.97843666 0.95148248 0.99191375 0.8490566 0.9703504 ] mean value: 0.9514322138850441 key: test_roc_auc value: [0.85714286 0.97619048 0.95238095 0.975 1. 0.95238095 0.83095238 0.87857143 0.875 0.90119048] mean value: 0.9198809523809525 key: train_roc_auc value: [0.82702703 0.98648649 0.98651555 0.98655914 0.98654461 0.97840744 0.95135135 0.99192095 0.84946237 0.97043011] mean value: 0.9514705027608255 key: test_jcc value: [0.72727273 0.95454545 0.9047619 0.95454545 1. 0.9047619 0.72 0.7826087 0.75 0.80952381] mean value: 0.850801995106343 key: train_jcc value: [0.65591398 0.97311828 0.97326203 0.97368421 0.97354497 0.95721925 0.91176471 0.98395722 0.69892473 0.94086022] mean value: 0.9042249596928515 key: TN value: 194 mean value: 194.0 key: FP value: 21 mean value: 21.0 key: FN value: 12 mean value: 12.0 key: TP value: 185 mean value: 185.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.45 Accuracy on Blind test: 0.64 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.16417074 0.14623141 0.14611936 0.14649653 0.14675474 0.15263605 0.15256119 0.1455555 0.14614868 0.14716053] mean value: 0.14938347339630126 key: score_time value: [0.01506853 0.01520467 0.01521516 0.01508999 0.01510882 0.0154891 0.01506519 0.01622915 0.01549888 0.0156498 ] mean value: 0.015361928939819336 key: test_mcc value: [0.9047619 1. 1. 0.95227002 1. 0.95238095 0.95227002 0.86333169 1. 0.95238095] mean value: 0.9577395534494869 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95238095 1. 1. 0.97674419 1. 0.97560976 0.97435897 0.93023256 1. 0.97560976] mean value: 0.9784936183121097 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.95238095 1. 1. 0.95454545 1. 1. 1. 0.86956522 1. 0.95238095] mean value: 0.9728872576698663 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95238095 1. 1. 1. 1. 0.95238095 0.95 1. 1. 1. ] mean value: 0.9854761904761904 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95238095 1. 1. 0.97560976 1. 0.97560976 0.97560976 0.92682927 1. 0.97560976] mean value: 0.9781649245063878 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95238095 1. 1. 0.975 1. 0.97619048 0.975 0.92857143 1. 0.97619048] mean value: 0.9783333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.90909091 1. 1. 0.95454545 1. 0.95238095 0.95 0.86956522 1. 0.95238095] mean value: 0.9587963485789572 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 200 mean value: 200.0 key: FP value: 3 mean value: 3.0 key: FN value: 6 mean value: 6.0 key: TP value: 203 mean value: 203.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.93 Accuracy on Blind test: 0.97 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.03610611 0.04993343 0.05819893 0.05507755 0.06369472 0.05800819 0.05741405 0.04725194 0.0697515 0.05640531] mean value: 0.05518417358398438 key: score_time value: [0.02264643 0.02638483 0.02947092 0.03190923 0.03001213 0.03039432 0.02232504 0.0379746 0.03620601 0.01787853] mean value: 0.02852020263671875 key: test_mcc value: [0.90889326 0.90889326 0.90238095 0.95227002 0.95238095 0.95238095 0.90649828 0.8213423 0.95227002 0.95238095] mean value: 0.9209690938025448 key: train_mcc value: [1. 1. 0.98927544 0.9946235 0.98927544 0.98927544 0.9946235 0.98921825 0.98384191 0.98921825] mean value: 0.9919351733305355 key: test_fscore value: [0.95 0.95 0.95238095 0.97674419 0.97560976 0.97560976 0.94736842 0.90909091 0.97435897 0.97560976] mean value: 0.9586772711222661 key: train_fscore value: [1. 1. 0.99456522 0.99728997 0.99456522 0.99456522 0.99731903 0.99462366 0.99191375 0.99462366] mean value: 0.9959465718384873 key: test_precision value: [1. 1. 0.95238095 0.95454545 1. 1. 1. 0.83333333 1. 0.95238095] mean value: 0.9692640692640693 key: train_precision value: [1. 1. 1. 1. 1. 1. 0.99465241 0.99462366 0.99459459 0.99462366] mean value: 0.9978494312839663 key: test_recall value: [0.9047619 0.9047619 0.95238095 1. 0.95238095 0.95238095 0.9 1. 0.95 1. ] mean value: 0.9516666666666665 key: train_recall value: [1. 1. 0.98918919 0.99459459 0.98918919 0.98918919 1. 0.99462366 0.98924731 0.99462366] mean value: 0.9940656785818076 key: test_accuracy value: [0.95238095 0.95238095 0.95121951 0.97560976 0.97560976 0.97560976 0.95121951 0.90243902 0.97560976 0.97560976] mean value: 0.9587688734030199 key: train_accuracy value: [1. 1. 0.99460916 0.99730458 0.99460916 0.99460916 0.99730458 0.99460916 0.99191375 0.99460916] mean value: 0.9959568733153639 key: test_roc_auc value: [0.95238095 0.95238095 0.95119048 0.975 0.97619048 0.97619048 0.95 0.9047619 0.975 0.97619048] mean value: 0.9589285714285714 key: train_roc_auc value: [1. 1. 0.99459459 0.9972973 0.99459459 0.99459459 0.9972973 0.99460913 0.99192095 0.99460913] mean value: 0.995951758209823 key: test_jcc value: [0.9047619 0.9047619 0.90909091 0.95454545 0.95238095 0.95238095 0.9 0.83333333 0.95 0.95238095] mean value: 0.9213636363636363 key: train_jcc value: [1. 1. 0.98918919 0.99459459 0.98918919 0.98918919 0.99465241 0.98930481 0.98395722 0.98930481] mean value: 0.991938141349906 key: TN value: 199 mean value: 199.0 key: FP value: 10 mean value: 10.0 key: FN value: 7 mean value: 7.0 key: TP value: 196 mean value: 196.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.91 Accuracy on Blind test: 0.96 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.0951314 0.0657649 0.08133554 0.10624385 0.10583973 0.05929923 0.07229829 0.14089751 0.10813332 0.08627105] mean value: 0.09212148189544678 key: score_time value: [0.0226891 0.01400733 0.02260351 0.02237463 0.02234697 0.01414204 0.01397967 0.02263641 0.0223701 0.01710892] mean value: 0.01942586898803711 key: test_mcc value: [0.62187434 0.61904762 0.6133669 0.48063079 0.80907152 0.76500781 0.57570364 0.46300848 0.7098505 0.65871309] mean value: 0.6316274678175477 key: train_mcc value: [0.92567765 0.91485102 0.92538015 0.89861855 0.91506448 0.91458711 0.92501826 0.93618785 0.92588469 0.92539732] mean value: 0.9206667079567575 key: test_fscore value: [0.8 0.80952381 0.8 0.7027027 0.9 0.87179487 0.74285714 0.71794872 0.84210526 0.82051282] mean value: 0.800744532849796 key: train_fscore value: [0.96111111 0.95555556 0.96132597 0.94736842 0.95555556 0.9558011 0.96174863 0.96703297 0.96132597 0.96153846] mean value: 0.9588363744400097 key: test_precision value: [0.84210526 0.80952381 0.84210526 0.8125 0.94736842 0.94444444 0.86666667 0.73684211 0.88888889 0.84210526] mean value: 0.8532550125313282 key: train_precision value: [0.98857143 0.98285714 0.98305085 0.97159091 0.98285714 0.97740113 0.97777778 0.98876404 0.98863636 0.98314607] mean value: 0.9824652854551446 key: test_recall value: [0.76190476 0.80952381 0.76190476 0.61904762 0.85714286 0.80952381 0.65 0.7 0.8 0.8 ] mean value: 0.7569047619047619 key: train_recall value: [0.93513514 0.92972973 0.94054054 0.92432432 0.92972973 0.93513514 0.94623656 0.94623656 0.93548387 0.94086022] mean value: 0.9363411798895671 key: test_accuracy value: [0.80952381 0.80952381 0.80487805 0.73170732 0.90243902 0.87804878 0.7804878 0.73170732 0.85365854 0.82926829] mean value: 0.813124274099884 key: train_accuracy value: [0.96216216 0.95675676 0.96226415 0.94878706 0.95687332 0.95687332 0.96226415 0.96765499 0.96226415 0.96226415] mean value: 0.9598164201937787 key: test_roc_auc value: [0.80952381 0.80952381 0.80595238 0.73452381 0.90357143 0.8797619 0.77738095 0.73095238 0.85238095 0.82857143] mean value: 0.8132142857142857 key: train_roc_auc value: [0.96216216 0.95675676 0.96220575 0.9487213 0.95680035 0.95681488 0.96230747 0.96771287 0.96233653 0.962322 ] mean value: 0.9598140075559429 key: test_jcc value: [0.66666667 0.68 0.66666667 0.54166667 0.81818182 0.77272727 0.59090909 0.56 0.72727273 0.69565217] mean value: 0.6719743083003953 key: train_jcc value: [0.92513369 0.91489362 0.92553191 0.9 0.91489362 0.91534392 0.92631579 0.93617021 0.92553191 0.92592593] mean value: 0.9209740597178842 key: TN value: 179 mean value: 179.0 key: FP value: 50 mean value: 50.0 key: FN value: 27 mean value: 27.0 key: TP value: 156 mean value: 156.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.42 Accuracy on Blind test: 0.74 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.53300953 0.51279473 0.51417923 0.54170537 0.52085948 0.51572061 0.51508975 0.50532246 0.51649785 0.51727605] mean value: 0.5192455053329468 key: score_time value: [0.00918722 0.00914145 0.00938344 0.00916529 0.00929856 0.00910997 0.00938439 0.00916171 0.00917625 0.00926542] mean value: 0.009227371215820313 key: test_mcc value: [1. 1. 1. 0.90238095 1. 0.95238095 1. 0.86333169 0.95227002 0.95238095] mean value: 0.9622744566930101 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 0.95238095 1. 0.97560976 1. 0.93023256 0.97435897 0.97560976] mean value: 0.9808191997074583 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 0.95238095 1. 1. 1. 0.86956522 1. 0.95238095] mean value: 0.977432712215321 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.95238095 1. 0.95238095 1. 1. 0.95 1. ] mean value: 0.9854761904761904 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 0.95121951 1. 0.97560976 1. 0.92682927 0.97560976 0.97560976] mean value: 0.9804878048780488 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 0.95119048 1. 0.97619048 1. 0.92857143 0.975 0.97619048] mean value: 0.9807142857142856 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 0.90909091 1. 0.95238095 1. 0.86956522 0.95 0.95238095] mean value: 0.9633418031244118 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 201 mean value: 201.0 key: FP value: 3 mean value: 3.0 key: FN /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") value: 5 mean value: 5.0 key: TP value: 203 mean value: 203.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.91 Accuracy on Blind test: 0.96 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02316737 0.02680206 0.02732277 0.02696562 0.02697563 0.02746534 0.02724886 0.02720308 0.02752709 0.02719426] mean value: 0.026787209510803222 key: score_time value: [0.01268864 0.0126133 0.01294136 0.01298118 0.0129559 0.01299787 0.01317263 0.01305532 0.01269197 0.01263595] mean value: 0.012873411178588867 key: test_mcc value: [0.48737732 0.78446454 0.7633652 0.7197263 0.86333169 0.73786479 0.80817439 0.56836003 0.86333169 0.56190476] mean value: 0.7157900728918658 key: train_mcc value: [1. 0.98391316 0.98921825 0.9734012 0.97339739 0.97339739 0.99462366 0.98921825 0.99462366 0.95776892] mean value: 0.9829561871566351 key: test_fscore value: [0.76923077 0.89361702 0.88888889 0.86956522 0.92307692 0.875 0.89473684 0.79069767 0.93023256 0.7804878 ] mean value: 0.8615533699405933 key: train_fscore value: [1. 0.9919571 0.99459459 0.98666667 0.98630137 0.98630137 0.99730458 0.99462366 0.99730458 0.97802198] mean value: 0.991307590390137 key: test_precision value: [0.64516129 0.80769231 0.83333333 0.8 1. 0.77777778 0.94444444 0.73913043 0.86956522 0.76190476] mean value: 0.8179009567649118 key: train_precision value: [1. 0.98404255 0.99459459 0.97368421 1. 1. 1. 0.99462366 1. 1. ] mean value: 0.9946945014226378 key: test_recall value: [0.95238095 1. 0.95238095 0.95238095 0.85714286 1. 0.85 0.85 1. 0.8 ] mean value: 0.9214285714285715 key: train_recall value: [1. 1. 0.99459459 1. 0.97297297 0.97297297 0.99462366 0.99462366 0.99462366 0.95698925] mean value: 0.9881400755594305 key: test_accuracy value: [0.71428571 0.88095238 0.87804878 0.85365854 0.92682927 0.85365854 0.90243902 0.7804878 0.92682927 0.7804878 ] mean value: 0.8497677119628341 key: train_accuracy value: [1. 0.99189189 0.99460916 0.98652291 0.98652291 0.98652291 0.99730458 0.99460916 0.99730458 0.97843666] mean value: 0.9913724775988927 key: test_roc_auc value: [0.71428571 0.88095238 0.87619048 0.85119048 0.92857143 0.85 0.90119048 0.78214286 0.92857143 0.78095238] mean value: 0.849404761904762 key: train_roc_auc value: [1. 0.99189189 0.99460913 0.98655914 0.98648649 0.98648649 0.99731183 0.99460913 0.99731183 0.97849462] mean value: 0.9913760534728278 key: test_jcc value: [0.625 0.80769231 0.8 0.76923077 0.85714286 0.77777778 0.80952381 0.65384615 0.86956522 0.64 ] mean value: 0.7609778892604979 key: train_jcc value: [1. 0.98404255 0.98924731 0.97368421 0.97297297 0.97297297 0.99462366 0.98930481 0.99462366 0.95698925] mean value: 0.9828461393465717 key: TN value: 160 mean value: 160.0 key: FP value: 16 mean value: 16.0 key: FN value: 46 mean value: 46.0 key: TP value: 190 mean value: 190.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.21 Accuracy on Blind test: 0.68 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.01527357 0.01521778 0.01518393 0.03679013 0.036515 0.04166365 0.03657699 0.04537487 0.02655697 0.03394699] mean value: 0.03030998706817627 key: score_time value: [0.01232934 0.01232672 0.01249433 0.02470875 0.02159786 0.02464294 0.02181315 0.03624797 0.02282119 0.03252983] mean value: 0.02215120792388916 key: test_mcc value: [1. 0.95346259 0.8047619 0.75714286 1. 0.95238095 0.95227002 0.65952381 0.90692382 0.85441771] mean value: 0.8840883660082678 key: train_mcc value: [0.97298719 0.97298719 0.97305937 0.95692987 0.96771006 0.96771006 0.97306016 0.96765475 0.95692987 0.96771194] mean value: 0.967674046655907 key: test_fscore value: [1. 0.97560976 0.9047619 0.87804878 1. 0.97560976 0.97435897 0.82926829 0.95238095 0.92307692] mean value: 0.9413115339944609 key: train_fscore value: [0.98644986 0.98644986 0.98644986 0.97849462 0.98369565 0.98369565 0.98652291 0.98387097 0.97837838 0.98378378] mean value: 0.9837791562454985 key: test_precision value: [1. 1. 0.9047619 0.9 1. 1. 1. 0.80952381 0.90909091 0.94736842] mean value: 0.9470745044429254 key: train_precision value: [0.98913043 0.98913043 0.98913043 0.97326203 0.98907104 0.98907104 0.98918919 0.98387097 0.98369565 0.98913043] mean value: 0.9864681656823766 key: test_recall value: [1. 0.95238095 0.9047619 0.85714286 1. 0.95238095 0.95 0.85 1. 0.9 ] mean value: 0.9366666666666668 key: train_recall value: [0.98378378 0.98378378 0.98378378 0.98378378 0.97837838 0.97837838 0.98387097 0.98387097 0.97311828 0.97849462] mean value: 0.9811246730601569 key: test_accuracy value: [1. 0.97619048 0.90243902 0.87804878 1. 0.97560976 0.97560976 0.82926829 0.95121951 0.92682927] mean value: 0.9415214866434379 key: train_accuracy value: [0.98648649 0.98648649 0.98652291 0.97843666 0.98382749 0.98382749 0.98652291 0.98382749 0.97843666 0.98382749] mean value: 0.9838202083485104 key: test_roc_auc value: [1. 0.97619048 0.90238095 0.87857143 1. 0.97619048 0.975 0.8297619 0.95238095 0.92619048] mean value: 0.9416666666666667 key: train_roc_auc value: [0.98648649 0.98648649 0.98651555 0.97845103 0.98381285 0.98381285 0.98653008 0.98382738 0.97845103 0.98384191] mean value: 0.983821563498983 key: test_jcc value: [1. 0.95238095 0.82608696 0.7826087 1. 0.95238095 0.95 0.70833333 0.90909091 0.85714286] mean value: 0.8938024656502919 key: train_jcc value: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:282: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:283: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_CV['Resampling'] = rs_ros /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:288: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:289: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_BT['Resampling'] = rs_ros [0.97326203 0.97326203 0.97326203 0.95789474 0.96791444 0.96791444 0.97340426 0.96825397 0.95767196 0.96808511] mean value: 0.9680924997732191 key: TN value: 195 mean value: 195.0 key: FP value: 13 mean value: 13.0 key: FN value: 11 mean value: 11.0 key: TP value: 193 mean value: 193.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.9 Accuracy on Blind test: 0.95 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.3271625 0.26242065 0.26160622 0.2503655 0.16396499 0.2712121 0.25434446 0.27765441 0.22024608 0.19233727] mean value: 0.2481314182281494 key: score_time value: [0.02375269 0.01582861 0.02272272 0.02287126 0.01536274 0.0208714 0.02181959 0.03867531 0.01217222 0.02051806] mean value: 0.021459460258483887 key: test_mcc value: [1. 0.95346259 0.8047619 0.75714286 1. 0.95238095 0.95227002 0.65952381 0.90692382 0.85441771] mean value: 0.8840883660082678 key: train_mcc value: [0.97298719 0.97298719 0.98384191 0.95692987 0.96771006 0.96771006 0.97306016 0.9784365 0.95692987 0.96771194] mean value: 0.9698304748061133 key: test_fscore value: [1. 0.97560976 0.9047619 0.87804878 1. 0.97560976 0.97435897 0.82926829 0.95238095 0.92307692] mean value: 0.9413115339944609 key: train_fscore value: [0.98644986 0.98644986 0.99191375 0.97849462 0.98369565 0.98369565 0.98652291 0.98924731 0.97837838 0.98378378] mean value: 0.984863178867309 key: test_precision value: [1. 1. 0.9047619 0.9 1. 1. 1. 0.80952381 0.90909091 0.94736842] mean value: 0.9470745044429254 key: train_precision value: [0.98913043 0.98913043 0.98924731 0.97326203 0.98907104 0.98907104 0.98918919 0.98924731 0.98369565 0.98913043] mean value: 0.9870174877955137 key: test_recall value: [1. 