/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_7030.py:464: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( 1.22.4 1.4.1 aaindex_df contains non-numerical data Total no. of non-numerial columns: 2 Selecting numerical data only PASS: successfully selected numerical columns only for aaindex_df Now checking for NA in the remaining aaindex_cols Counting aaindex_df cols with NA ncols with NA: 4 columns Dropping these... Original ncols: 127 Revised df ncols: 123 Checking NA in revised df... PASS: cols with NA successfully dropped from aaindex_df Proceeding with combining aa_df with other features_df PASS: ncols match Expected ncols: 123 Got: 123 Total no. of columns in clean aa_df: 123 Proceeding to merge, expected nrows in merged_df: 858 PASS: my_features_df and aa_df successfully combined nrows: 858 ncols: 269 count of NULL values before imputation or_mychisq 244 log10_or_mychisq 244 dtype: int64 count of NULL values AFTER imputation mutationinformation 0 or_rawI 0 logorI 0 dtype: int64 PASS: OR values imputed, data ready for ML Total no. of features for aaindex: 123 PASS: x_features has no target variable No. of columns for x_features: 174 PASS: ML data with input features, training and test generated... Total no. of input features: 174 --------No. of numerical features: 168 --------No. of categorical features: 6 Total no. of evolutionary features: 3 Total no. of stability features: 28 --------Common stabilty cols: 5 --------Foldx cols: 23 Total no. of affinity features: 5 --------Common affinity cols: 3 --------Gene specific affinity cols: 2 Total no. of residue level features: 132 --------AA index cols: 123 --------Residue Prop cols: 3 --------AA change Prop cols: 6 Total no. of genomic features: 6 --------MAF+OR cols: 2 --------Lineage cols: 4 --------Other cols: 0 ------------------------------------------------------------- Successfully split data: ALL features actual values: training set imputed values: blind test set Total data size: 448 Train data size: (300, 174) y_train numbers: Counter({0: 236, 1: 64}) Test data size: (148, 174) y_test_numbers: Counter({0: 117, 1: 31}) y_train ratio: 3.6875 y_test ratio: 3.774193548387097 ------------------------------------------------------------- index: 0 ind: 1 Mask count check: True index: 1 ind: 2 Mask count check: False Original Data Counter({0: 236, 1: 64}) Data dim: (300, 174) Simple Random OverSampling Counter({1: 236, 0: 236}) (472, 174) Simple Random UnderSampling Counter({0: 64, 1: 64}) (128, 174) Simple Combined Over and UnderSampling Counter({0: 236, 1: 236}) (472, 174) SMOTE_NC OverSampling Counter({1: 236, 0: 236}) (472, 174) ##################################################################### Running ML analysis: feature groups Gene name: embB Drug name: ethambutol Output directory: /home/tanu/git/Data/ethambutol/output/ml/tts_7030/ ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( key: fit_time value: [0.02937937 0.03311324 0.03480864 0.0340519 0.03387308 0.03259921 0.03355289 0.03285098 0.03236556 0.05050564] mean value: 0.034710049629211426 key: score_time value: [0.01199722 0.01186538 0.01197195 0.01283383 0.01298165 0.01298761 0.01297903 0.01430368 0.01283002 0.01292968] mean value: 0.012768006324768067 key: test_mcc value: [0.60421798 0.47913962 0.34151451 0.16850509 0.78446454 0.53452248 0.53452248 0.67082039 0.2941742 0.38888889] mean value: 0.48007701907786793 key: train_mcc value: [0.7168396 0.69115394 0.75506049 0.74208347 0.7211399 0.73357553 0.7084467 0.70860165 0.73425497 0.68294833] mean value: 0.7194104566119666 key: test_fscore value: [0.6 0.54545455 0.4 0.22222222 0.8 0.5 0.5 0.72727273 0.4 0.44444444] mean value: 0.513939393939394 key: train_fscore value: [0.75 0.72340426 0.78350515 0.7755102 0.75510204 0.76767677 0.74747475 0.74226804 0.7628866 0.72164948] mean value: 0.7529477293719139 key: test_precision value: [1. 