0.95238095 0.9047619 0.85714286 1. 0.95238095 0.95 0.85 1. 0.9 ] mean value: 0.9366666666666668 key: train_recall value: [0.98378378 0.98378378 0.99459459 0.98378378 0.97837838 0.97837838 0.98387097 0.98924731 0.97311828 0.97849462] mean value: 0.9827433885498402 key: test_accuracy value: [1. 0.97619048 0.90243902 0.87804878 1. 0.97560976 0.97560976 0.82926829 0.95121951 0.92682927] mean value: 0.9415214866434379 key: train_accuracy value: [0.98648649 0.98648649 0.99191375 0.97843666 0.98382749 0.98382749 0.98652291 0.98921833 0.97843666 0.98382749] mean value: 0.9848983754644133 key: test_roc_auc value: [1. 0.97619048 0.90238095 0.87857143 1. 0.97619048 0.975 0.8297619 0.95238095 0.92619048] mean value: 0.9416666666666667 key: train_roc_auc value: [0.98648649 0.98648649 0.99192095 0.97845103 0.98381285 0.98381285 0.98653008 0.98921825 0.97845103 0.98384191] mean value: 0.9849011915140947 key: test_jcc value: [1. 0.95238095 0.82608696 0.7826087 1. 0.95238095 0.95 0.70833333 0.90909091 0.85714286] mean value: 0.8938024656502919 key: train_jcc value: [0.97326203 0.97326203 0.98395722 0.95789474 0.96791444 0.96791444 0.97340426 0.9787234 0.95767196 0.96808511] mean value: 0.9702089620899317 key: TN value: 195 mean value: 195.0 key: FP value: 13 mean value: 13.0 key: FN value: 11 mean value: 11.0 key: TP value: 193 mean value: 193.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.9 Accuracy on Blind test: 0.95 PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02878523 0.02737093 0.03174114 0.02732897 0.02855468 0.04491949 0.0289259 0.03289247 0.02990484 0.02912402] mean value: 0.030954766273498534 key: score_time value: [0.01180434 0.01176667 0.01328897 0.01191807 0.01183844 0.01215386 0.01213384 0.01283312 0.01175165 0.01184511] mean value: 0.012133407592773437 key: test_mcc value: [0.73029674 0.54772256 1. 0.90829511 0.71562645 0.44038551 0.63305416 1. 0.62641448 0.13483997] mean value: 0.6736634981203286 key: train_mcc value: [0.88425952 0.85282059 0.8848558 0.93717105 0.88486842 0.85339912 0.86391052 0.87454765 0.86509383 0.89528509] mean value: 0.8796211592825314 key: test_fscore value: [0.86956522 0.7826087 1. 0.94736842 0.84210526 0.72727273 0.8 1. 0.83333333 0.64 ] mean value: 0.8442253657860064 key: train_fscore value: [0.94240838 0.92708333 0.94300518 0.96875 0.94240838 0.92708333 0.93121693 0.9375 0.93333333 0.94736842] mean value: 0.9400157287543415 key: test_precision value: [0.83333333 0.75 1. 1. 0.88888889 0.66666667 0.88888889 1. 0.76923077 0.57142857] mean value: 0.8368437118437118 key: train_precision value: [0.9375 0.91752577 0.93814433 0.96875 0.94736842 0.92708333 0.93617021 0.92783505 0.91 0.94736842] mean value: 0.9357745542843728 key: test_recall value: [0.90909091 0.81818182 1. 0.9 0.8 0.8 0.72727273 1. 0.90909091 0.72727273] mean value: 0.859090909090909 key: train_recall value: [0.94736842 0.93684211 0.94791667 0.96875 0.9375 0.92708333 0.92631579 0.94736842 0.95789474 0.94736842] mean value: 0.9444407894736843 key: test_accuracy value: [0.86363636 0.77272727 1. 0.95238095 0.85714286 0.71428571 0.80952381 1. 0.80952381 0.57142857] mean value: 0.8350649350649351 key: train_accuracy value: [0.94210526 0.92631579 0.94240838 0.96858639 0.94240838 0.92670157 0.93193717 0.93717277 0.93193717 0.94764398] mean value: 0.9397216864149904 key: test_roc_auc value: [0.86363636 0.77272727 1. 0.95 0.85454545 0.71818182 0.81363636 1. 0.80454545 0.56363636] mean value: 0.8340909090909092 key: train_roc_auc value: [0.94210526 0.92631579 0.94237939 0.96858553 0.94243421 0.92669956 0.93190789 0.93722588 0.93207237 0.94764254] mean value: 0.9397368421052631 key: test_jcc value: [0.76923077 0.64285714 1. 0.9 0.72727273 0.57142857 0.66666667 1. 0.71428571 0.47058824] mean value: 0.7462329827035709 key: train_jcc value: [0.89108911 0.86407767 0.89215686 0.93939394 0.89108911 0.86407767 0.87128713 0.88235294 0.875 0.9 ] mean value: 0.8870524429655987 key: TN value: 86 mean value: 86.0 key: FP value: 15 mean value: 15.0 key: FN value: 20 mean value: 20.0 key: TP value: 91 mean value: 91.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.68 Accuracy on Blind test: 0.85 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.8474319 0.64568019 0.65819812 0.74121666 0.64531708 0.66669083 0.67523885 0.69857788 0.63087988 0.70886588] mean value: 0.6918097257614135 key: score_time value: [0.01244688 0.01247382 0.01246166 0.01256037 0.01257372 0.0124898 0.01269221 0.01252818 0.01268768 0.01263762] mean value: 0.012555193901062012 key: test_mcc value: [1. 0.83205029 1. 0.90829511 0.90909091 0.80909091 0.74795759 1. 0.82275335 0.58630197] mean value: 0.8615540131941046 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.9 1. 0.94736842 0.95238095 0.9 0.84210526 1. 0.91666667 0.81481481] mean value: 0.927333611807296 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 1. 0.90909091 0.9 1. 1. 0.84615385 0.6875 ] mean value: 0.9342744755244755 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.81818182 1. 0.9 1. 0.9 0.72727273 1. 1. 1. ] mean value: 0.9345454545454546 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.90909091 1. 0.95238095 0.95238095 0.9047619 0.85714286 1. 0.9047619 0.76190476] mean value: 0.9242424242424242 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.90909091 1. 0.95 0.95454545 0.90454545 0.86363636 1. 0.9 0.75 ] mean value: 0.9231818181818182 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.81818182 1. 0.9 0.90909091 0.81818182 0.72727273 1. 0.84615385 0.6875 ] mean value: 0.8706381118881119 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 97 mean value: 97.0 key: FP value: 8 mean value: 8.0 key: FN value: 9 mean value: 9.0 key: TP value: 98 mean value: 98.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.78 Accuracy on Blind test: 0.9 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01245213 0.01243806 0.00974703 0.00927186 0.0097537 0.01013827 0.00901365 0.00911999 0.00905824 0.00975323] mean value: 0.010074615478515625 key: score_time value: [0.01222992 0.01084161 0.00986362 0.00998068 0.00956964 0.0091145 0.00861526 0.00893164 0.0087111 0.00935578] mean value: 0.00972137451171875 key: test_mcc value: [0.20412415 0.18898224 0.82572282 0.14545455 0.55161872 0.14545455 0.44038551 0.43007562 0.03739788 0.24120908] mean value: 0.32104250973123794 key: train_mcc value: [0.41809986 0.44147316 0.43405367 0.45081174 0.45172183 0.400897 0.48801375 0.44832596 0.47224501 0.41856152] mean value: 0.4424203495544211 key: test_fscore value: [0.66666667 0.64 0.90909091 0.57142857 0.7826087 0.57142857 0.7 0.75 0.58333333 0.69230769] mean value: 0.6866864439907918 key: train_fscore value: [0.73076923 0.74285714 0.74178404 0.75229358 0.74641148 0.72641509 0.73224044 0.73631841 0.75121951 0.7254902 ] mean value: 0.7385799120152144 key: test_precision value: [0.5625 0.57142857 0.83333333 0.54545455 0.69230769 0.54545455 0.77777778 0.69230769 0.53846154 0.6 ] mean value: 0.6359025696525695 key: train_precision value: [0.67256637 0.67826087 0.67521368 0.67213115 0.69026549 0.6637931 0.76136364 0.69811321 0.7 0.67889908] mean value: 0.6890606580654846 key: test_recall value: [0.81818182 0.72727273 1. 0.6 0.9 0.6 0.63636364 0.81818182 0.63636364 0.81818182] mean value: 0.7554545454545455 key: train_recall value: [0.8 0.82105263 0.82291667 0.85416667 0.8125 0.80208333 0.70526316 0.77894737 0.81052632 0.77894737] mean value: 0.7986403508771931 key: test_accuracy value: [0.59090909 0.59090909 0.9047619 0.57142857 0.76190476 0.57142857 0.71428571 0.71428571 0.52380952 0.61904762] mean value: 0.6562770562770563 key: train_accuracy value: [0.70526316 0.71578947 0.71204188 0.71727749 0.72251309 0.69633508 0.7434555 0.72251309 0.73298429 0.70680628] mean value: 0.7174979333149627 key: test_roc_auc value: [0.59090909 0.59090909 0.90909091 0.57272727 0.76818182 0.57272727 0.71818182 0.70909091 0.51818182 0.60909091] mean value: 0.655909090909091 key: train_roc_auc value: [0.70526316 0.71578947 0.71145833 0.71655702 0.72203947 0.69577851 0.74325658 0.72280702 0.73338816 0.70718202] mean value: 0.7173519736842104 key: test_jcc value: [0.5 0.47058824 0.83333333 0.4 0.64285714 0.4 0.53846154 0.6 0.41176471 0.52941176] mean value: 0.5326416720534368 key: train_jcc value: [0.57575758 0.59090909 0.58955224 0.60294118 0.59541985 0.57037037 0.57758621 0.58267717 0.6015625 0.56923077] mean value: 0.585600694112349 key: TN value: 59 mean value: 59.0 key: FP value: 26 mean value: 26.0 key: FN value: 47 mean value: 47.0 key: TP value: 80 mean value: 80.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.35 Accuracy on Blind test: 0.71 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00917554 0.00898218 0.00888371 0.00896883 0.00891352 0.00882316 0.00896239 0.00886655 0.00893283 0.00890827] mean value: 0.00894169807434082 key: score_time value: [0.00879407 0.00865746 0.00865412 0.00859976 0.00862265 0.00858164 0.00859523 0.00866842 0.00866795 0.0087111 ] mean value: 0.008655238151550292 key: test_mcc value: [ 0.09245003 0.18898224 -0.05504819 0.13483997 0.43007562 -0.23636364 0.18090681 0.13762047 -0.35527986 -0.03015113] mean value: 0.0488032319211866 key: train_mcc value: [0.50386393 0.41134755 0.45548246 0.47824733 0.44500555 0.51226436 0.48461189 0.55362565 0.48123065 0.53504756] mean value: 0.48607269365804306 key: test_fscore value: [0.5 0.52631579 0.42105263 0.47058824 0.66666667 0.38095238 0.47058824 0.60869565 0.41666667 0.35294118] mean value: 0.4814467434571082 key: train_fscore value: [0.72093023 0.69565217 0.72916667 0.70175439 0.68604651 0.77073171 0.70930233 0.73809524 0.71590909 0.74285714] mean value: 0.7210445475490609 key: test_precision value: [0.55555556 0.625 0.44444444 0.57142857 0.75 0.36363636 0.66666667 0.58333333 0.38461538 0.5 ] mean value: 0.544468031968032 key: train_precision value: [0.80519481 0.71910112 0.72916667 0.8 0.77631579 0.72477064 0.79220779 0.84931507 0.77777778 0.8125 ] mean value: 0.7786349665611217 key: test_recall value: [0.45454545 0.45454545 0.4 0.4 0.6 0.4 0.36363636 0.63636364 0.45454545 0.27272727] mean value: 0.44363636363636355 key: train_recall value: [0.65263158 0.67368421 0.72916667 0.625 0.61458333 0.82291667 0.64210526 0.65263158 0.66315789 0.68421053] mean value: 0.6760087719298246 key: test_accuracy value: [0.54545455 0.59090909 0.47619048 0.57142857 0.71428571 0.38095238 0.57142857 0.57142857 0.33333333 0.47619048] mean value: 0.523160173160173 key: train_accuracy value: [0.74736842 0.70526316 0.72774869 0.73298429 0.71727749 0.7539267 0.7382199 0.76963351 0.7382199 0.76439791] mean value: 0.739503995591072 key: test_roc_auc value: [0.54545455 0.59090909 0.47272727 0.56363636 0.70909091 0.38181818 0.58181818 0.56818182 0.32727273 0.48636364] mean value: 0.5227272727272727 key: train_roc_auc value: [0.74736842 0.70526316 0.72774123 0.73355263 0.71781798 0.7535636 0.7377193 0.76902412 0.73782895 0.76398026] mean value: 0.7393859649122807 key: test_jcc value: [0.33333333 0.35714286 0.26666667 0.30769231 0.5 0.23529412 0.30769231 0.4375 0.26315789 0.21428571] mean value: 0.32227651991970874 key: train_jcc value: [0.56363636 0.53333333 0.57377049 0.54054054 0.52212389 0.62698413 0.54954955 0.58490566 0.55752212 0.59090909] mean value: 0.5643275174832757 key: TN value: 64 mean value: 64.0 key: FP value: 59 mean value: 59.0 key: FN value: 42 mean value: 42.0 key: TP value: 47 mean value: 47.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.06 Accuracy on Blind test: 0.49 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00854278 0.00899529 0.00837398 0.00849628 0.00835133 0.00847888 0.00856066 0.00836992 0.00841522 0.00843906] mean value: 0.008502340316772461 key: score_time value: [0.01104236 0.01535344 0.0098412 0.00971603 0.00971007 0.00972986 0.00974631 0.00971174 0.00977564 0.00984955] mean value: 0.01044762134552002 key: test_mcc value: [ 0.09245003 0.36514837 0.23636364 0.63305416 0.52727273 0.03015113 0.14545455 0.43007562 -0.05504819 -0.05504819] mean value: 0.2349873851761913 key: train_mcc value: [0.58950634 0.56845255 0.51847791 0.52878605 0.54973133 0.56027205 0.55060406 0.53932217 0.61350202 0.55103649] mean value: 0.556969097894801 key: test_fscore value: [0.5 0.69565217 0.6 0.81818182 0.76190476 0.375 0.57142857 0.75 0.52173913 0.52173913] mean value: 0.611564558629776 key: train_fscore value: [0.79581152 0.78306878 0.75789474 0.76683938 0.77720207 0.78350515 0.77948718 0.76595745 0.81025641 0.76502732] mean value: 0.7785050002608345 key: test_precision value: [0.55555556 0.66666667 0.6 0.75 0.72727273 0.5 0.6 0.69230769 0.5 0.5 ] mean value: 0.6091802641802643 key: train_precision value: [0.79166667 0.78723404 0.76595745 0.7628866 0.77319588 0.7755102 0.76 0.77419355 0.79 0.79545455] mean value: 0.7776098928178448 key: test_recall value: [0.45454545 0.72727273 0.6 0.9 0.8 0.3 0.54545455 0.81818182 0.54545455 0.54545455] mean value: 0.6236363636363637 key: train_recall value: [0.8 0.77894737 0.75 0.77083333 0.78125 0.79166667 0.8 0.75789474 0.83157895 0.73684211] mean value: 0.7799013157894736 key: test_accuracy value: [0.54545455 0.68181818 0.61904762 0.80952381 0.76190476 0.52380952 0.57142857 0.71428571 0.47619048 0.47619048] mean value: 0.6179653679653679 key: train_accuracy value: [0.79473684 0.78421053 0.7591623 0.76439791 0.77486911 0.78010471 0.77486911 0.76963351 0.80628272 0.77486911] mean value: 0.7783135850096445 key: test_roc_auc value: [0.54545455 0.68181818 0.61818182 0.81363636 0.76363636 0.51363636 0.57272727 0.70909091 0.47272727 0.47272727] mean value: 0.6163636363636364 key: train_roc_auc value: [0.79473684 0.78421053 0.75921053 0.76436404 0.77483553 0.78004386 0.775 0.76957237 0.80641447 0.77467105] mean value: 0.7783059210526316 key: test_jcc value: [0.33333333 0.53333333 0.42857143 0.69230769 0.61538462 0.23076923 0.4 0.6 0.35294118 0.35294118] mean value: 0.453958198664081 key: train_jcc value: [0.66086957 0.64347826 0.61016949 0.62184874 0.63559322 0.6440678 0.63865546 0.62068966 0.68103448 0.61946903] mean value: 0.6375875700721912 key: TN value: 65 mean value: 65.0 key: FP value: 40 mean value: 40.0 key: FN value: 41 mean value: 41.0 key: TP value: 66 mean value: 66.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.26 Accuracy on Blind test: 0.62 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.01135945 0.01094675 0.01100111 0.01088858 0.01091003 0.01119184 0.01110053 0.01104259 0.01101637 0.01096416] mean value: 0.011042141914367675 key: score_time value: [0.00942183 0.00925756 0.00914049 0.00912261 0.00915551 0.00912094 0.00912237 0.00918698 0.0099318 0.00918579] mean value: 0.009264588356018066 key: test_mcc value: [0.54772256 0.46225016 0.52295779 0.71818182 0.52727273 0.23373675 0.55161872 0.45226702 0.03015113 0.13483997] mean value: 0.4180998650339932 key: train_mcc value: [0.65266774 0.6843622 0.63426775 0.73019067 0.71757751 0.68585526 0.66740308 0.60269927 0.72927062 0.68652637] mean value: 0.6790820480507576 key: test_fscore value: [0.76190476 0.75 0.73684211 0.85714286 0.76190476 0.55555556 0.73684211 0.76923077 0.61538462 0.64 ] mean value: 0.7184807531649636 key: train_fscore value: [0.82539683 0.84375 0.82233503 0.87 0.86153846 0.84375 0.83838384 0.80412371 0.86734694 0.84536082] mean value: 0.842198562555782 key: test_precision value: [0.8 0.69230769 0.77777778 0.81818182 0.72727273 0.625 0.875 0.66666667 0.53333333 0.57142857] mean value: 0.7086968586968586 key: train_precision value: [0.82978723 0.83505155 0.8019802 0.83653846 0.84848485 0.84375 0.80582524 0.78787879 0.84158416 0.82828283] mean value: 0.8259163305773323 key: test_recall value: [0.72727273 0.81818182 0.7 0.9 0.8 0.5 0.63636364 0.90909091 0.72727273 0.72727273] mean value: 0.7445454545454546 key: train_recall value: [0.82105263 0.85263158 0.84375 0.90625 0.875 0.84375 0.87368421 0.82105263 0.89473684 0.86315789] mean value: 0.8595065789473683 key: test_accuracy value: [0.77272727 0.72727273 0.76190476 0.85714286 0.76190476 0.61904762 0.76190476 0.71428571 0.52380952 0.57142857] mean value: 0.7071428571428571 key: train_accuracy value: [0.82631579 0.84210526 0.81675393 0.86387435 0.85863874 0.84293194 0.83246073 0.80104712 0.86387435 0.84293194] mean value: 0.8390934141636814 key: test_roc_auc value: [0.77272727 0.72727273 0.75909091 0.85909091 0.76363636 0.61363636 0.76818182 0.70454545 0.51363636 0.56363636] mean value: 0.7045454545454545 key: train_roc_auc value: [0.82631579 0.84210526 0.81661184 0.86365132 0.85855263 0.84292763 0.83267544 0.80115132 0.86403509 0.84303728] mean value: 0.8391063596491228 key: test_jcc value: [0.61538462 0.6 0.58333333 0.75 0.61538462 0.38461538 0.58333333 0.625 0.44444444 0.47058824] mean value: 0.5672083961789844 key: train_jcc value: [0.7027027 0.72972973 0.69827586 0.7699115 0.75675676 0.72972973 0.72173913 0.67241379 0.76576577 0.73214286] mean value: 0.7279167831859517 key: TN value: 71 mean value: 71.0 key: FP value: 27 mean value: 27.0 key: FN value: 35 mean value: 35.0 key: TP value: 79 mean value: 79.