0.75 0.66666667 0.5 1. 1. 1. 0.8 0.5 0.66666667] mean value: 0.7883333333333333 key: train_precision value: [0.92307692 0.91891892 0.95 0.92682927 0.925 0.92682927 0.90243902 0.92307692 0.94871795 0.8974359 ] mean value: 0.9242324172202222 key: test_recall value: [0.42857143 0.42857143 0.28571429 0.14285714 0.66666667 0.33333333 0.33333333 0.66666667 0.33333333 0.33333333] mean value: 0.3952380952380953 key: train_recall value: [0.63157895 0.59649123 0.66666667 0.66666667 0.63793103 0.65517241 0.63793103 0.62068966 0.63793103 0.60344828] mean value: 0.6354506957047793 key: test_accuracy value: [0.86666667 0.83333333 0.8 0.76666667 0.93333333 0.86666667 0.86666667 0.9 0.8 0.83333333] mean value: 0.8466666666666667 key: train_accuracy value: [0.91111111 0.9037037 0.92222222 0.91851852 0.91111111 0.91481481 0.90740741 0.90740741 0.91481481 0.9 ] mean value: 0.9111111111111112 key: test_roc_auc value: [0.71428571 0.69254658 0.62111801 0.54968944 0.83333333 0.66666667 0.66666667 0.8125 0.625 0.64583333] mean value: 0.6827639751552794 key: train_roc_auc value: [0.80874722 0.79120336 0.8286385 0.82629108 0.81189005 0.82051074 0.80953155 0.80326936 0.81424854 0.79229018] mean value: 0.8106620561525046 key: test_jcc value: [0.42857143 0.375 0.25 0.125 0.66666667 0.33333333 0.33333333 0.57142857 0.25 0.28571429] mean value: 0.36190476190476184 key: train_jcc value: [0.6 0.56666667 0.6440678 0.63333333 0.60655738 0.62295082 0.59677419 0.59016393 0.61666667 0.56451613] mean value: 0.6041696917005023 key: TN value: 229 mean value: 229.0 key: FP value: 39 mean value: 39.0 key: FN value: 7 mean value: 7.0 key: TP value: 25 mean value: 25.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.57 Accuracy on Blind test: 0.87 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.70284939 0.83141327 0.7548821 0.96309876 0.73414922 0.70942235 0.77504945 0.77884579 0.69634461 0.74346662] mean value: 0.7689521551132202 key: score_time value: [0.01313448 0.01442432 0.01456642 0.01466513 0.01308274 0.01448059 0.01453996 0.01436615 0.01447749 0.01468396] mean value: 0.014242124557495118 key: test_mcc value: [0.90632697 0.62732919 0.34151451 0.81064348 0.79166667 0.58333333 0.66666667 0.79166667 0.79166667 0.67082039] mean value: 0.6981634545719541 key: train_mcc value: [1. 1. 1. 0.989012 1. 1. 0.97854339 1. 1. 1. ] mean value: 0.996755538901208 key: test_fscore value: [0.92307692 0.71428571 0.4 0.83333333 0.83333333 0.66666667 0.66666667 0.83333333 0.83333333 0.72727273] mean value: 0.7431302031302032 key: train_fscore value: [1. 1. 1. 0.99130435 1. 1. 0.98305085 1. 1. 1. ] mean value: 0.9974355195283714 key: test_precision value: [1. 0.71428571 0.66666667 1. 0.83333333 0.66666667 1. 0.83333333 0.83333333 0.8 ] mean value: 0.8347619047619048 key: train_precision value: [1. 1. 1. 0.98275862 1. 1. 0.96666667 1. 1. 1. ] mean value: 0.9949425287356322 key: test_recall value: [0.85714286 0.71428571 0.28571429 0.71428571 0.83333333 0.66666667 0.5 0.83333333 0.83333333 0.66666667] mean value: 0.6904761904761905 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.96666667 0.86666667 0.8 0.93333333 0.93333333 0.86666667 0.9 0.93333333 0.93333333 0.9 ] mean value: 0.9033333333333335 key: train_accuracy value: [1. 1. 1. 0.9962963 1. 1. 0.99259259 1. 1. 1. ] mean value: 0.9988888888888889 key: test_roc_auc value: [0.92857143 0.8136646 0.62111801 0.85714286 0.89583333 0.79166667 0.75 0.89583333 0.89583333 0.8125 ] mean value: 0.8262163561076605 key: train_roc_auc value: [1. 1. 1. 0.99765258 1. 1. 0.99528302 1. 1. 1. ] mean value: 0.9992935601027548 key: test_jcc value: [0.85714286 0.55555556 0.25 0.71428571 0.71428571 0.5 0.5 0.71428571 0.71428571 0.57142857] mean value: 0.6091269841269841 key: train_jcc value: [1. 1. 1. 0.98275862 1. 1. 0.96666667 1. 1. 1. ] mean value: 0.9949425287356322 key: TN value: 227 mean value: 227.0 key: FP value: 19 mean value: 19.0 key: FN value: 9 mean value: 9.0 key: TP value: 45 mean value: 45.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.85 Accuracy on Blind test: 0.95 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01280761 0.0125072 0.00945711 0.01048613 0.00934339 0.00971079 0.00998425 0.00970817 0.00902152 0.00909138] mean value: 0.010211753845214843 key: score_time value: [0.01178479 0.01134276 0.00900006 0.00981688 0.00934839 0.00933266 0.00949502 0.00865149 0.008708 0.00873661] mean value: 0.009621667861938476 key: test_mcc value: [ 0.8136646 0.3539192 0.44099379 -0.15478181 0.16666667 0.31524416 0.2236068 0.1767767 0.1767767 0.07537784] mean value: 0.25882446262811765 key: train_mcc value: [0.56619289 0.58730807 0.53804925 0.34313622 0.5896613 0.62833238 0.60422007 0.59087082 0.56908335 0.54107098] mean value: 0.5557925334413916 key: test_fscore value: [0.85714286 0.52631579 0.57142857 0.28571429 0.33333333 0.46153846 0.36363636 0.375 0.375 0.28571429] mean value: 0.4434823947981842 key: train_fscore value: [0.6618705 0.67605634 0.62135922 0.47413793 0.68085106 0.71014493 0.68965517 0.68055556 0.66666667 0.6375 ] mean value: 0.649879738196278 key: test_precision value: [0.85714286 0.41666667 0.57142857 0.19047619 0.33333333 0.42857143 0.4 0.3 0.3 0.25 ] mean value: 0.40476190476190477 key: train_precision value: [0.56097561 0.56470588 0.69565217 0.31428571 0.57831325 0.6125 0.57471264 0.56976744 0.575 0.5 ] mean value: 0.5545912718858471 key: test_recall value: [0.85714286 0.71428571 0.57142857 0.57142857 0.33333333 0.5 0.33333333 0.5 0.5 0.33333333] mean value: 0.5214285714285715 key: train_recall value: [0.80701754 0.84210526 0.56140351 0.96491228 0.82758621 0.84482759 0.86206897 0.84482759 0.79310345 0.87931034] mean value: 0.8227162734422262 key: test_accuracy value: [0.93333333 0.7 0.8 0.33333333 0.73333333 0.76666667 0.76666667 0.66666667 0.66666667 0.66666667] mean value: 0.7033333333333334 key: train_accuracy value: [0.82592593 0.82962963 0.85555556 0.54814815 0.83333333 0.85185185 0.83333333 0.82962963 0.82962963 0.78518519] mean value: 0.8022222222222222 key: test_roc_auc value: [0.9068323 0.70496894 0.72049689 0.41614907 0.58333333 0.66666667 0.60416667 0.60416667 0.60416667 0.54166667] mean value: 0.6352613871635612 key: train_roc_auc value: [0.81900173 0.83419817 0.7478379 0.700766 0.83124593 0.84930059 0.84377033 0.83514964 0.81636304 0.81937215] mean value: 0.8097005496894892 key: test_jcc value: [0.75 0.35714286 0.4 0.16666667 0.2 0.3 0.22222222 0.23076923 0.23076923 0.16666667] mean value: 0.3024236874236874 key: train_jcc value: [0.49462366 0.5106383 0.45070423 0.31073446 0.51612903 0.5505618 0.52631579 0.51578947 0.5 0.46788991] mean value: 0.48433866438409173 key: TN value: 177 mean value: 177.0 key: FP value: 30 mean value: 30.0 key: FN value: 59 mean value: 59.0 key: TP value: 34 mean value: 34.