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value:/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.43 Accuracy on Blind test: 0.74 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.69027948 1.232337 0.704772 0.73980832 0.75131059 0.69982934 0.72087932 0.74564934 0.70578551 0.71666169] mean value: 0.770731258392334 key: score_time value: [0.01450229 0.0136013 0.01349425 0.01363969 0.01377344 0.01384234 0.01815867 0.01383829 0.01365829 0.01377439] mean value: 0.014228296279907227 key: test_mcc value: [ 0.45454545 0.54772256 0.82572282 0.66332496 0.61818182 0.23636364 0.63305416 0.66332496 0.23636364 -0.08528029] mean value: 0.4793323721352055 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.72727273 0.7826087 0.90909091 0.75 0.8 0.6 0.8 0.84615385 0.63636364 0.59259259] mean value: 0.7444082407125886 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.72727273 0.75 0.83333333 1. 0.8 0.6 0.88888889 0.73333333 0.63636364 0.5 ] mean value: 0.746919191919192 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.72727273 0.81818182 1. 0.6 0.8 0.6 0.72727273 1. 0.63636364 0.72727273] mean value: 0.7636363636363637 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.72727273 0.77272727 0.9047619 0.80952381 0.80952381 0.61904762 0.80952381 0.80952381 0.61904762 0.47619048] mean value: 0.7357142857142858 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.72727273 0.77272727 0.90909091 0.8 0.80909091 0.61818182 0.81363636 0.8 0.61818182 0.46363636] mean value: 0.7331818181818182 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.57142857 0.64285714 0.83333333 0.6 0.66666667 0.42857143 0.66666667 0.73333333 0.46666667 0.42105263] mean value: 0.6030576441102757 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 75 mean value: 75.0 key: FP value: 25 mean value: 25.0 key: FN value: 31 mean value: 31.0 key: TP value: 81 mean value: 81.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.63 Accuracy on Blind test: 0.84 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.0173099 0.01580215 0.01309872 0.01248264 0.01198626 0.01239681 0.01150823 0.01147032 0.0118463 0.01268888] mean value: 0.013059020042419434 key: score_time value: [0.01170039 0.00911403 0.0089891 0.00858307 0.00857282 0.00855732 0.00863171 0.00857186 0.00849724 0.00859404] mean value: 0.008981156349182128 key: test_mcc value: [1. 1. 0.80909091 0.90829511 0.80909091 0.90829511 0.71818182 0.90909091 0.80909091 1. ] mean value: 0.887113566700395 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 0.9 0.94736842 0.9 0.94736842 0.85714286 0.95238095 0.90909091 1. ] mean value: 0.9413351560719981 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.9 1. 0.9 1. 0.9 1. 0.90909091 1. ] mean value: 0.9609090909090907 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.9 0.9 0.9 0.9 0.81818182 0.90909091 0.90909091 1. ] mean value: 0.9236363636363636 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 0.9047619 0.95238095 0.9047619 0.95238095 0.85714286 0.95238095 0.9047619 1. ] mean value: 0.9428571428571428 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.90454545 0.95 0.90454545 0.95 0.85909091 0.95454545 0.90454545 1. ] mean value: 0.9427272727272727 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 0.81818182 0.9 0.81818182 0.9 0.75 0.90909091 0.83333333 1. ] mean value: 0.8928787878787879 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 102 mean value: 102.0 key: FP value: 8 mean value: 8.0 key: FN value: 4 mean value: 4.0 key: TP value: 98 mean value: 98.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.78 Accuracy on Blind test: 0.89 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.09913039 0.09283686 0.09363127 0.09323001 0.09556413 0.09252 0.09354663 0.09332609 0.09272718 0.09323263] mean value: 0.09397451877593994 key: score_time value: [0.01736093 0.01741695 0.01741195 0.01764488 0.01735115 0.01733899 0.01745343 0.01734233 0.017344 0.01745343] mean value: 0.01741180419921875 key: test_mcc value: [0.36514837 0.54772256 0.90829511 0.71818182 0.80909091 0.33709993 0.61818182 0.80909091 0.23636364 0.23636364] mean value: 0.5585538693908872 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.7826087 0.94736842 0.85714286 0.9 0.58823529 0.81818182 0.90909091 0.63636364 0.63636364] mean value: 0.7742021934631977 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.7 0.75 1. 0.81818182 0.9 0.71428571 0.81818182 0.90909091 0.63636364 0.63636364] mean value: 0.7882467532467533 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.63636364 0.81818182 0.9 0.9 0.9 0.5 0.81818182 0.90909091 0.63636364 0.63636364] mean value: 0.7654545454545455 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.68181818 0.77272727 0.95238095 0.85714286 0.9047619 0.66666667 0.80952381 0.9047619 0.61904762 0.61904762] mean value: 0.7787878787878787 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.68181818 0.77272727 0.95 0.85909091 0.90454545 0.65909091 0.80909091 0.90454545 0.61818182 0.61818182] mean value: 0.7777272727272727 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.64285714 0.9 0.75 0.81818182 0.41666667 0.69230769 0.83333333 0.46666667 0.46666667] mean value: 0.6486679986679986 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 84 mean value: 84.0 key: FP value: 25 mean value: 25.0 key: FN value: 22 mean value: 22.0 key: TP value: 81 mean value: 81.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.52 Accuracy on Blind test: 0.75 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.008847 0.00880098 0.00887179 0.00869989 0.0088563 0.00886416 0.00878453 0.00897479 0.00888658 0.00894952] mean value: 0.008853554725646973 key: score_time value: [0.00852537 0.00856757 0.00852895 0.00844431 0.00855374 0.00850129 0.00854421 0.00863028 0.00860786 0.00860882] mean value: 0.008551239967346191 key: test_mcc value: [ 0.09245003 0.36514837 0.42727273 0.43007562 0.71818182 -0.15894099 0.42727273 -0.06741999 0.13483997 -0.04545455] mean value: 0.23234257449958196 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.69565217 0.7 0.66666667 0.85714286 0.33333333 0.72727273 0.56 0.64 0.47619048] mean value: 0.6156258234519104 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.55555556 0.66666667 0.7 0.75 0.81818182 0.375 0.72727273 0.5 0.57142857 0.5 ] mean value: 0.6164105339105339 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.45454545 0.72727273 0.7 0.6 0.9 0.3 0.72727273 0.63636364 0.72727273 0.45454545] mean value: 0.6227272727272727 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.54545455 0.68181818 0.71428571 0.71428571 0.85714286 0.42857143 0.71428571 0.47619048 0.57142857 0.47619048] mean value: 0.6179653679653679 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.54545455 0.68181818 0.71363636 0.70909091 0.85909091 0.42272727 0.71363636 0.46818182 0.56363636 0.47727273] mean value: 0.6154545454545455 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0.53333333 0.53846154 0.5 0.75 0.2 0.57142857 0.38888889 0.47058824 0.3125 ] mean value: 0.4598533900739783 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 65 mean value: 65.0 key: FP value: 40 mean value: 40.0 key: FN value: 41 mean value: 41.0 key: TP value: 66 mean value: 66.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.11 Accuracy on Blind test: 0.55 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [1.24118233 1.25259256 1.26126266 1.25328112 1.22599387 1.22450852 1.22039127 1.22889853 1.221771 1.22961187] mean value: 1.2359493732452393 key: score_time value: [0.09635234 0.09605432 0.09466815 0.08944607 0.09384155 0.09182 0.08958817 0.09731269 0.09603953 0.09355998] mean value: 0.09386827945709228 key: test_mcc value: [0.83205029 0.56694671 0.90829511 0.90909091 0.90909091 0.61818182 0.63305416 0.90909091 0.80909091 0.52295779] mean value: 0.7617849518009066 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.91666667 0.8 0.94736842 0.95238095 0.95238095 0.8 0.8 0.95238095 0.90909091 0.7826087 ] mean value: 0.8812877549605238 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.84615385 0.71428571 1. 0.90909091 0.90909091 0.8 0.88888889 1. 0.90909091 0.75 ] mean value: 0.8726601176601175 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.90909091 0.9 1. 1. 0.8 0.72727273 0.90909091 0.90909091 0.81818182] mean value: 0.8972727272727272 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.90909091 0.77272727 0.95238095 0.95238095 0.95238095 0.80952381 0.80952381 0.95238095 0.9047619 0.76190476] mean value: 0.8777056277056279 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.90909091 0.77272727 0.95 0.95454545 0.95454545 0.80909091 0.81363636 0.95454545 0.90454545 0.75909091] mean value: 0.8781818181818183 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.84615385 0.66666667 0.9 0.90909091 0.90909091 0.66666667 0.66666667 0.90909091 0.83333333 0.64285714] mean value: 0.794961704961705 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 91 mean value: 91.0 key: FP value: 11 mean value: 11.0 key: FN value: 15 mean value: 15.0 key: TP value: 95 mean value: 95.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.8 Accuracy on Blind test: 0.9 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.83920407 0.89417028 0.90412354 0.90621185 0.87500978 0.87239695 0.8454318 0.97728825 0.8785305 0.85419989] mean value: 0.8846566915512085 key: score_time value: [0.15499449 0.18715811 0.16653514 0.1395185 0.19633818 0.17854738 0.18856549 0.19819713 0.19455981 0.18306184] mean value: 0.17874760627746583 key: test_mcc value: [0.75592895 0.46225016 0.90829511 0.82572282 0.90909091 0.52727273 0.74795759 0.90909091 0.80909091 0.42727273] mean value: 0.7281972813438443 key: train_mcc value: [0.95810708 0.95874497 0.96863692 0.93798081 0.94810203 0.93798081 0.94811895 0.95832877 0.94769737 0.95832877] mean value: 0.9522026470211411 key: test_fscore value: [0.88 0.75 0.94736842 0.90909091 0.95238095 0.76190476 0.84210526 0.95238095 0.90909091 0.72727273] mean value: 0.8631594896331739 key: train_fscore value: [0.97916667 0.97938144 0.98445596 0.96938776 0.97435897 0.96938776 0.97409326 0.97916667 0.97382199 0.97916667] mean value: 0.9762387140188749 key: test_precision value: [0.78571429 0.69230769 1. 0.83333333 0.90909091 0.72727273 1. 1. 0.90909091 0.72727273] mean value: 0.8584082584082584 key: train_precision value: [0.96907216 0.95959596 0.97938144 0.95 0.95959596 0.95 0.95918367 0.96907216 0.96875 0.96907216] mean value: 0.9633723530805636 key: test_recall value: [1. 0.81818182 0.9 1. 1. 0.8 0.72727273 0.90909091 0.90909091 0.72727273] mean value: 0.8790909090909089 key: train_recall value: [0.98947368 1. 0.98958333 0.98958333 0.98958333 0.98958333 0.98947368 0.98947368 0.97894737 0.98947368] mean value: 0.9895175438596493 key: test_accuracy value: [0.86363636 0.72727273 0.95238095 0.9047619 0.95238095 0.76190476 0.85714286 0.95238095 0.9047619 0.71428571] mean value: 0.859090909090909 key: train_accuracy value: [0.97894737 0.97894737 0.98429319 0.96858639 0.97382199 0.96858639 0.97382199 0.97905759 0.97382199 0.97905759] mean value: 0.9758941857260954 key: test_roc_auc value: [0.86363636 0.72727273 0.95 0.90909091 0.95454545 0.76363636 0.86363636 0.95454545 0.90454545 0.71363636] mean value: 0.8604545454545454 key: train_roc_auc value: [0.97894737 0.97894737 0.98426535 0.96847588 0.97373904 0.96847588 0.97390351 0.97911184 0.97384868 0.97911184] mean value: 0.9758826754385966 key: test_jcc value: [0.78571429 0.6 0.9 0.83333333 0.90909091 0.61538462 0.72727273 0.90909091 0.83333333 0.57142857] mean value: 0.7684648684648685 key: train_jcc value: [0.95918367 0.95959596 0.96938776 0.94059406 0.95 0.94059406 0.94949495 0.95918367 0.94897959 0.95918367] mean value: 0.9536197395249729 key: TN value: 89 mean value: 89.0 key: FP value: 13 mean value: 13.0 key: FN value: 17 mean value: 17.0 key: TP value: 93 mean value: 93.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.78 Accuracy on Blind test: 0.9 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.06178808 0.04554296 0.04399633 0.04482245 0.21215343 0.04420877 0.04602003 0.04388404 0.04623508 0.04778576] mean value: 0.0636436939239502 key: score_time value: [0.01035094 0.01071358 0.01052022 0.01035261 0.01173902 0.01051378 0.01033735 0.01026654 0.01043367 0.01034498] mean value: 0.010557270050048828 key: test_mcc value: [1. 0.91287093 0.90829511 1. 0.90909091 0.90829511 0.80909091 0.90909091 0.80909091 0.90829511] mean value: 0.9074119884226655 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.95652174 0.94736842 1. 0.95238095 0.94736842 0.90909091 0.95238095 0.90909091 0.95652174] mean value: 0.9530724043309856 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.91666667 1. 1. 0.90909091 1. 0.90909091 1. 0.90909091 0.91666667] mean value: 0.9560606060606058 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.9 1. 1. 0.9 0.90909091 0.90909091 0.90909091 1. ] mean value: 0.9527272727272725 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.95454545 0.95238095 1. 0.95238095 0.95238095 0.9047619 0.95238095 0.9047619 0.95238095] mean value: 0.9525974025974027 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.95454545 0.95 1. 0.95454545 0.95 0.90454545 0.95454545 0.90454545 0.95 ] mean value: 0.9522727272727272 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.91666667 0.9 1. 0.90909091 0.9 0.83333333 0.90909091 0.83333333 0.91666667] mean value: 0.9118181818181817 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 101 mean value: 101.0 key: FP value: 5 mean value: 5.0 key: FN value: 5 mean value: 5.0 key: TP value: 101 mean value: 101.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.89 Accuracy on Blind test: 0.95 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02883172 0.05682755 0.05887222 0.06574631 0.0676229 0.05554724 0.06847453 0.04513168 0.02737117 0.05307961] mean value: 0.05275049209594727 key: score_time value: [0.02276325 0.02177358 0.02305865 0.02589798 0.01629591 0.02290034 0.02269125 0.01211476 0.01202202 0.02856231] mean value: 0.02080800533294678 key: test_mcc value: [0.83205029 0.63636364 0.90909091 0.80909091 0.71562645 0.71818182 0.44038551 0.90909091 0.61818182 0.62641448] mean value: 0.7214476731026188 key: train_mcc value: [0.97894737 1. 0.97905702 1. 0.98958333 1. 1. 0.97927405 1. 1. ] mean value: 0.9926861768392901 key: test_fscore value: [0.9 0.81818182 0.95238095 0.9 0.84210526 0.85714286 0.7 0.95238095 0.81818182 0.83333333] mean value: 0.8573706994759627 key: train_fscore value: [0.98947368 1. 0.98958333 1. 0.9947644 1. 1. 0.98958333 1. 1. ] mean value: 0.9963404748782952 key: test_precision value: [1. 0.81818182 0.90909091 0.9 0.88888889 0.81818182 0.77777778 1. 0.81818182 0.76923077] mean value: 0.86995337995338 key: train_precision value: [0.98947368 1. 0.98958333 1. 1. 1. 1. 0.97938144 1. 1. ] mean value: 0.9958438460842828 key: test_recall value: [0.81818182 0.81818182 1. 0.9 0.8 0.9 0.63636364 0.90909091 0.81818182 0.90909091] mean value: 0.8509090909090908 key: train_recall value: [0.98947368 1. 0.98958333 1. 0.98958333 1. 1. 1. 1. 1. ] mean value: 0.9968640350877193 key: test_accuracy value: [0.90909091 0.81818182 0.95238095 0.9047619 0.85714286 0.85714286 0.71428571 0.95238095 0.80952381 0.80952381] mean value: 0.8584415584415584 key: train_accuracy value: [0.98947368 1. 0.9895288 1. 0.9947644 1. 1. 0.9895288 1. 1. ] mean value: 0.9963295673739323 key: test_roc_auc value: [0.90909091 0.81818182 0.95454545 0.90454545 0.85454545 0.85909091 0.71818182 0.95454545 0.80909091 0.80454545] mean value: 0.8586363636363636 key: train_roc_auc value: [0.98947368 1. 0.98952851 1. 0.99479167 1. 1. 0.98958333 1. 1. ] mean value: 0.9963377192982457 key: test_jcc value: [0.81818182 0.69230769 0.90909091 0.81818182 0.72727273 0.75 0.53846154 0.90909091 0.69230769 0.71428571] mean value: 0.756918081918082 key: train_jcc value: [0.97916667 1. 0.97938144 1. 0.98958333 1. 1. 0.97938144 1. 1. ] mean value: 0.9927512886597938 key: TN value: 92 mean value: 92.0 key: FP value: 16 mean value: 16.0 key: FN value: 14 mean value: 14.0 key: TP value: 90 mean value: 90.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.59 Accuracy on Blind test: 0.81 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.02240634 0.00912261 0.00886822 0.00868654 0.00866103 0.00864649 0.00873685 0.00869918 0.00888872 0.00869131] mean value: 0.010140728950500489 key: score_time value: [0.0115993 0.00894094 0.00857878 0.00843859 0.00846052 0.0084939 0.00852418 0.00846744 0.00855279 0.00850368] mean value: 0.00885601043701172 key: test_mcc value: [ 0.09090909 0.18257419 0.61818182 0.33636364 0.42727273 0.13483997 0.24771685 0.43007562 -0.06741999 0.03739788] mean value: 0.24379117869663122 key: train_mcc value: [0.36850272 0.37896836 0.38226912 0.39449154 0.41362292 0.36131235 0.41359649 0.36156007 0.45732729 0.37171053] mean value: 0.39033613998907246 key: test_fscore value: [0.54545455 0.57142857 0.8 0.66666667 0.7 0.47058824 0.6 0.75 0.56 0.58333333] mean value: 0.6247471352177235 key: train_fscore value: [0.6875 0.69109948 0.6974359 0.71287129 0.71134021 0.68717949 0.70526316 0.67027027 0.73737374 0.68421053] mean value: 0.6984544046223988 key: test_precision value: [0.54545455 0.6 0.8 0.63636364 0.7 0.57142857 0.66666667 0.69230769 0.5 0.53846154] mean value: 0.6250682650682651 key: train_precision value: [0.68041237 0.6875 0.68686869 0.67924528 0.70408163 0.67676768 0.70526316 0.68888889 0.70873786 0.68421053] mean value: 0.6901976087619398 key: test_recall value: [0.54545455 0.54545455 0.8 0.7 0.7 0.4 0.54545455 0.81818182 0.63636364 0.63636364] mean value: 0.6327272727272728 key: train_recall value: [0.69473684 0.69473684 0.70833333 0.75 0.71875 0.69791667 0.70526316 0.65263158 0.76842105 0.68421053] mean value: 0.7075 key: test_accuracy value: [0.54545455 0.59090909 0.80952381 0.66666667 0.71428571 0.57142857 0.61904762 0.71428571 0.47619048 0.52380952] mean value: 0.6231601731601731 key: train_accuracy value: [0.68421053 0.68947368 0.69109948 0.69633508 0.70680628 0.68062827 0.70680628 0.68062827 0.72774869 0.68586387] mean value: 0.6949600440892809 key: test_roc_auc value: [0.54545455 0.59090909 0.80909091 0.66818182 0.71363636 0.56363636 0.62272727 0.70909091 0.46818182 0.51818182] mean value: 0.6209090909090909 key: train_roc_auc value: [0.68421053 0.68947368 0.69100877 0.69605263 0.70674342 0.68053728 0.70679825 0.68048246 0.72796053 0.68585526] mean value: 0.6949122807017544 key: test_jcc value: [0.375 0.4 0.66666667 0.5 0.53846154 0.30769231 0.42857143 0.6 0.38888889 0.41176471] mean value: 0.46170455361631835 key: train_jcc value: [0.52380952 0.528 0.53543307 0.55384615 0.552 0.5234375 0.54471545 0.50406504 0.584 0.52 ] mean value: 0.5369306736326698 key: TN value: 65 mean value: 65.0 key: FP value: 39 mean value: 39.0 key: FN value: 41 mean value: 41.0 key: TP value: 67 mean value: 67.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.32 Accuracy on Blind test: 0.68 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01359797 0.01863289 0.01495814 0.01887131 0.01808858 0.01531839 0.01982331 0.01887655 0.01818299 0.01601124] mean value: 0.01723613739013672 key: score_time value: [0.00850892 0.01137233 0.01131248 0.01158357 0.01162577 0.01158118 0.01159835 0.0116539 0.01165724 0.01160526] mean value: 0.011249899864196777 key: test_mcc value: [0.61237244 0.81818182 0.90909091 0.82275335 0.62641448 0.30934411 0.71818182 0.90829511 0.61818182 0.24120908] mean value: 0.6584024928181599 key: train_mcc value: [0.85951623 0.96847471 0.91801541 1. 0.92922547 0.39904191 1. 0.96906883 0.92917291 0.94811895] mean value: 0.8920634419087234 key: test_fscore value: [0.70588235 0.90909091 0.95238095 0.88888889 0.77777778 0.68965517 0.85714286 0.95652174 0.81818182 0.69230769] mean value: 0.82478301602563 key: train_fscore value: [0.92134831 0.98429319 0.95959596 1. 0.96216216 0.73563218 1. 0.98445596 0.96174863 0.97409326] mean value: 0.9483329670667896 key: test_precision value: [1. 0.90909091 0.90909091 1. 0.875 0.52631579 0.9 0.91666667 0.81818182 0.6 ] mean value: 0.8454346092503988 key: train_precision value: [0.98795181 0.97916667 0.93137255 1. 1. 0.58181818 1. 0.96938776 1. 0.95918367] mean value: 0.94088806333048 key: test_recall value: [0.54545455 0.90909091 1. 0.8 0.7 1. 0.81818182 1. 0.81818182 0.81818182] mean value: 0.840909090909091 key: train_recall value: [0.86315789 0.98947368 0.98958333 1. 0.92708333 1. 1. 1. 0.92631579 0.98947368] mean value: 0.9685087719298245 key: test_accuracy value: [0.77272727 0.90909091 0.95238095 0.9047619 0.80952381 0.57142857 0.85714286 0.95238095 0.80952381 0.61904762] mean value: 0.8158008658008657 key: train_accuracy value: [0.92631579 0.98421053 0.95811518 1. 0.96335079 0.63874346 1. 0.98429319 0.96335079 0.97382199] mean value: 0.9392201708459631 key: test_roc_auc value: [0.77272727 0.90909091 0.95454545 0.9 0.80454545 0.59090909 0.85909091 0.95 0.80909091 0.60909091] mean value: 0.8159090909090908 key: train_roc_auc value: [0.92631579 0.98421053 0.95794956 1. 0.96354167 0.63684211 1. 0.984375 0.96315789 0.97390351] mean value: 0.9390296052631578 key: test_jcc value: [0.54545455 0.83333333 0.90909091 0.8 0.63636364 0.52631579 0.75 0.91666667 0.69230769 0.52941176] mean value: 0.7138944337396349 key: train_jcc value: [0.85416667 0.96907216 0.9223301 1. 0.92708333 0.58181818 1. 0.96938776 0.92631579 0.94949495] mean value: 0.9099668937924689 key: TN value: 84 mean value: 84.0 key: FP value: 17 mean value: 17.0 key: FN value: 22 mean value: 22.0 key: TP value: 89 mean value: 89.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.7 Accuracy on Blind test: 0.87 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01456928 0.01509285 0.01491833 0.01472616 0.01364017 0.01490045 0.01457667 0.01463819 0.0145216 0.01388884] mean value: 0.014547252655029297 key: score_time value: [0.01182294 0.01178408 0.01197004 0.01183724 0.01172924 0.01182938 0.01196957 0.01184297 0.01184607 0.0117929 ] mean value: 0.011842441558837891 key: test_mcc value: [0.68313005 0.73029674 0.90909091 0.90829511 0.45226702 0.55161872 0.4719399 0.90909091 0.34027852 0.15569979] mean value: 0.6111707676032829 key: train_mcc value: [0.87856517 0.92884073 0.92795293 1. 0.57673971 0.79066814 0.79946001 0.87603267 0.77378259 0.70617821] mean value: 0.8258220151047642 key: test_fscore value: [0.77777778 0.86956522 0.95238095 0.94736842 0.625 0.7826087 0.66666667 0.95238095 0.73333333 0.68965517] mean value: 0.7996737189049584 key: train_fscore value: [0.93333333 0.96446701 0.96446701 1. 0.68027211 0.89719626 0.87573964 0.93478261 0.88785047 0.85585586] mean value: 0.899396429082304 key: test_precision value: [1. 0.83333333 0.90909091 1. 0.83333333 0.69230769 0.85714286 1. 0.57894737 0.55555556] mean value: 0.8259711049184734 key: train_precision value: [0.98823529 0.93137255 0.94059406 1. 0.98039216 0.81355932 1. 0.96629213 0.79831933 0.7480315 ] mean value: 0.9166796340065385 key: test_recall value: [0.63636364 0.90909091 1. 0.9 0.5 0.9 0.54545455 0.90909091 1. 0.90909091] mean value: 0.8209090909090909 key: train_recall value: [0.88421053 1. 0.98958333 1. 0.52083333 1. 0.77894737 0.90526316 1. 1. ] mean value: 0.9078837719298246 key: test_accuracy value: [0.81818182 0.86363636 0.95238095 0.95238095 0.71428571 0.76190476 0.71428571 0.95238095 0.61904762 0.57142857] mean value: 0.7919913419913419 key: train_accuracy value: [0.93684211 0.96315789 0.96335079 1. 0.7539267 0.88481675 0.89005236 0.93717277 0.87434555 0.83246073] mean value: 0.9036125654450261 key: test_roc_auc value: [0.81818182 0.86363636 0.95454545 0.95 0.70454545 0.76818182 0.72272727 0.95454545 0.6 0.55454545] mean value: 0.7890909090909091 key: train_roc_auc value: [0.93684211 0.96315789 0.96321272 1. 0.75515351 0.88421053 0.88947368 0.93700658 0.875 0.83333333] mean value: 0.9037390350877195 key: test_jcc value: [0.63636364 0.76923077 0.90909091 0.9 0.45454545 0.64285714 0.5 0.90909091 0.57894737 0.52631579] mean value: 0.6826441979073559 key: train_jcc value: [0.875 0.93137255 0.93137255 1. 0.51546392 0.81355932 0.77894737 0.87755102 0.79831933 0.7480315 ] mean value: 0.8269617550222188 key: TN value: 81 mean value: 81.0 key: FP value: 19 mean value: 19.0 key: FN value: 25 mean value: 25.0 key: TP value: 87 mean value: 87.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.57 Accuracy on Blind test: 0.81 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.12517595 0.10848331 0.10823202 0.1077652 0.10984206 0.10818481 0.10810757 0.1085639 0.10866189 0.10933065] mean value: 0.11023473739624023 key: score_time value: [0.01501179 0.01486182 0.01477385 0.01486659 0.01476145 0.01496387 0.01477718 0.01481032 0.0147624 0.01616335] mean value: 0.014975261688232423 key: test_mcc value: [1. 0.63636364 0.80909091 1. 0.90909091 0.90909091 0.80909091 0.90909091 0.80909091 0.90829511] mean value: 0.8699204197138339 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.81818182 0.9 1. 0.95238095 0.95238095 0.90909091 0.95238095 0.90909091 0.95652174] mean value: 0.9350028232636929 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.81818182 0.9 1. 0.90909091 0.90909091 0.90909091 1. 0.90909091 0.91666667] mean value: 0.927121212121212 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.81818182 0.9 1. 1. 1. 0.90909091 0.90909091 0.90909091 1. ] mean value: 0.9445454545454546 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.81818182 0.9047619 1. 0.95238095 0.95238095 0.9047619 0.95238095 0.9047619 0.95238095] mean value: 0.9341991341991343 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.81818182 0.90454545 1. 0.95454545 0.95454545 0.90454545 0.95454545 0.90454545 0.95 ] mean value: 0.9345454545454543 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.69230769 0.81818182 1. 0.90909091 0.90909091 0.83333333 0.90909091 0.83333333 0.91666667] mean value: 0.8821095571095571 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 98 mean value: 98.0 key: FP value: 6 mean value: 6.0 key: FN value: 8 mean value: 8.0 key: TP value: 100 mean value: 100.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.84 Accuracy on Blind test: 0.93 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.03018928 0.03429747 0.03949785 0.05620551 0.03740382 0.03586698 0.03402066 0.03567505 0.03418803 0.04045534] mean value: 0.03777999877929687 key: score_time value: [0.01721668 0.02065182 0.02762413 0.02365732 0.0261569 0.01647234 0.02367759 0.0223124 0.0182445 0.02581906] mean value: 0.02218327522277832 key: test_mcc value: [1. 0.83205029 0.90829511 0.82275335 0.80909091 1. 0.80909091 0.90909091 0.80909091 0.90829511] mean value: 0.8807757494367419 key: train_mcc value: [1. 0.98952851 1. 0.97905702 0.97905702 0.97905702 1. 0.98958333 0.9895822 1. ] mean value: 0.9905865090197933 key: test_fscore value: [1. 0.91666667 0.94736842 0.88888889 0.9 1. 0.90909091 0.95238095 0.90909091 0.95652174] mean value: 0.9380008486301392 key: train_fscore value: [1. 0.9947644 1. 0.98958333 0.98958333 0.98958333 1. 0.9947644 0.99470899 1. ] mean value: 0.9952987790520513 key: test_precision value: [1. 0.84615385 1. 1. 0.9 1. 0.90909091 1. 0.90909091 0.91666667] mean value: 0.948100233100233 key: train_precision value: [1. 0.98958333 1. 0.98958333 0.98958333 0.98958333 1. 0.98958333 1. 1. ] mean value: 0.9947916666666667 key: test_recall value: [1. 1. 0.9 0.8 0.9 1. 0.90909091 0.90909091 0.90909091 1. ] mean value: 0.9327272727272726 key: train_recall value: [1. 1. 1. 0.98958333 0.98958333 0.98958333 1. 1. 0.98947368 1. ] mean value: 0.9958223684210527 key: test_accuracy value: [1. 0.90909091 0.95238095 0.9047619 0.9047619 1. 0.9047619 0.95238095 0.9047619 0.95238095] mean value: 0.9385281385281387 key: train_accuracy value: [1. 0.99473684 1. 0.9895288 0.9895288 0.9895288 1. 0.9947644 0.9947644 1. ] mean value: 0.9952852025351338 key: test_roc_auc value: [1. 0.90909091 0.95 0.9 0.90454545 1. 0.90454545 0.95454545 0.90454545 0.95 ] mean value: 0.9377272727272727 key: train_roc_auc value: [1. 0.99473684 1. 0.98952851 0.98952851 0.98952851 1. 0.99479167 0.99473684 1. ] mean value: 0.9952850877192982 key: test_jcc value: [1. 0.84615385 0.9 0.8 0.81818182 1. 0.83333333 0.90909091 0.83333333 0.91666667] mean value: 0.8856759906759907 key: train_jcc value: [1. 0.98958333 1. 0.97938144 0.97938144 0.97938144 1. 0.98958333 0.98947368 1. ] mean value: 0.99067846807741 key: TN value: 100 mean value: 100.0 key: FP value: 7 mean value: 7.0 key: FN value: 6 mean value: 6.0 key: TP value: 99 mean value: 99.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.87 Accuracy on Blind test: 0.94 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.03967857 0.04909682 0.06145763 0.02647614 0.02489495 0.07871032 0.03897619 0.02560925 0.02572608 0.06365681] mean value: 0.04342827796936035 key: score_time value: [0.02542925 0.02239847 0.01280093 0.0128448 0.01276064 0.02025652 0.01270938 0.01264906 0.01257157 0.01288128] mean value: 0.01573019027709961 key: test_mcc value: [ 0.09245003 0.27272727 0.33028913 0.71818182 0.61818182 0.13483997 0.24771685 0.23373675 -0.05504819 -0.35527986] mean value: 0.22377955940216845 key: train_mcc value: [0.94784115 0.94742091 0.94769737 0.94769164 0.93717105 0.94769737 0.91641992 0.95831967 0.95832877 0.97905702] mean value: 0.9487644872499189 key: test_fscore value: [0.5 0.63636364 0.63157895 0.85714286 0.8 0.47058824 0.6 0.66666667 0.52173913 0.41666667] mean value: 0.6100746139937148 key: train_fscore value: [0.97326203 0.97382199 0.97382199 0.97409326 0.96875 0.97382199 0.95744681 0.9787234 0.97916667 0.98947368] mean value: 0.9742381828563804 key: test_precision value: [0.55555556 0.63636364 0.66666667 0.81818182 0.8 0.57142857 0.66666667 0.61538462 0.5 0.38461538] mean value: 0.6214862914862915 key: train_precision value: [0.98913043 0.96875 0.97894737 0.96907216 0.96875 0.97894737 0.96774194 0.98924731 0.96907216 0.98947368] mean value: 0.9769132433043974 key: test_recall value: [0.45454545 0.63636364 0.6 0.9 0.8 0.4 0.54545455 0.72727273 0.54545455 0.45454545] mean value: 0.6063636363636363 key: train_recall value: [0.95789474 0.97894737 0.96875 0.97916667 0.96875 0.96875 0.94736842 0.96842105 0.98947368 0.98947368] mean value: 0.9716995614035089 key: test_accuracy value: [0.54545455 0.63636364 0.66666667 0.85714286 0.80952381 0.57142857 0.61904762 0.61904762 0.47619048 0.33333333] mean value: 0.6134199134199133 key: train_accuracy value: [0.97368421 0.97368421 0.97382199 0.97382199 0.96858639 0.97382199 0.95811518 0.97905759 0.97905759 0.9895288 ] mean value: 0.9743179939377239 key: test_roc_auc value: [0.54545455 0.63636364 0.66363636 0.85909091 0.80909091 0.56363636 0.62272727 0.61363636 0.47272727 0.32727273] mean value: 0.6113636363636362 key: train_roc_auc value: [0.97368421 0.97368421 0.97384868 0.97379386 0.96858553 0.97384868 0.95805921 0.97900219 0.97911184 0.98952851] mean value: 0.9743146929824562 key: test_jcc value: [0.33333333 0.46666667 0.46153846 0.75 0.66666667 0.30769231 0.42857143 0.5 0.35294118 0.26315789] mean value: 0.4530567935676295 key: train_jcc value: [0.94791667 0.94897959 0.94897959 0.94949495 0.93939394 0.94897959 0.91836735 0.95833333 0.95918367 0.97916667] mean value: 0.9498795351473923 key: TN value: 66 mean value: 66.0 key: FP value: 42 mean value: 42.0 key: FN value: 40 mean value: 40.0 key: TP value: 64 mean value: 64.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.26 Accuracy on Blind test: 0.61 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.37137389 0.33760333 0.33995962 0.34063864 0.3470366 0.3378973 0.3002677 0.34190512 0.30660367 0.34101677] mean value: 0.3364302635192871 key: score_time value: [0.00910854 0.00920963 0.00930381 0.00901294 0.00916195 0.00908446 0.00947428 0.00910878 0.00905728 0.00914216] mean value: 0.009166383743286132 key: test_mcc value: [1. 0.83205029 0.90909091 0.82275335 0.80909091 0.90909091 0.71818182 1. 0.80909091 0.90829511] mean value: 0.8717644206319989 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.91666667 0.95238095 0.88888889 0.9 0.95238095 0.85714286 1. 0.90909091 0.95652174] mean value: 0.9333072965681662 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.84615385 0.90909091 1. 0.9 0.90909091 0.9 1. 0.90909091 0.91666667] mean value: 0.9290093240093238 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.8 0.9 1. 0.81818182 1. 0.90909091 1. ] mean value: 0.9427272727272726 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.90909091 0.95238095 0.9047619 0.9047619 0.95238095 0.85714286 1. 0.9047619 0.95238095] mean value: 0.9337662337662339 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.90909091 0.95454545 0.9 0.90454545 0.95454545 0.85909091 1. 0.90454545 0.95 ] mean value: 0.9336363636363636 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.84615385 0.90909091 0.8 0.81818182 0.90909091 0.75 1. 0.83333333 0.91666667] mean value: 0.8782517482517482 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 98 mean value: 98.0 key: FP value: 6 mean value: 6.0 key: FN value: 8 mean value: 8.0 key: TP value: 100 mean value: 100.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.84 Accuracy on Blind test: 0.92 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01949501 0.02266788 0.02243805 0.02208471 0.02231646 0.02282453 0.02309084 0.0220356 0.02272868 0.02262187] mean value: 0.022230362892150878 key: score_time value: [0.01193953 0.01205492 0.01245713 0.01253104 0.01255751 0.01247668 0.01246691 0.01270652 0.01268959 0.01259542] mean value: 0.012447524070739745 key: test_mcc value: [-0.09245003 0.20412415 0.18090681 0.18090681 0.21968621 0.06741999 0.03739788 -0.13762047 -0.13762047 -0.03015113] mean value: 0.04925997289082072 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.66666667 0.64 0.64 0.66666667 0.58333333 0.58333333 0.4 0.4 0.35294118] mean value: 0.5432941176470588 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.46153846 0.5625 0.53333333 0.53333333 0.52941176 0.5 0.53846154 0.44444444 0.44444444 0.5 ] mean value: 0.5047467320261438 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.54545455 0.81818182 0.8 0.8 0.9 0.7 0.63636364 0.36363636 0.36363636 0.27272727] mean value: 0.62 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.45454545 0.59090909 0.57142857 0.57142857 0.57142857 0.52380952 0.52380952 0.42857143 0.42857143 0.47619048] mean value: 0.5140692640692641 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.45454545 0.59090909 0.58181818 0.58181818 0.58636364 0.53181818 0.51818182 0.43181818 0.43181818 0.48636364] mean value: 0.5195454545454545 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0.5 0.47058824 0.47058824 0.5 0.41176471 0.41176471 0.25 0.25 0.21428571] mean value: 0.38123249299719886 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 42 mean value: 42.0 key: FP value: 43 mean value: 43.0 key: FN value: 64 mean value: 64.0 key: TP value: 63 mean value: 63.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.13 Accuracy on Blind test: 0.57 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.0221839 0.03195024 0.03572011 0.03568769 0.03169751 0.03557086 0.03364134 0.03437328 0.03475642 0.03443766] mean value: 0.03300189971923828 key: score_time value: [0.02333236 0.02400351 0.02397799 0.02430987 0.02144504 0.02073979 0.02383351 0.02371478 0.02098441 0.02247763] mean value: 0.02288188934326172 key: test_mcc value: [0.81818182 0.73029674 1. 1. 0.90909091 0.71818182 0.71818182 1. 0.61818182 0.62641448] mean value: 0.813852940846407 key: train_mcc value: [0.94784115 0.95810708 0.95831967 0.9690588 0.96863692 0.95831967 0.95832877 0.95832877 0.97927405 0.96864035] mean value: 0.9624855237784811 key: test_fscore value: [0.90909091 0.86956522 1. 1. 0.95238095 0.85714286 0.85714286 1. 0.81818182 0.83333333] mean value: 0.9096837944664034 key: train_fscore value: [0.97409326 0.97916667 0.97938144 0.98461538 0.98445596 0.97938144 0.97916667 0.97916667 0.98958333 0.98429319] mean value: 0.9813304021061862 key: test_precision value: [0.90909091 0.83333333 1. 1. 0.90909091 0.81818182 0.9 1. 0.81818182 0.76923077] mean value: 0.8957109557109557 key: train_precision value: [0.95918367 0.96907216 0.96938776 0.96969697 0.97938144 0.96938776 0.96907216 0.96907216 0.97938144 0.97916667] mean value: 0.9712802201480404 key: test_recall value: [0.90909091 0.90909091 1. 1. 1. 0.9 0.81818182 1. 0.81818182 0.90909091] mean value: 0.9263636363636364 key: train_recall value: [0.98947368 0.98947368 0.98958333 1. 0.98958333 0.98958333 0.98947368 0.98947368 1. 0.98947368] mean value: 0.9916118421052632 key: test_accuracy value: [0.90909091 0.86363636 1. 1. 0.95238095 0.85714286 0.85714286 1. 0.80952381 0.80952381] mean value: 0.905844155844156 key: train_accuracy value: [0.97368421 0.97894737 0.97905759 0.98429319 0.98429319 0.97905759 0.97905759 0.97905759 0.9895288 0.98429319] mean value: 0.9811270322402865 key: test_roc_auc value: [0.90909091 0.86363636 1. 1. 0.95454545 0.85909091 0.85909091 1. 0.80909091 0.80454545] mean value: 0.9059090909090909 key: train_roc_auc value: [0.97368421 0.97894737 0.97900219 0.98421053 0.98426535 0.97900219 0.97911184 0.97911184 0.98958333 0.98432018] mean value: 0.981123903508772 key: test_jcc value: [0.83333333 0.76923077 1. 1. 0.90909091 0.75 0.75 1. 0.69230769 0.71428571] mean value: 0.841824841824842 key: train_jcc value: [0.94949495 0.95918367 0.95959596 0.96969697 0.96938776 0.95959596 0.95918367 0.95918367 0.97938144 0.96907216] mean value: 0.9633776222141466 key: TN value: 94 mean value: 94.0 key: FP value: 8 mean value: 8.0 key: FN value: 12 mean value: 12.0 key: TP value: 98 mean value: 98.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.74 Accuracy on Blind test: 0.88 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.2252419 0.22955441 0.22431469 0.22547555 0.24449944 0.23760462 0.29956794 0.22531152 0.23448348 0.22501922] mean value: 0.2371072769165039 key: score_time value: [0.02095652 0.02331495 0.02272773 0.02966738 0.02431035 0.02423763 0.02446938 0.02391839 0.02422833 0.0242424 ] mean value: 0.024207305908203126 key: test_mcc value: [0.81818182 0.73029674 1. 1. 0.90909091 0.71818182 0.71818182 1. 0.61818182 0.62641448] mean value: 0.813852940846407 key: train_mcc value: [0.94784115 0.95810708 0.95831967 0.9690588 0.96863692 0.95831967 0.95832877 0.95832877 0.97927405 0.96864035] mean value: 0.9624855237784811 key: test_fscore value: [0.90909091 0.86956522 1. 1. 0.95238095 0.85714286 0.85714286 1. 0.81818182 0.83333333] mean value: 0.9096837944664034 key: train_fscore value: [0.97409326 0.97916667 0.97938144 0.98461538 0.98445596 0.97938144 0.97916667 0.97916667 0.98958333 0.98429319] mean value: 0.9813304021061862 key: test_precision value: [0.90909091 0.83333333 1. 1. 0.90909091 0.81818182 0.9 1. 0.81818182 0.76923077] mean value: 0.8957109557109557 key: train_precision value: [0.95918367 0.96907216 0.96938776 0.96969697 0.97938144 0.96938776 0.96907216 0.96907216 0.97938144 0.97916667] mean value: 0.9712802201480404 key: test_recall value: [0.90909091 0.90909091 1. 1. 1. 0.9 0.81818182 1. 0.81818182 0.90909091] mean value: 0.9263636363636364 key: train_recall value: [0.98947368 0.98947368 0.98958333 1. 0.98958333 0.98958333 0.98947368 0.98947368 1. 0.98947368] mean value: 0.9916118421052632 key: test_accuracy value: [0.90909091 0.86363636 1. 1. 0.95238095 0.85714286 0.85714286 1. 0.80952381 0.80952381] mean value: 0.905844155844156 key: train_accuracy value: [0.97368421 0.97894737 0.97905759 0.98429319 0.98429319 0.97905759 0.97905759 0.97905759 0.9895288 0.98429319] mean value: 0.9811270322402865 key: test_roc_auc value: [0.90909091 0.86363636 1. 1. 0.95454545 0.85909091 0.85909091 1. 0.80909091 0.80454545] mean value: 0.9059090909090909 key: train_roc_auc value: [0.97368421 0.97894737 0.97900219 0.98421053 0.98426535 0.97900219 0.97911184 0.97911184 0.98958333 0.98432018] mean value: 0.981123903508772 key: test_jcc value: [0.83333333 0.76923077 1. 1. 0.90909091 0.75 0.75 1. 0.69230769 0.71428571] mean value: 0.841824841824842 key: train_jcc value: [0.94949495 0.95918367 0.95959596 0.96969697 0.96938776 0.95959596 0.95918367 0.95918367 0.97938144 0.96907216] mean value: 0.9633776222141466 key: TN value: 94 mean value: 94.0 key: FP value: 8 mean value: 8.0 key: FN value: 12 mean value: 12.0 key: TP value: 98 mean value: 98.0 key: trainingY_neg value: 106 mean value: 106.0 key: trainingY_pos value: 106 mean value: 106.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:356: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:357: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_CV['Resampling'] = rs_rus /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:362: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:363: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_BT['Resampling'] = rs_rus /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( MCC on Blind test: 0.74 Accuracy on Blind test: 0.88 PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.03361392 0.02674818 0.03462672 0.03609133 0.03604364 0.03495097 0.03474545 0.03721118 0.05540252 0.05710363] mean value: 0.03865375518798828 key: score_time value: [0.01211977 0.01200271 0.01244116 0.01268816 0.0126245 0.01258707 0.01261234 0.01320767 0.01244402 0.01252508] mean value: 0.012525248527526855 key: test_mcc value: [0.80952381 0.8660254 0.7098505 0.90238095 0.70714286 0.85441771 0.80907152 0.95238095 0.86240942 0.95238095] mean value: 0.8425584072097785 key: train_mcc value: [0.90317784 0.92454046 0.89769524 0.91915141 0.91380162 0.93553077 0.90846996 0.91421693 0.92473841 0.93025158] mean value: 0.9171574208092543 key: test_fscore value: [0.9047619 0.92307692 0.84210526 0.95 0.85 0.92307692 0.9 0.97560976 0.93333333 0.97560976] mean value: 0.9177573859602102 key: train_fscore value: [0.95054945 0.96174863 0.94850949 0.95956873 0.95675676 0.9673913 0.95367847 0.95604396 0.96174863 0.96438356] mean value: 0.9580378989464318 key: test_precision value: [0.9047619 1. 0.88888889 0.95 0.85 0.94736842 0.94736842 1. 0.875 1. ] mean value: 0.9363387635756057 key: train_precision value: [0.96648045 0.97237569 0.95628415 0.96216216 0.96195652 0.97802198 0.96153846 0.97206704 0.97237569 0.97777778] mean value: 0.9681039921493962 key: test_recall value: [0.9047619 0.85714286 0.8 0.95 0.85 0.9 0.85714286 0.95238095 1. 0.95238095] mean value: 0.9023809523809524 key: train_recall value: [0.93513514 0.95135135 0.94086022 0.95698925 0.9516129 0.95698925 0.94594595 0.94054054 0.95135135 0.95135135] mean value: 0.9482127288578901 key: test_accuracy value: [0.9047619 0.92857143 0.85365854 0.95121951 0.85365854 0.92682927 0.90243902 0.97560976 0.92682927 0.97560976] mean value: 0.9199186991869919 key: train_accuracy value: [0.95135135 0.96216216 0.94878706 0.95956873 0.95687332 0.96765499 0.9541779 0.95687332 0.96226415 0.96495957] mean value: 0.9584672543163109 key: test_roc_auc value: [0.9047619 0.92857143 0.85238095 0.95119048 0.85357143 0.92619048 0.90357143 0.97619048 0.925 0.97619048] mean value: 0.9197619047619048 key: train_roc_auc value: [0.95135135 0.96216216 0.94880849 0.9595757 0.95688753 0.96768381 0.95415577 0.95682941 0.96223482 0.96492299] mean value: 0.9584612031386225 key: test_jcc value: [0.82608696 0.85714286 0.72727273 0.9047619 0.73913043 0.85714286 0.81818182 0.95238095 0.875 0.95238095] mean value: 0.8509481460568417 key: train_jcc value: [0.90575916 0.92631579 0.90206186 0.92227979 0.91709845 0.93684211 0.91145833 0.91578947 0.92631579 0.93121693] mean value: 0.9195137678760738 key: TN value: 193 mean value: 193.0 key: FP value: 20 mean value: 20.0 key: FN value: 13 mean value: 13.0 key: TP value: 186 mean value: 186.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.77 Accuracy on Blind test: 0.9 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.72588873 0.72638083 0.79159212 0.71572828 0.72554207 0.8414104 0.73313403 1.04889321 0.86867905 0.7414546 ] mean value: 0.7918703317642212 key: score_time value: [0.0126152 0.01262069 0.01263237 0.01266813 0.01270723 0.01270628 0.0126307 0.01389456 0.01262283 0.01270795] mean value: 0.012780594825744628 key: test_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.85811633 0.90889326 0.90649828 0.90649828 0.95238095 0.90692382 0.90692382 0.90692382 0.90238095 0.95238095] mean value: 0.9107920477231237 key: train_mcc value: [1. 0.98918919 0.98927544 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9978464631087196 key: test_fscore value: [0.93023256 0.95 0.94736842 0.94736842 0.97560976 0.95238095 0.95 0.95 0.95238095 0.97560976] mean value: 0.9530950817201823 key: train_fscore value: [1. 0.99459459 0.99465241 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9989247001011707 key: test_precision value: [0.90909091 1. 1. 1. 0.95238095 0.90909091 1. 1. 0.95238095 1. ] mean value: 0.9722943722943723 key: train_precision value: [1. 0.99459459 0.9893617 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9983956296722255 key: test_recall value: [0.95238095 0.9047619 0.9 0.9 1. 1. 0.9047619 0.9047619 0.95238095 0.95238095] mean value: 0.9371428571428572 key: train_recall value: [1. 0.99459459 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9994594594594595 key: test_accuracy value: [0.92857143 0.95238095 0.95121951 0.95121951 0.97560976 0.95121951 0.95121951 0.95121951 0.95121951 0.97560976] mean value: 0.9539488966318235 key: train_accuracy value: [1. 0.99459459 0.99460916 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.998920375901508 key: test_roc_auc value: [0.92857143 0.95238095 0.95 0.95 0.97619048 0.95238095 0.95238095 0.95238095 0.95119048 0.97619048] mean value: 0.9541666666666666 key: train_roc_auc value: [1. 0.99459459 0.99459459 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.998918918918919 key: test_jcc value: [0.86956522 0.9047619 0.9 0.9 0.95238095 0.90909091 0.9047619 0.9047619 0.90909091 0.95238095] mean value: 0.910679465462074 key: train_jcc value: [1. 0.98924731 0.9893617 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9978609013955616 key: TN value: 200 mean value: 200.0 key: FP value: 13 mean value: 13.0 key: FN value: 6 mean value: 6.0 key: TP value: 193 mean value: 193.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.86 Accuracy on Blind test: 0.94 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01363659 0.01345634 0.00994134 0.00960851 0.00949359 0.00958729 0.0094173 0.00937819 0.00946307 0.00946736] mean value: 0.010344958305358887 key: score_time value: [0.01365209 0.00971627 0.00930667 0.00895095 0.0087657 0.0089097 0.00887871 0.00876594 0.00890136 0.00898695] mean value: 0.009483432769775391 key: test_mcc value: [0.23809524 0.58834841 0.23472895 0.56836003 0.32612121 0.36666667 0.41428571 0.60952381 0.51966679 0.31655495] mean value: 0.4182351767630109 key: train_mcc value: [0.51446836 0.49423687 0.50746481 0.49629637 0.53221435 0.49149084 0.49254324 0.50655846 0.50342023 0.50200402] mean value: 0.5040697536773684 key: test_fscore value: [0.61904762 0.80851064 0.65217391 0.79069767 0.68181818 0.68292683 0.71428571 0.80952381 0.7826087 0.68181818] mean value: 0.7223411257173928 key: train_fscore value: [0.77192982 0.75773196 0.76649746 0.73295455 0.77402597 0.75949367 0.75949367 0.7628866 0.76455696 0.76732673] mean value: 0.7616897399142623 key: test_precision value: [0.61904762 0.73076923 0.57692308 0.73913043 0.625 0.66666667 0.71428571 0.80952381 0.72 0.65217391] mean value: 0.6853520465042204 key: train_precision value: [0.71962617 0.72413793 0.72596154 0.77710843 0.74874372 0.71770335 0.71428571 0.72906404 0.71904762 0.70776256] mean value: 0.7283441069150348 key: test_recall value: [0.61904762 0.9047619 0.75 0.85 0.75 0.7 0.71428571 0.80952381 0.85714286 0.71428571] mean value: 0.766904761904762 key: train_recall value: [0.83243243 0.79459459 0.81182796 0.69354839 0.80107527 0.80645161 0.81081081 0.8 0.81621622 0.83783784] mean value: 0.8004795117698343 key: test_accuracy value: [0.61904762 0.78571429 0.6097561 0.7804878 0.65853659 0.68292683 0.70731707 0.80487805 0.75609756 0.65853659] mean value: 0.7063298490127757 key: train_accuracy value: [0.75405405 0.74594595 0.75202156 0.74663073 0.76549865 0.74393531 0.74393531 0.75202156 0.74932615 0.74663073] mean value: 0.7500000000000001 key: test_roc_auc value: [0.61904762 0.78571429 0.61309524 0.78214286 0.66071429 0.68333333 0.70714286 0.8047619 0.75357143 0.65714286] mean value: 0.7066666666666668 key: train_roc_auc value: [0.75405405 0.74594595 0.75185992 0.74677419 0.7654025 0.74376635 0.74411508 0.75215054 0.74950596 0.74687591] mean value: 0.750045045045045 key: test_jcc value: [0.44827586 0.67857143 0.48387097 0.65384615 0.51724138 0.51851852 0.55555556 0.68 0.64285714 0.51724138] mean value: 0.569597838778039 key: train_jcc value: [0.62857143 0.60995851 0.62139918 0.57847534 0.63135593 0.6122449 0.6122449 0.61666667 0.61885246 0.62248996] mean value: 0.6152259261717272 key: TN value: 133 mean value: 133.0 key: FP value: 48 mean value: 48.0 key: FN value: 73 mean value: 73.0 key: TP value: 158 mean value: 158.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.38 Accuracy on Blind test: 0.72 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00960875 0.00961447 0.00955772 0.00955391 0.00958133 0.00977755 0.00968075 0.00956798 0.01065421 0.00960755] mean value: 0.009720420837402344 key: score_time value: [0.00918698 0.00876713 0.00875735 0.00879526 0.00878072 0.00886106 0.00873804 0.00886297 0.00907731 0.00888038] mean value: 0.008870720863342285 key: test_mcc value: [ 0.1490712 0.3478328 0.12293728 0.29009627 0.44466675 0.51320273 0.21904762 0.35737186 -0.06905393 0.35038478] mean value: 0.27255573395110366 key: train_mcc value: [0.39922511 0.42369499 0.42411077 0.39660058 0.40199754 0.39002634 0.4362526 0.42849689 0.41863013 0.41598338] mean value: 0.413501833326159 key: test_fscore value: [0.5 0.61111111 0.4375 0.51612903 0.625 0.73684211 0.61904762 0.48275862 0.35294118 0.58823529] mean value: 0.5469564958957842 key: train_fscore value: [0.63022508 0.6407767 0.64536741 0.61639344 0.64375 0.62619808 0.65822785 0.65830721 0.64984227 0.65420561] mean value: 0.6423293654148221 key: test_precision value: [0.6 0.73333333 0.58333333 0.72727273 0.83333333 0.77777778 0.61904762 0.875 0.46153846 0.76923077] mean value: 0.6979867354867355 key: train_precision value: [0.77777778 0.7983871 0.79527559 0.78991597 0.76865672 0.77165354 0.79389313 0.78358209 0.78030303 0.77205882] mean value: 0.7831503764370378 key: test_recall value: [0.42857143 0.52380952 0.35 0.4 0.5 0.7 0.61904762 0.33333333 0.28571429 0.47619048] mean value: 0.4616666666666666 key: train_recall value: [0.52972973 0.53513514 0.54301075 0.50537634 0.55376344 0.52688172 0.56216216 0.56756757 0.55675676 0.56756757] mean value: 0.5447951176983434 key: test_accuracy value: [0.57142857 0.66666667 0.56097561 0.63414634 0.70731707 0.75609756 0.6097561 0.63414634 0.46341463 0.65853659] mean value: 0.6262485481997677 key: train_accuracy value: [0.68918919 0.7 0.70080863 0.68463612 0.69272237 0.68463612 0.70889488 0.70619946 0.70080863 0.70080863] mean value: 0.6968704013987033 key: test_roc_auc value: [0.57142857 0.66666667 0.55595238 0.62857143 0.70238095 0.7547619 0.60952381 0.64166667 0.46785714 0.66309524] mean value: 0.6261904761904762 key: train_roc_auc value: [0.68918919 0.7 0.70123511 0.6851206 0.69309794 0.68506248 0.70850044 0.70582679 0.70042139 0.70045045] mean value: 0.6968904388259227 key: test_jcc value: [0.33333333 0.44 0.28 0.34782609 0.45454545 0.58333333 0.44827586 0.31818182 0.21428571 0.41666667] mean value: 0.38364482693718077 key: train_jcc value: [0.4600939 0.47142857 0.47641509 0.44549763 0.47465438 0.45581395 0.49056604 0.49065421 0.48130841 0.48611111] mean value: 0.47325432898515085 key: TN value: 163 mean value: 163.0 key: FP value: 111 mean value: 111.0 key: FN value: 43 mean value: 43.0 key: TP value: 95 mean value: 95.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.25 Accuracy on Blind test: 0.61 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00936174 0.00998497 0.01001406 0.00935721 0.00906157 0.0099709 0.0100174 0.01004004 0.01016951 0.01013422] mean value: 0.009811162948608398 key: score_time value: [0.01630521 0.01282525 0.012254 0.01248646 0.01178503 0.01724839 0.017205 0.01558185 0.01626253 0.0120213 ] mean value: 0.014397501945495605 key: test_mcc value: [0.14285714 0.46352358 0.21957752 0.27179142 0.46300848 0.31960727 0.57570364 0.56836003 0.27338837 0.53864117] mean value: 0.38364586184785077 key: train_mcc value: [0.61730841 0.61209986 0.58747618 0.57456037 0.58691428 0.61839695 0.55346014 0.60226766 0.60102483 0.5810842 ] mean value: 0.5934592871144924 key: test_fscore value: [0.57142857 0.64705882 0.55555556 0.57142857 0.71794872 0.61111111 0.80851064 0.76923077 0.61538462 0.72222222] mean value: 0.6589879596137418 key: train_fscore value: [0.80222841 0.79888268 0.78309859 0.76878613 0.78431373 0.8033241 0.76880223 0.79329609 0.7826087 0.78089888] mean value: 0.7866239527605026 key: test_precision value: [0.57142857 0.84615385 0.625 0.66666667 0.73684211 0.6875 0.73076923 0.83333333 0.66666667 0.86666667] mean value: 0.7231027086948141 key: train_precision value: [0.82758621 0.8265896 0.82248521 0.83125 0.81871345 0.82857143 0.79310345 0.82080925 0.84375 0.8128655 ] mean value: 0.822572408214349 key: test_recall value: [0.57142857 0.52380952 0.5 0.5 0.7 0.55 0.9047619 0.71428571 0.57142857 0.61904762] mean value: 0.6154761904761905 key: train_recall value: [0.77837838 0.77297297 0.74731183 0.71505376 0.75268817 0.77956989 0.74594595 0.76756757 0.72972973 0.75135135] mean value: 0.7540569601859926 key: test_accuracy value: [0.57142857 0.71428571 0.6097561 0.63414634 0.73170732 0.65853659 0.7804878 0.7804878 0.63414634 0.75609756] mean value: 0.6871080139372822 key: train_accuracy value: [0.80810811 0.80540541 0.79245283 0.78436658 0.79245283 0.80862534 0.77628032 0.80053908 0.79784367 0.78975741] mean value: 0.7955831572812705 key: test_roc_auc value: [0.57142857 0.71428571 0.60714286 0.63095238 0.73095238 0.65595238 0.77738095 0.78214286 0.63571429 0.75952381] mean value: 0.6865476190476191 key: train_roc_auc value: [0.80810811 0.80540541 0.79257483 0.78455391 0.7925603 0.80870387 0.77619878 0.80045045 0.79766056 0.78965417] mean value: 0.7955870386515548 key: test_jcc value: [0.4 0.47826087 0.38461538 0.4 0.56 0.44 0.67857143 0.625 0.44444444 0.56521739] mean value: 0.4976109518500823 key: train_jcc value: [0.66976744 0.66511628 0.64351852 0.62441315 0.64516129 0.6712963 0.62443439 0.65740741 0.64285714 0.640553 ] mean value: 0.6484524906404061 key: TN value: 156 mean value: 156.0 key: FP value: 79 mean value: 79.0 key: FN value: 50 mean value: 50.0 key: TP value: 127 mean value: 127.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.31 Accuracy on Blind test: 0.65 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value:/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( [0.01748967 0.01689219 0.0167439 0.01735997 0.0168469 0.01769972 0.01706409 0.01706934 0.01685452 0.01693988] mean value: 0.01709601879119873 key: score_time value: [0.0109098 0.01085544 0.01085448 0.01093626 0.01070666 0.01160288 0.01079202 0.01068521 0.01070261 0.01083326] mean value: 0.010887861251831055 key: test_mcc value: [0.62187434 0.76980036 0.51966679 0.65952381 0.60952381 0.7098505 0.8047619 0.71121921 0.7633652 0.78072006] mean value: 0.6950305975867748 key: train_mcc value: [0.77930111 0.78419633 0.81230044 0.77400792 0.80592851 0.76820692 0.77427749 0.76318023 0.78148627 0.78453492] mean value: 0.7827420129069609 key: test_fscore value: [0.81818182 0.87179487 0.72222222 0.82926829 0.8 0.84210526 0.9047619 0.85 0.88888889 0.86486486] mean value: 0.8392088126555393 key: train_fscore value: [0.88642659 0.89010989 0.90358127 0.8852459 0.90322581 0.88409704 0.8839779 0.87912088 0.88450704 0.89071038] mean value: 0.8891002697697242 key: test_precision value: [0.7826087 0.94444444 0.8125 0.80952381 0.8 0.88888889 0.9047619 0.89473684 0.83333333 1. ] mean value: 0.8670797918709818 key: train_precision value: [0.90909091 0.90502793 0.92655367 0.9 0.90322581 0.88648649 0.9039548 0.89385475 0.92352941 0.90055249] mean value: 0.9052276256122077 key: test_recall value: [0.85714286 0.80952381 0.65 0.85 0.8 0.8 0.9047619 0.80952381 0.95238095 0.76190476] mean value: 0.8195238095238097 key: train_recall value: [0.86486486 0.87567568 0.88172043 0.87096774 0.90322581 0.88172043 0.86486486 0.86486486 0.84864865 0.88108108] mean value: 0.8737634408602151 key: test_accuracy value: [0.80952381 0.88095238 0.75609756 0.82926829 0.80487805 0.85365854 0.90243902 0.85365854 0.87804878 0.87804878] mean value: 0.8446573751451801 key: train_accuracy value: [0.88918919 0.89189189 0.90566038 0.88679245 0.90296496 0.88409704 0.88679245 0.88140162 0.88948787 0.89218329] mean value: 0.8910461134989438 key: test_roc_auc value: [0.80952381 0.88095238 0.75357143 0.8297619 0.8047619 0.85238095 0.90238095 0.8547619 0.87619048 0.88095238] mean value: 0.8445238095238097 key: train_roc_auc value: [0.88918919 0.89189189 0.90572508 0.88683522 0.90296425 0.88410346 0.88673351 0.88135716 0.88937809 0.89215344] mean value: 0.8910331299040977 key: test_jcc value: [0.69230769 0.77272727 0.56521739 0.70833333 0.66666667 0.72727273 0.82608696 0.73913043 0.8 0.76190476] mean value: 0.725964723682115 key: train_jcc value: [0.7960199 0.8019802 0.8241206 0.79411765 0.82352941 0.79227053 0.79207921 0.78431373 0.79292929 0.80295567] mean value: 0.8004316183121798 key: TN value: 179 mean value: 179.0 key: FP value: 37 mean value: 37.0 key: FN value: 27 mean value: 27.0 key: TP value: 169 mean value: 169.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.57 Accuracy on Blind test: 0.81 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.4426558 1.25770402 1.43716693 1.26745296 1.44111037 1.38606858 1.27516031 1.38661623 1.26520538 1.44973159] mean value: 1.3608872175216675 key: score_time value: [0.0147295 0.01381016 0.0137701 0.01382208 0.01384902 0.01415205 0.01397204 0.01392603 0.01390982 0.01387882] mean value: 0.013981962203979492 key: test_mcc value: [0.80952381 0.90889326 0.90649828 0.8547619 0.80907152 0.8047619 0.90692382 0.90692382 0.90649828 1. ] mean value: 0.8813856601490313 key: train_mcc value: [1. 1. 1. 0.9946235 1. 0.9946235 1. 0.99462366 1. 1. ] mean value: 0.9983870656085617 key: test_fscore value: [0.9047619 0.95 0.94736842 0.92682927 0.9047619 0.9 0.95 0.95 0.95454545 1. ] mean value: 0.9388266953414579 key: train_fscore value: [1. 1. 1. 0.99731903 1. 0.99731903 1. 0.99730458 1. 1. ] mean value: 0.9991942651915335 key: test_precision value: [0.9047619 1. 1. 0.9047619 0.86363636 0.9 1. 1. 0.91304348 1. ] mean value: 0.9486203651421043 key: train_precision value: [1. 1. 1. 0.99465241 1. 0.99465241 1. 0.99462366 1. 1. ] mean value: 0.9983928468748203 key: test_recall value: [0.9047619 0.9047619 0.9 0.95 0.95 0.9 0.9047619 0.9047619 1. 1. ] mean value: 0.931904761904762 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9047619 0.95238095 0.95121951 0.92682927 0.90243902 0.90243902 0.95121951 0.95121951 0.95121951 1. ] mean value: 0.9393728222996515 key: train_accuracy value: [1. 1. 1. 0.99730458 1. 0.99730458 1. 0.99730458 1. 1. ] mean value: 0.9991913746630727 key: test_roc_auc value: [0.9047619 0.95238095 0.95 0.92738095 0.90357143 0.90238095 0.95238095 0.95238095 0.95 1. ] mean value: 0.9395238095238094 key: train_roc_auc value: [1. 1. 1. 0.9972973 1. 0.9972973 1. 0.99731183 1. 1. ] mean value: 0.9991906422551583 key: test_jcc value: [0.82608696 0.9047619 0.9 0.86363636 0.82608696 0.81818182 0.9047619 0.9047619 0.91304348 1. ] mean value: 0.8861321287408244 key: train_jcc value: [1. 1. 1. 0.99465241 1. 0.99465241 1. 0.99462366 1. 1. ] mean value: 0.9983928468748203 key: TN value: 195 mean value: 195.0 key: FP value: 14 mean value: 14.0 key: FN value: 11 mean value: 11.0 key: TP value: 192 mean value: 192.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.8 Accuracy on Blind test: 0.91 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02328062 0.01698422 0.01714063 0.01603866 0.01591897 0.01552367 0.01692295 0.0148356 0.01414895 0.01521873] mean value: 0.016601300239562987 key: score_time value: [0.01345897 0.00929999 0.00878119 0.00894523 0.00869012 0.00870967 0.00864792 0.00867152 0.00922465 0.00865269] mean value: 0.009308195114135743 key: test_mcc value: [0.95346259 0.90889326 1. 1. 0.95238095 0.90238095 0.90692382 0.90692382 0.95227002 0.90692382] mean value: 0.9390159240291451 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97560976 0.95 1. 1. 0.97560976 0.95 0.95 0.95 0.97674419 0.95 ] mean value: 0.9677963698241634 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 1. 0.95238095 0.95 1. 1. 0.95454545 1. ] mean value: 0.9856926406926407 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95238095 0.9047619 1. 1. 1. 0.95 0.9047619 0.9047619 1. 0.9047619 ] mean value: 0.9521428571428572 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97619048 0.95238095 1. 1. 0.97560976 0.95121951 0.95121951 0.95121951 0.97560976 0.95121951] mean value: 0.9684668989547036 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97619048 0.95238095 1. 1. 0.97619048 0.95119048 0.95238095 0.95238095 0.975 0.95238095] mean value: 0.9688095238095238 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95238095 0.9047619 1. 1. 0.95238095 0.9047619 0.9047619 0.9047619 0.95454545 0.9047619 ] mean value: 0.9383116883116884 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 203 mean value: 203.0 key: FP value: 10 mean value: 10.0 key: FN value: 3 mean value: 3.0 key: TP value: 196 mean value: 196.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.9 Accuracy on Blind test: 0.95 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.10815501 0.10895181 0.1093998 0.10929322 0.10891581 0.10842085 0.10859251 0.10859179 0.1095922 0.10897279] mean value: 0.10888857841491699 key: score_time value: [0.01763892 0.01770115 0.01765752 0.01774645 0.01755905 0.01760721 0.01772714 0.01766253 0.0177381 0.01773667] mean value: 0.01767747402191162 key: test_mcc value: [0.81322028 0.90889326 0.7565654 1. 0.7098505 0.8047619 0.90238095 0.8547619 0.80817439 0.8213423 ] mean value: 0.8379950902937058 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.90909091 0.95 0.87179487 1. 0.84210526 0.9 0.95238095 0.92682927 0.90909091 0.89473684] mean value: 0.9156029015913483 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.86956522 1. 0.89473684 1. 0.88888889 0.9 0.95238095 0.95 0.86956522 1. ] mean value: 0.9325137118157713 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95238095 0.9047619 0.85 1. 0.8 0.9 0.95238095 0.9047619 0.95238095 0.80952381] mean value: 0.9026190476190477 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9047619 0.95238095 0.87804878 1. 0.85365854 0.90243902 0.95121951 0.92682927 0.90243902 0.90243902] mean value: 0.9174216027874564 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9047619 0.95238095 0.87738095 1. 0.85238095 0.90238095 0.95119048 0.92738095 0.90119048 0.9047619 ] mean value: 0.9173809523809524 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.83333333 0.9047619 0.77272727 1. 0.72727273 0.81818182 0.90909091 0.86363636 0.83333333 0.80952381] mean value: 0.8471861471861472 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 192 mean value: 192.0 key: FP value: 20 mean value: 20.0 key: FN value: 14 mean value: 14.0 key: TP value: 186 mean value: 186.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.54 Accuracy on Blind test: 0.8 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00962472 0.00962687 0.00960588 0.00952983 0.00948834 0.00954628 0.00967455 0.0095439 0.00956202 0.00961089] mean value: 0.009581327438354492 key: score_time /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( value: [0.00869465 0.00862598 0.008672 0.00865126 0.00859308 0.00861502 0.00867414 0.00862432 0.00860453 0.00868845] mean value: 0.008644342422485352 key: test_mcc value: [0.82462113 0.68640647 0.44466675 0.80907152 0.80817439 0.37309549 0.66668392 0.6133669 0.71121921 0.72229808] mean value: 0.6659603856937134 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.89473684 0.81081081 0.625 0.9047619 0.89473684 0.62857143 0.82051282 0.8 0.85 0.84210526] mean value: 0.8071235912025386 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.9375 0.83333333 0.86363636 0.94444444 0.73333333 0.88888889 0.84210526 0.89473684 0.94117647] mean value: 0.8879154939487757 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.80952381 0.71428571 0.5 0.95 0.85 0.55 0.76190476 0.76190476 0.80952381 0.76190476] mean value: 0.7469047619047618 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9047619 0.83333333 0.70731707 0.90243902 0.90243902 0.68292683 0.82926829 0.80487805 0.85365854 0.85365854] mean value: 0.8274680603948896 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9047619 0.83333333 0.70238095 0.90357143 0.90119048 0.6797619 0.83095238 0.80595238 0.8547619 0.85595238] mean value: 0.8272619047619048 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.80952381 0.68181818 0.45454545 0.82608696 0.80952381 0.45833333 0.69565217 0.66666667 0.73913043 0.72727273] mean value: 0.6868553547901375 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 187 mean value: 187.0 key: FP value: 52 mean value: 52.0 key: FN value: 19 mean value: 19.0 key: TP value: 154 mean value: 154.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.41 Accuracy on Blind test: 0.73 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.52321553 1.51518893 1.51075912 1.50979471 1.50419736 1.51972842 1.55826187 1.5144372 1.5101018 1.51376414] mean value: 1.51794490814209 key: score_time value: [0.09260964 0.09168124 0.09199357 0.09185886 0.09356117 0.09131193 0.09143853 0.09412074 0.09189868 0.09186673] mean value: 0.09223411083221436 key: test_mcc value: [0.81322028 1. 0.8047619 1. 0.80907152 0.8547619 0.95238095 0.95227002 0.90649828 1. ] mean value: 0.9092964857581508 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.90909091 1. 0.9 1. 0.9047619 0.92682927 0.97560976 0.97674419 0.95454545 1. ] mean value: 0.9547581478835024 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.86956522 1. 0.9 1. 0.86363636 0.9047619 1. 0.95454545 0.91304348 1. ] mean value: 0.9405552418595896 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95238095 1. 0.9 1. 0.95 0.95 0.95238095 1. 1. 1. ] mean value: 0.9704761904761904 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9047619 1. 0.90243902 1. 0.90243902 0.92682927 0.97560976 0.97560976 0.95121951 1. ] mean value: 0.9538908246225318 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9047619 1. 0.90238095 1. 0.90357143 0.92738095 0.97619048 0.975 0.95 1. ] mean value: 0.9539285714285712 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.83333333 1. 0.81818182 1. 0.82608696 0.86363636 0.95238095 0.95454545 0.91304348 1. ] mean value: 0.9161208356860531 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 193 mean value: 193.0 key: FP value: 6 mean value: 6.0 key: FN value: 13 mean value: 13.0 key: TP value: 200 mean value: 200.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.85 Accuracy on Blind test: 0.94 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.89944077 0.96595263 0.91089082 0.95778012 0.93107462 1.00435877 0.92148137 0.93732476 0.98079419 0.89986396] mean value: 0.9408962011337281 key: score_time value: [0.22168422 0.22878671 0.21265125 0.23230767 0.20985246 0.24188519 0.21738148 0.22345281 0.21452069 0.20086122] mean value: 0.22033836841583251 key: test_mcc value: [0.76980036 0.95346259 0.65952381 0.95238095 0.76500781 0.8047619 0.95238095 0.95227002 0.86240942 0.95227002] mean value: 0.8624267820275844 key: train_mcc value: [0.98391316 0.98391316 0.97339739 0.97866283 0.98395537 0.98927544 0.98927606 0.98395676 0.98395676 0.978494 ] mean value: 0.9828800926027311 key: test_fscore value: [0.88888889 0.97560976 0.82926829 0.97560976 0.88372093 0.9 0.97560976 0.97674419 0.93333333 0.97674419] mean value: 0.9315529085523414 key: train_fscore value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.9919571 0.9919571 0.9867374 0.9893617 0.992 0.99465241 0.99462366 0.9919571 0.9919571 0.98924731] mean value: 0.9914450895047775 key: test_precision value: [0.83333333 1. 0.80952381 0.95238095 0.82608696 0.9 1. 0.95454545 0.875 0.95454545] mean value: 0.9105415960850743 key: train_precision value: [0.98404255 0.98404255 0.97382199 0.97894737 0.98412698 0.9893617 0.98930481 0.98404255 0.98404255 0.98395722] mean value: 0.983569028905601 key: test_recall value: [0.95238095 0.95238095 0.85 1. 0.95 0.9 0.95238095 1. 1. 1. ] mean value: 0.9557142857142857 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.99459459] mean value: 0.9994594594594595 key: test_accuracy value: [0.88095238 0.97619048 0.82926829 0.97560976 0.87804878 0.90243902 0.97560976 0.97560976 0.92682927 0.97560976] mean value: 0.9296167247386758 key: train_accuracy value: [0.99189189 0.99189189 0.98652291 0.98921833 0.99191375 0.99460916 0.99460916 0.99191375 0.99191375 0.98921833] mean value: 0.991370292125009 key: test_roc_auc value: [0.88095238 0.97619048 0.8297619 0.97619048 0.8797619 0.90238095 0.97619048 0.975 0.925 0.975 ] mean value: 0.9296428571428571 key: train_roc_auc value: [0.99189189 0.99189189 0.98648649 0.98918919 0.99189189 0.99459459 0.99462366 0.99193548 0.99193548 0.98923278] mean value: 0.9913673350770125 key: test_jcc value: [0.8 0.95238095 0.70833333 0.95238095 0.79166667 0.81818182 0.95238095 0.95454545 0.875 0.95454545] mean value: 0.8759415584415585 key: train_jcc value: [0.98404255 0.98404255 0.97382199 0.97894737 0.98412698 0.9893617 0.98930481 0.98404255 0.98404255 0.9787234 ] mean value: 0.9830456474059993 key: TN value: 186 mean value: 186.0 key: FP value: 9 mean value: 9.0 key: FN value: 20 mean value: 20.0 key: TP value: 197 mean value: 197.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.77 Accuracy on Blind test: 0.9 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.07526803 0.05560231 0.05945444 0.09954143 0.0577004 0.06429505 0.05793118 0.05943251 0.05828786 0.0576067 ] mean value: 0.06451199054718018 key: score_time value: [0.01105881 0.01142812 0.01062226 0.01171064 0.01137996 0.01111913 0.01055002 0.01063538 0.01095271 0.01062679] mean value: 0.011008381843566895 key: test_mcc value: [0.95346259 0.95346259 0.95227002 1. 0.95238095 1. 0.95238095 0.95238095 0.95227002 1. ] mean value: 0.9668608066001669 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97560976 0.97560976 0.97435897 1. 0.97560976 1. 0.97560976 0.97560976 0.97674419 1. ] mean value: 0.9829151940893291 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 1. 0.95238095 1. 1. 1. 0.95454545 1. ] mean value: 0.9906926406926407 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95238095 0.95238095 0.95 1. 1. 1. 0.95238095 0.95238095 1. 1. ] mean value: 0.9759523809523809 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97619048 0.97619048 0.97560976 1. 0.97560976 1. 0.97560976 0.97560976 0.97560976 1. ] mean value: 0.9830429732868758 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97619048 0.97619048 0.975 1. 0.97619048 1. 0.97619048 0.97619048 0.975 1. ] mean value: 0.983095238095238 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95238095 0.95238095 0.95 1. 0.95238095 1. 0.95238095 0.95238095 0.95454545 1. ] mean value: 0.9666450216450215 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 204 mean value: 204.0 key: FP value: 5 mean value: 5.0 key: FN value: 2 mean value: 2.0 key: TP value: 201 mean value: 201.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.9 Accuracy on Blind test: 0.95 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03443933 0.05179811 0.06322622 0.05100846 0.06835747 0.07097554 0.07047963 0.03397155 0.03488135 0.03616333] mean value: 0.051530098915100096 key: score_time value: [0.02279735 0.0122478 0.01230407 0.02061558 0.02173162 0.0191772 0.01230192 0.01241112 0.01224446 0.01825356] mean value: 0.016408467292785646 key: test_mcc value: [0.85811633 0.8660254 0.86240942 1. 0.90692382 0.80817439 0.80907152 0.86333169 0.8547619 0.8547619 ] mean value: 0.8683576386302903 key: train_mcc value: [0.97837838 0.98379816 0.98384144 0.98384144 0.98384144 0.98384144 0.9784365 0.9784365 0.98921825 0.9784365 ] mean value: 0.9822070037330393 key: test_fscore value: [0.92682927 0.92307692 0.91891892 1. 0.95238095 0.89473684 0.9 0.92307692 0.92682927 0.92682927] mean value: 0.9292678364437027 key: train_fscore value: [0.98918919 0.99191375 0.9919571 0.9919571 0.9919571 0.9919571 0.98918919 0.98918919 0.99459459 0.98918919] mean value: 0.9911093516212641 key: test_precision value: [0.95 1. 1. 1. 0.90909091 0.94444444 0.94736842 1. 0.95 0.95 ] mean value: 0.9650903774587984 key: train_precision value: [0.98918919 0.98924731 0.98930481 0.98930481 0.98930481 0.98930481 0.98918919 0.98918919 0.99459459 0.98918919] mean value: 0.9897817914516207 key: test_recall value: [0.9047619 0.85714286 0.85 1. 1. 0.85 0.85714286 0.85714286 0.9047619 0.9047619 ] mean value: 0.8985714285714286 key: train_recall value: [0.98918919 0.99459459 0.99462366 0.99462366 0.99462366 0.99462366 0.98918919 0.98918919 0.99459459 0.98918919] mean value: 0.992444056960186 key: test_accuracy value: [0.92857143 0.92857143 0.92682927 1. 0.95121951 0.90243902 0.90243902 0.92682927 0.92682927 0.92682927] mean value: 0.9320557491289201 key: train_accuracy value: [0.98918919 0.99189189 0.99191375 0.99191375 0.99191375 0.99191375 0.98921833 0.98921833 0.99460916 0.98921833] mean value: 0.9911000218547388 key: test_roc_auc value: [0.92857143 0.92857143 0.925 1. 0.95238095 0.90119048 0.90357143 0.92857143 0.92738095 0.92738095] mean value: 0.9322619047619047 key: train_roc_auc value: [0.98918919 0.99189189 0.99190642 0.99190642 0.99190642 0.99190642 0.98921825 0.98921825 0.99460913 0.98921825] mean value: 0.9910970648067421 key: test_jcc value: [0.86363636 0.85714286 0.85 1. 0.90909091 0.80952381 0.81818182 0.85714286 0.86363636 0.86363636] mean value: 0.8691991341991342 key: train_jcc value: [0.97860963 0.98395722 0.98404255 0.98404255 0.98404255 0.98404255 0.97860963 0.97860963 0.98924731 0.97860963] mean value: 0.9823813246519049 key: TN value: 199 mean value: 199.0 key: FP value: 21 mean value: 21.0 key: FN value: 7 mean value: 7.0 key: TP value: 185 mean value: 185.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.68 Accuracy on Blind test: 0.86 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.0135808 0.01309872 0.00985193 0.00955606 0.00936365 0.01058984 0.00950861 0.00956726 0.00948119 0.00925517] mean value: 0.01038532257080078 key: score_time value: [0.01231837 0.0104692 0.00897026 0.00873733 0.00892115 0.00917673 0.00902939 0.00907087 0.00861287 0.00889134] mean value: 0.009419751167297364 key: test_mcc value: [0.28603878 0.43052839 0.08051176 0.41428571 0.17142857 0.31666667 0.37171226 0.47003614 0.41487884 0.46300848] mean value: 0.3419095594012099 key: train_mcc value: [0.44330153 0.36757294 0.3857278 0.39622203 0.41262884 0.41778553 0.37478201 0.40162743 0.4503185 0.37478201] mean value: 0.40247486105604446 key: test_fscore value: [0.63414634 0.72727273 0.57777778 0.7 0.58536585 0.65 0.66666667 0.71794872 0.72727273 0.74418605] mean value: 0.6730636858572197 key: train_fscore value: [0.72386059 0.68463612 0.7 0.69892473 0.71240106 0.70967742 0.68983957 0.70080863 0.71978022 0.68983957] mean value: 0.7029767903859494 key: test_precision value: [0.65 0.69565217 0.52 0.7 0.57142857 0.65 0.72222222 0.77777778 0.69565217 0.72727273] mean value: 0.6710005646527386 key: train_precision value: [0.71808511 0.6827957 0.68556701 0.69892473 0.69948187 0.70967742 0.68253968 0.69892473 0.73184358 0.68253968] mean value: 0.6990379503120753 key: test_recall value: [0.61904762 0.76190476 0.65 0.7 0.6 0.65 0.61904762 0.66666667 0.76190476 0.76190476] mean value: 0.679047619047619 key: train_recall value: [0.72972973 0.68648649 0.71505376 0.69892473 0.72580645 0.70967742 0.6972973 0.7027027 0.70810811 0.6972973 ] mean value: 0.7071083987213018 key: test_accuracy value: [0.64285714 0.71428571 0.53658537 0.70731707 0.58536585 0.65853659 0.68292683 0.73170732 0.70731707 0.73170732] mean value: 0.6698606271777002 key: train_accuracy value: [0.72162162 0.68378378 0.69272237 0.69811321 0.70619946 0.70889488 0.68733154 0.70080863 0.72506739 0.68733154] mean value: 0.7011874408100823 key: test_roc_auc value: [0.64285714 0.71428571 0.53928571 0.70714286 0.58571429 0.65833333 0.68452381 0.73333333 0.70595238 0.73095238] mean value: 0.6702380952380953 key: train_roc_auc value: [0.72162162 0.68378378 0.69266202 0.69811101 0.70614647 0.70889276 0.68735833 0.70081372 0.7250218 0.68735833] mean value: 0.701176983435048 key: test_jcc value: [0.46428571 0.57142857 0.40625 0.53846154 0.4137931 0.48148148 0.5 0.56 0.57142857 0.59259259] mean value: 0.5099721573126745 key: train_jcc value: [0.56722689 0.5204918 0.53846154 0.53719008 0.55327869 0.55 0.52653061 0.53941909 0.56223176 0.52653061] mean value: 0.5421361074949125 key: TN value: 136 mean value: 136.0 key: FP value: 66 mean value: 66.0 key: FN value: 70 mean value: 70.0 key: TP value: 140 mean value: 140.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.33 Accuracy on Blind test: 0.68 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01755261 0.0244925 0.02322912 0.02689171 0.02338552 0.02113056 0.02441788 0.02576876 0.02323055 0.02049685] mean value: 0.023059606552124023 key: score_time value: [0.00870085 0.01136637 0.01221657 0.01218843 0.01224065 0.01185799 0.01182604 0.0121398 0.01182389 0.01190352] mean value: 0.011626410484313964 key: test_mcc value: [0.81322028 1. 0.90649828 1. 0.95238095 0.8547619 0.90692382 1. 0.8547619 1. ] mean value: 0.9288547149722348 key: train_mcc value: [0.95765257 0.98379816 0.9784365 0.97866283 0.9946235 0.97306016 0.98384144 0.97866529 0.9681586 0.98384191] mean value: 0.9780740950182457 key: test_fscore value: [0.90909091 1. 0.94736842 1. 0.97560976 0.92682927 0.95 1. 0.92682927 1. ] mean value: 0.9635727622826469 key: train_fscore value: [0.97883598 0.99191375 0.98924731 0.9893617 0.99731903 0.98652291 0.99186992 0.98930481 0.98351648 0.99191375] mean value: 0.9889805647006705 key: test_precision value: [0.86956522 1. 1. 1. 0.95238095 0.9047619 1. 1. 0.95 1. ] mean value: 0.9676708074534162 key: train_precision value: [0.95854922 0.98924731 0.98924731 0.97894737 0.99465241 0.98918919 0.99456522 0.97883598 1. 0.98924731] mean value: 0.9862481318536436 key: test_recall value: [0.95238095 1. 0.9 1. 1. 0.95 0.9047619 1. 0.9047619 1. ] mean value: 0.961190476190476 key: train_recall value: [1. 0.99459459 0.98924731 1. 1. 0.98387097 0.98918919 1. 0.96756757 0.99459459] mean value: 0.9919064225515839 key: test_accuracy value: [0.9047619 1. 0.95121951 1. 0.97560976 0.92682927 0.95121951 1. 0.92682927 1. ] mean value: 0.9636469221835077 key: train_accuracy value: [0.97837838 0.99189189 0.98921833 0.98921833 0.99730458 0.98652291 0.99191375 0.98921833 0.98382749 0.99191375] mean value: 0.9889407736577548 key: test_roc_auc value: [0.9047619 1. 0.95 1. 0.97619048 0.92738095 0.95238095 1. 0.92738095 1. ] mean value: 0.9638095238095238 key: train_roc_auc value: [0.97837838 0.99189189 0.98921825 0.98918919 0.9972973 0.98653008 0.99190642 0.98924731 0.98378378 0.99192095] mean value: 0.9889363557105494 key: test_jcc value: [0.83333333 1. 0.9 1. 0.95238095 0.86363636 0.9047619 1. 0.86363636 1. ] mean value: 0.9317748917748917 key: train_jcc value: [0.95854922 0.98395722 0.9787234 0.97894737 0.99465241 0.97340426 0.98387097 0.97883598 0.96756757 0.98395722] mean value: 0.9782465609858717 key: TN value: 199 mean value: 199.0 key: FP value: 8 mean value: 8.0 key: FN value: 7 mean value: 7.0 key: TP value: 198 mean value: 198.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.82 Accuracy on Blind test: 0.92 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01616383 0.01703405 0.01742053 0.01710224 0.01646566 0.01644945 0.0160799 0.01625156 0.01585746 0.01778984] mean value: 0.016661453247070312 key: score_time value: [0.01153302 0.01175213 0.0117445 0.01178885 0.0118649 0.0118494 0.01189089 0.01182055 0.01171756 0.01178432] mean value: 0.011774611473083497 key: test_mcc value: [0.8660254 0.8660254 0.85441771 0.8547619 0.7633652 0.70272837 0.698212 0.86240942 0.85441771 1. ] mean value: 0.8322363111639266 key: train_mcc value: [0.88528142 0.92567765 0.9681586 0.98927544 0.91221469 0.73643388 0.73681663 0.79883884 0.95160448 0.93156865] mean value: 0.8835870293159503 key: test_fscore value: [0.92307692 0.92307692 0.92307692 0.92682927 0.86486486 0.85106383 0.85714286 0.93333333 0.93023256 1. ] mean value: 0.9132697480791275 key: train_fscore value: [0.9375 0.96111111 0.98412698 0.99465241 0.95211268 0.87119438 0.87058824 0.90024331 0.97547684 0.96587927] mean value: 0.9412885205728119 key: test_precision value: [1. 1. 0.94736842 0.9047619 0.94117647 0.74074074 0.75 0.875 0.90909091 1. ] mean value: 0.9068138446234422 key: train_precision value: [0.98802395 0.98857143 0.96875 0.9893617 1. 0.77178423 0.77083333 0.81858407 0.98351648 0.93877551] mean value: 0.92182007130104 key: test_recall value: [0.85714286 0.85714286 0.9 0.95 0.8 1. 1. 1. 0.95238095 1. ] mean value: 0.9316666666666666 key: train_recall value: [0.89189189 0.93513514 1. 1. 0.90860215 1. 1. 1. 0.96756757 0.99459459] mean value: 0.9697791339726823 key: test_accuracy value: [0.92857143 0.92857143 0.92682927 0.92682927 0.87804878 0.82926829 0.82926829 0.92682927 0.92682927 1. ] mean value: 0.9101045296167248 key: train_accuracy value: [0.94054054 0.96216216 0.98382749 0.99460916 0.9541779 0.85175202 0.85175202 0.88948787 0.97574124 0.96495957] mean value: 0.9369009980330736 key: test_roc_auc value: [0.92857143 0.92857143 0.92619048 0.92738095 0.87619048 0.83333333 0.825 0.925 0.92619048 1. ] mean value: 0.9096428571428572 key: train_roc_auc value: [0.94054054 0.96216216 0.98378378 0.99459459 0.95430108 0.85135135 0.85215054 0.88978495 0.97571927 0.96503923] mean value: 0.9369427492008138 key: test_jcc value: [0.85714286 0.85714286 0.85714286 0.86363636 0.76190476 0.74074074 0.75 0.875 0.86956522 1. ] mean value: 0.8432275655101742 key: train_jcc value: [0.88235294 0.92513369 0.96875 0.9893617 0.90860215 0.77178423 0.77083333 0.81858407 0.95212766 0.93401015] mean value: 0.8921539932035006 key: TN value: 183 mean value: 183.0 key: FP value: 14 mean value: 14.0 key: FN value: 23 mean value: 23.0 key: TP value: 192 mean value: 192.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.81 Accuracy on Blind test: 0.92 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.16330028 0.14592481 0.14636064 0.14555335 0.1465106 0.14619303 0.14668226 0.14667153 0.14667702 0.14632678] mean value: 0.14802002906799316 key: score_time value: [0.01503825 0.01503277 0.01506281 0.01544476 0.01512909 0.01512289 0.01505589 0.01523805 0.01561737 0.01514649] mean value: 0.015188837051391601 key: test_mcc value: [0.95346259 0.95346259 1. 0.95238095 0.90692382 1. 0.95238095 1. 0.95227002 1. ] mean value: 0.9670880922421489 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97560976 0.97560976 1. 0.97560976 0.95238095 1. 0.97560976 1. 0.97674419 1. ] mean value: 0.9831564162817708 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 0.95238095 0.90909091 1. 1. 1. 0.95454545 1. ] mean value: 0.9816017316017316 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95238095 0.95238095 1. 1. 1. 1. 0.95238095 1. 1. 1. ] mean value: 0.9857142857142858 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97619048 0.97619048 1. 0.97560976 0.95121951 1. 0.97560976 1. 0.97560976 1. ] mean value: 0.9830429732868758 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97619048 0.97619048 1. 0.97619048 0.95238095 1. 0.97619048 1. 0.975 1. ] mean value: 0.9832142857142857 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95238095 0.95238095 1. 0.95238095 0.90909091 1. 0.95238095 1. 0.95454545 1. ] mean value: 0.9673160173160174 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 202 mean value: 202.0 key: FP value: 3 mean value: 3.0 key: FN value: 4 mean value: 4.0 key: TP value: 203 mean value: 203.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.93 Accuracy on Blind test: 0.97 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.0419879 0.05815363 0.05153394 0.05133629 0.06185651 0.04853463 0.04930449 0.05603623 0.04445434 0.05075979] mean value: 0.05139577388763428 key: score_time value: [0.02328753 0.02999187 0.02305889 0.02622795 0.02008605 0.01859808 0.0239203 0.03557754 0.02351022 0.02400208] mean value: 0.0248260498046875 key: test_mcc value: [0.8660254 0.95346259 0.85441771 1. 0.8547619 0.95227002 0.95238095 0.90692382 0.90649828 0.90692382] mean value: 0.9153664500518636 key: train_mcc value: [0.989247 1. 0.99462366 0.99462366 0.99462366 0.98921825 1. 1. 0.99462366 0.97866283] mean value: 0.9935622705535619 key: test_fscore value: [0.92307692 0.97560976 0.92307692 1. 0.92682927 0.97435897 0.97560976 0.95 0.95454545 0.95 ] mean value: 0.9553107055546078 key: train_fscore value: [0.99456522 1. 0.99730458 0.99730458 0.99730458 0.99462366 1. 1. 0.99730458 0.98907104] mean value: 0.9967478240397618 key: test_precision value: [1. 1. 0.94736842 1. 0.9047619 1. 1. 1. 0.91304348 1. ] mean value: 0.9765173804075407 key: train_precision value: [1. 1. 1. 1. 1. 0.99462366 1. 1. 0.99462366 1. ] mean value: 0.9989247311827956 key: test_recall value: [0.85714286 0.95238095 0.9 1. 0.95 0.95 0.95238095 0.9047619 1. 0.9047619 ] mean value: 0.9371428571428572 key: train_recall value: [0.98918919 1. 0.99462366 0.99462366 0.99462366 0.99462366 1. 1. 1. 0.97837838] mean value: 0.9946062191223481 key: test_accuracy value: [0.92857143 0.97619048 0.92682927 1. 0.92682927 0.97560976 0.97560976 0.95121951 0.95121951 0.95121951] mean value: 0.9563298490127758 key: train_accuracy value: [0.99459459 1. 0.99730458 0.99730458 0.99730458 0.99460916 1. 1. 0.99730458 0.98921833] mean value: 0.9967640416697019 key: test_roc_auc value: [0.92857143 0.97619048 0.92619048 1. 0.92738095 0.975 0.97619048 0.95238095 0.95 0.95238095] mean value: 0.9564285714285713 key: train_roc_auc value: [0.99459459 1. 0.99731183 0.99731183 0.99731183 0.99460913 1. 1. 0.99731183 0.98918919] mean value: 0.9967640220866028 key: test_jcc value: [0.85714286 0.95238095 0.85714286 1. 0.86363636 0.95 0.95238095 0.9047619 0.91304348 0.9047619 ] mean value: 0.9155251270468663 key: train_jcc value: [0.98918919 1. 0.99462366 0.99462366 0.99462366 0.98930481 1. 1. 0.99462366 0.97837838] mean value: 0.9935367004057707 key: TN value: 201 mean value: 201.0 key: FP value: 13 mean value: 13.0 key: FN value: 5 mean value: 5.0 key: TP value: 193 mean value: 193.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.9 Accuracy on Blind test: 0.95 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.09415078 0.11422873 0.14302111 0.11281705 0.11488152 0.11356688 0.11255336 0.11312151 0.11276913 0.12180877] mean value: 0.1152918815612793 key: score_time value: [0.02256703 0.0243187 0.02233911 0.02280259 0.02246737 0.02313232 0.028368 0.02239347 0.02279854 0.02269793] mean value: 0.02338850498199463 key: test_mcc value: [0.62187434 0.63059263 0.48849265 0.61152662 0.56527676 0.66432098 0.65871309 0.66668392 0.4373371 0.74124932] mean value: 0.6086067388065854 key: train_mcc value: [0.90460775 0.92000825 0.92539732 0.90381625 0.93618785 0.91980011 0.90379406 0.92538015 0.90994228 0.9361732 ] mean value: 0.9185107229970159 key: test_fscore value: [0.8 0.78947368 0.66666667 0.78947368 0.75675676 0.81081081 0.8372093 0.82051282 0.66666667 0.83333333] mean value: 0.7770903725493689 key: train_fscore value: [0.94972067 0.95844875 0.96153846 0.95054945 0.96703297 0.95890411 0.95027624 0.96132597 0.95264624 0.96685083] mean value: 0.9577293690791937 key: test_precision value: [0.84210526 0.88235294 0.84615385 0.83333333 0.82352941 0.88235294 0.81818182 0.88888889 0.8 1. ] mean value: 0.8616898443833427 key: train_precision value: [0.98265896 0.98295455 0.98314607 0.97191011 0.98876404 0.97765363 0.97175141 0.98305085 0.98275862 0.98870056] mean value: 0.9813348806544546 key: test_recall value: [0.76190476 0.71428571 0.55 0.75 0.7 0.75 0.85714286 0.76190476 0.57142857 0.71428571] mean value: 0.713095238095238 key: train_recall value: [0.91891892 0.93513514 0.94086022 0.93010753 0.94623656 0.94086022 0.92972973 0.94054054 0.92432432 0.94594595] mean value: 0.9352659110723627 key: test_accuracy value: [0.80952381 0.80952381 0.73170732 0.80487805 0.7804878 0.82926829 0.82926829 0.82926829 0.70731707 0.85365854] mean value: 0.7984901277584204 key: train_accuracy value: [0.95135135 0.95945946 0.96226415 0.95148248 0.96765499 0.95956873 0.95148248 0.96226415 0.9541779 0.96765499] mean value: 0.9587360676039921 key: test_roc_auc value: [0.80952381 0.80952381 0.72738095 0.80357143 0.77857143 0.82738095 0.82857143 0.83095238 0.71071429 0.85714286] mean value: 0.7983333333333335 key: train_roc_auc value: [0.95135135 0.95945946 0.962322 0.95154025 0.96771287 0.9596193 0.951424 0.96220575 0.95409765 0.96759663] mean value: 0.958732926474862 key: test_jcc value: [0.66666667 0.65217391 0.5 0.65217391 0.60869565 0.68181818 0.72 0.69565217 0.5 0.71428571] mean value: 0.6391466214944476 key: train_jcc value: [0.90425532 0.92021277 0.92592593 0.90575916 0.93617021 0.92105263 0.90526316 0.92553191 0.90957447 0.93582888] mean value: 0.9189574435559686 key: TN value: 182 mean value: 182.0 key: FP value: 59 mean value: 59.0 key: FN value: 24 mean value: 24.0 key: TP value: 147 mean value: 147.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.42 Accuracy on Blind test: 0.74 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.5280242 0.51709318 0.52253103 0.52089429 0.53239608 0.52510905 0.51605487 0.51583099 0.51282382 0.51904821] mean value: 0.5209805727005005 key: score_time value: [0.00917411 0.00927401 0.00938559 0.00967669 0.00932646 0.00964832 0.00938344 0.0091908 0.00919986 0.00924921] mean value: 0.009350848197937012 key: test_mcc value: [1. 0.95346259 1. 1. 0.95238095 1. 0.95238095 0.95238095 0.95227002 1. ] mean value: 0.9762875461572262 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.97560976 1. 1. 0.97560976 1. 0.97560976 0.97560976 0.97674419 1. ] mean value: 0.9879183210436755 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 1. 0.95238095 1. 1. 1. 0.95454545 1. ] mean value: 0.9906926406926407 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.95238095 1. 1. 1. 1. 0.95238095 0.95238095 1. 1. ] mean value: 0.9857142857142858 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.97619048 1. 1. 0.97560976 1. 0.97560976 0.97560976 0.97560976 1. ] mean value: 0.987862950058072 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.97619048 1. 1. 0.97619048 1. 0.97619048 0.97619048 0.975 1. ] mean value: 0.9879761904761905 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.95238095 1. 1. 0.95238095 1. 0.95238095 0.95238095 0.95454545 1. ] mean value: 0.9764069264069264 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 204 mean value: 204.0 key: FP value: 3 mean value: 3.0 key: FN value: 2 mean value: 2.0 key: TP value: 203 mean value: 203.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.91 Accuracy on Blind test: 0.96 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02632236 0.02660441 0.03398514 0.02744603 0.02748156 0.02773428 0.02761316 0.02803922 0.02759814 0.02734256] mean value: 0.02801668643951416 key: score_time value: [0.01254487 0.01330376 0.01410699 0.01402807 0.01527119 0.01543665 0.01530933 0.01407933 0.01403427 0.01432133] mean value: 0.014243578910827637 key: test_mcc value: [0.81322028 0.76980036 0.86333169 0.74124932 0.74124932 0.6133669 0.7565654 0.65871309 0.70714286 0.80907152] mean value: 0.7473710740902314 key: train_mcc value: [0.93710863 1. 0.97866283 0.99462366 1. 0.96787795 0.94746155 0.99462366 0.98927544 0.98395537] mean value: 0.9793589089718562 key: test_fscore value: [0.90909091 0.88888889 0.93023256 0.86956522 0.86956522 0.80952381 0.88372093 0.8372093 0.85714286 0.9 ] mean value: 0.8754939690126747 key: train_fscore value: [0.96648045 1. 0.9893617 0.99730458 1. 0.98404255 0.97222222 0.99730458 0.99456522 0.99182561] mean value: 0.9893106919359556 key: test_precision value: [0.86956522 0.83333333 0.86956522 0.76923077 0.76923077 0.77272727 0.86363636 0.81818182 0.85714286 0.94736842] mean value: 0.8369982039318424 key: train_precision value: [1. 1. 0.97894737 1. 1. 0.97368421 1. 0.99462366 1. 1. ] mean value: 0.9947255234861346 key: test_recall value: [0.95238095 0.95238095 1. 1. 1. 0.85 0.9047619 0.85714286 0.85714286 0.85714286] mean value: 0.9230952380952381 key: train_recall value: [0.93513514 1. 1. 0.99462366 1. 0.99462366 0.94594595 1. 0.98918919 0.98378378] mean value: 0.9843301365882011 key: test_accuracy value: [0.9047619 0.88095238 0.92682927 0.85365854 0.85365854 0.80487805 0.87804878 0.82926829 0.85365854 0.90243902] mean value: 0.868815331010453 key: train_accuracy value: [0.96756757 1. 0.98921833 0.99730458 1. 0.98382749 0.97304582 0.99730458 0.99460916 0.99191375] mean value: 0.9894791287244116 key: test_roc_auc value: [0.9047619 0.88095238 0.92857143 0.85714286 0.85714286 0.80595238 0.87738095 0.82857143 0.85357143 0.90357143] mean value: 0.8697619047619047 key: train_roc_auc value: [0.96756757 1. 0.98918919 0.99731183 1. 0.98379831 0.97297297 0.99731183 0.99459459 0.99189189] mean value: 0.9894638186573671 key: test_jcc value: [0.83333333 0.8 0.86956522 0.76923077 0.76923077 0.68 0.79166667 0.72 0.75 0.81818182] mean value: 0.780120857403466 key: train_jcc value: [0.93513514 1. 0.97894737 0.99462366 1. 0.96858639 0.94594595 0.99462366 0.98918919 0.98378378] mean value: 0.9790835121737619 key: TN value: 169 mean value: 169.0 key: FP value: 17 mean value: 17.0 key: FN value: 37 mean value: 37.0 key: TP value: 189 mean value: 189.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.25 Accuracy on Blind test: 0.7 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.01563764 0.01484275 0.01484203 0.01481032 0.02531528 0.03736687 0.03732777 0.03908205 0.03567171 0.05811954] mean value: 0.02930159568786621 key: score_time value: [0.01234174 0.01208162 0.01213408 0.01210332 0.02343178 0.02073622 0.02129221 0.0238409 0.02119112 0.02207351] mean value: 0.018122649192810057 key: test_mcc value: [0.9047619 0.8660254 0.85441771 0.95227002 0.90692382 0.95238095 0.90692382 1. 0.90649828 1. ] mean value: 0.9250201911273045 key: train_mcc value: [0.96756757 0.97298719 0.956873 0.96765475 0.95692987 0.96771194 0.9784365 0.97305937 0.97305937 0.96238811] mean value: 0.9676667677298388 key: test_fscore value: [0.95238095 0.92307692 0.92307692 0.97435897 0.95238095 0.97560976 0.95 1. 0.95454545 1. ] mean value: 0.9605429935917741 key: train_fscore value: [0.98378378 0.98644986 0.97849462 0.98387097 0.97837838 0.98378378 0.98918919 0.98644986 0.98644986 0.98092643] mean value: 0.9837776750546631 key: test_precision value: [0.95238095 1. 0.94736842 1. 0.90909091 0.95238095 1. 1. 0.91304348 1. ] mean value: 0.9674264713166314 key: train_precision value: [0.98378378 0.98913043 0.97849462 0.98387097 0.98369565 0.98913043 0.98918919 0.98913043 0.98913043 0.98901099] mean value: 0.9864566944686158 key: test_recall value: [0.95238095 0.85714286 0.9 0.95 1. 1. 0.9047619 1. 1. 1. ] mean value: 0.9564285714285713 key: train_recall value: [0.98378378 0.98378378 0.97849462 0.98387097 0.97311828 0.97849462 0.98918919 0.98378378 0.98378378 0.97297297] mean value: 0.9811275791920954 key: test_accuracy value: [0.95238095 0.92857143 0.92682927 0.97560976 0.95121951 0.97560976 0.95121951 1. 0.95121951 1. ] mean value: 0.9612659698025551 key: train_accuracy value: [0.98378378 0.98648649 0.97843666 0.98382749 0.97843666 0.98382749 0.98921833 0.98652291 0.98652291 0.98113208] mean value: 0.9838194798572157 key: test_roc_auc value: [0.95238095 0.92857143 0.92619048 0.975 0.95238095 0.97619048 0.95238095 1. 0.95 1. ] mean value: 0.9613095238095237 key: train_roc_auc value: [0.98378378 0.98648649 0.9784365 0.98382738 0.97845103 0.98384191 0.98921825 0.98651555 0.98651555 0.98111014] mean value: 0.9838186573670447 key: test_jcc value: [0.90909091 0.85714286 0.85714286 0.95 0.90909091 0.95238095 0.9047619 1. 0.91304348 1. ] mean value: 0.9252653867871258 key: train_jcc value: [0.96808511 0.97326203 0.95789474 0.96825397 0.95767196 0.96808511 0.97860963 0.97326203 0.97326203 0.96256684] mean value: 0.9680953442378909 key: TN value: 199 mean value: 199.0 key: FP value: 9 mean value: 9.0 key: FN value: 7 mean value: 7.0 key: TP value: 197 mean value: 197.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:432: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:433: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_CV['Resampling'] = rs_rouC /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:438: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:439: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_BT['Resampling'] = rs_rouC blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.9 Accuracy on Blind test: 0.95 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.32478094 0.20739317 0.20109296 0.32834768 0.2527113 0.1824615 0.26740456 0.40533066 0.39677286 0.23047733] mean value: 0.2796772956848145 key: score_time value: [0.01243234 0.01315236 0.02611136 0.01238418 0.01246881 0.01612306 0.02184033 0.02457547 0.01257014 0.01241994] mean value: 0.01640779972076416 key: test_mcc value: [0.9047619 0.8660254 0.85441771 0.95227002 0.90692382 0.95238095 0.90692382 1. 0.90649828 1. ] mean value: 0.9250201911273045 key: train_mcc value: [0.96756757 0.97298719 0.956873 0.96765475 0.95692987 0.96771194 0.9784365 0.97305937 0.97305937 0.96238811] mean value: 0.9676667677298388 key: test_fscore value: [0.95238095 0.92307692 0.92307692 0.97435897 0.95238095 0.97560976 0.95 1. 0.95454545 1. ] mean value: 0.9605429935917741 key: train_fscore value: [0.98378378 0.98644986 0.97849462 0.98387097 0.97837838 0.98378378 0.98918919 0.98644986 0.98644986 0.98092643] mean value: 0.9837776750546631 key: test_precision value: [0.95238095 1. 0.94736842 1. 0.90909091 0.95238095 1. 1. 0.91304348 1. ] mean value: 0.9674264713166314 key: train_precision value: [0.98378378 0.98913043 0.97849462 0.98387097 0.98369565 0.98913043 0.98918919 0.98913043 0.98913043 0.98901099] mean value: 0.9864566944686158 key: test_recall value: [0.95238095 0.85714286 0.9 0.95 1. 1. 0.9047619 1. 1. 1. ] mean value: 0.9564285714285713 key: train_recall value: [0.98378378 0.98378378 0.97849462 0.98387097 0.97311828 0.97849462 0.98918919 0.98378378 0.98378378 0.97297297] mean value: 0.9811275791920954 key: test_accuracy value: [0.95238095 0.92857143 0.92682927 0.97560976 0.95121951 0.97560976 0.95121951 1. 0.95121951 1. ] mean value: 0.9612659698025551 key: train_accuracy value: [0.98378378 0.98648649 0.97843666 0.98382749 0.97843666 0.98382749 0.98921833 0.98652291 0.98652291 0.98113208] mean value: 0.9838194798572157 key: test_roc_auc value: [0.95238095 0.92857143 0.92619048 0.975 0.95238095 0.97619048 0.95238095 1. 0.95 1. ] mean value: 0.9613095238095237 key: train_roc_auc value: [0.98378378 0.98648649 0.9784365 0.98382738 0.97845103 0.98384191 0.98921825 0.98651555 0.98651555 0.98111014] mean value: 0.9838186573670447 key: test_jcc value: [0.90909091 0.85714286 0.85714286 0.95 0.90909091 0.95238095 0.9047619 1. 0.91304348 1. ] mean value: 0.9252653867871258 key: train_jcc value: [0.96808511 0.97326203 0.95789474 0.96825397 0.95767196 0.96808511 0.97860963 0.97326203 0.97326203 0.96256684] mean value: 0.9680953442378909 key: TN value: 199 mean value: 199.0 key: FP value: 9 mean value: 9.0 key: FN value: 7 mean value: 7.0 key: TP value: 197 mean value: 197.0 key: trainingY_neg value: 206 mean value: 206.0 key: trainingY_pos value: 206 mean value: 206.0 key: blindY_neg value: 52 mean value: 52.0 key: blindY_pos value: 103 mean value: 103.0 MCC on Blind test: 0.9 Accuracy on Blind test: 0.95 PASS: sorting df by score that is mapped onto the order I want Concatenating dfs with different resampling methods [WF]: 70/30 No. of dfs combining: 10 The sampling methods are: Resampling Logistic Regression none Logistic Regression smnc Logistic Regression ros Logistic Regression rus Logistic Regression rouC PASS: 10 dfs successfully combined nrows in combined_df_wf: 240 ncols in combined_df_wf: 9 Concatenating dfs with different resampling methods: 70/30 No. of dfs combining: 5 The sampling methods are: Resampling training_size 0 none 312 24 smnc 412 48 ros 412 72 rus 212 96 rouC 412 PASS: 5 dfs successfully combined nrows in combined_df: 120 ncols in combined_df: 32 File successfully written: /home/tanu/git/Data/isoniazid/output/ml/tts_7030/katg_baselineC_7030.csv File successfully written: /home/tanu/git/Data/isoniazid/output/ml/tts_7030/katg_baselineC_ext_7030.csv