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.3 Accuracy on Blind test: 0.71 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00921559 0.01021528 0.01006198 0.00915194 0.01011467 0.01024914 0.01006627 0.01004815 0.00904727 0.01015615] mean value: 0.009832644462585449 key: score_time value: [0.00920868 0.00943947 0.00940347 0.00901794 0.00946045 0.00941467 0.00939584 0.00885057 0.00862551 0.00935102] mean value: 0.009216761589050293 key: test_mcc value: [ 0. 0.17622684 -0.14744196 0.07881104 0.38888889 0.16666667 -0.13363062 -0.09284767 0. -0.13363062] mean value: 0.030304257323617856 key: train_mcc value: [0.26184314 0.26021557 0.28952646 0.24250501 0.25544197 0.33070969 0.26577992 0.32080672 0.27236552 0.22804921] mean value: 0.2727243221543163 key: test_fscore value: [0. 0.33333333 0. 0.2 0.44444444 0.33333333 0. 0. 0.18181818 0. ] mean value: 0.14929292929292928 key: train_fscore value: [0.3373494 0.34883721 0.37209302 0.36170213 0.34482759 0.38554217 0.34883721 0.40449438 0.38297872 0.2962963 ] mean value: 0.358295812371502 key: test_precision value: [0. 0.4 0. 0.33333333 0.66666667 0.33333333 0. 0. 0.2 0. ] mean value: 0.19333333333333333 key: train_precision value: [0.53846154 0.51724138 0.55172414 0.45945946 0.51724138 0.64 0.53571429 0.58064516 0.5 0.52173913] mean value: 0.5362226471912113 key: test_recall value: [0. 0.28571429 0. 0.14285714 0.33333333 0.33333333 0. 0. 0.16666667 0. ] mean value: 0.1261904761904762 key: train_recall value: [0.24561404 0.26315789 0.28070175 0.29824561 0.25862069 0.27586207 0.25862069 0.31034483 0.31034483 0.20689655] mean value: 0.2708408953418028 key: test_accuracy value: [0.76666667 0.73333333 0.7 0.73333333 0.83333333 0.73333333 0.73333333 0.76666667 0.7 0.73333333] mean value: 0.7433333333333334 key: train_accuracy value: [0.7962963 0.79259259 0.8 0.77777778 0.78888889 0.81111111 0.79259259 0.8037037 0.78518519 0.78888889] mean value: 0.7937037037037038 key: test_roc_auc value: [0.5 0.57763975 0.45652174 0.52795031 0.64583333 0.58333333 0.45833333 0.47916667 0.5 0.45833333] mean value: 0.5187111801242236 key: train_roc_auc value: [0.594638 0.5987151 0.60983445 0.60217445 0.59629148 0.61670462 0.59864997 0.62451204 0.61271958 0.57750488] mean value: 0.6031744559975911 key: test_jcc value: [0. 0.2 0. 0.11111111 0.28571429 0.2 0. 0. 0.1 0. ] mean value: 0.08968253968253968 key: train_jcc value: [0.20289855 0.21126761 0.22857143 0.22077922 0.20833333 0.23880597 0.21126761 0.25352113 0.23684211 0.17391304] mean value: 0.21861999903274615 key: TN value: 215 mean value: 215.0 key: FP value: 56 mean value: 56.0 key: FN value: 21 mean value: 21.0 key: TP value: 8 mean value: 8.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) MCC on Blind test: -0.03 Accuracy on Blind test: 0.7 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00965452 0.00992346 0.00971246 0.00962901 0.00954032 0.00851202 0.00849891 0.00883889 0.00885677 0.00982833] mean value: 0.009299468994140626 key: score_time value: [0.05858684 0.01619411 0.01401067 0.01489687 0.01341724 0.01285982 0.01148534 0.01255536 0.01079416 0.01213264] mean value: 0.017693305015563966 key: test_mcc value: [ 0. -0.14744196 0. -0.1024439 -0.09284767 -0.13363062 0. 0.37139068 -0.13363062 -0.16666667] mean value: -0.040527075735360685 key: train_mcc value: [0.32926729 0.25784885 0.29298224 0.29298224 0.28886635 0.32507645 0.25067149 0.36857928 0.34558167 0.3531848 ] mean value: 0.3105040658430679 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0.28571429 0. 0. ] mean value: 0.02857142857142857 key: train_fscore value: [0.30985915 0.21212121 0.30136986 0.30136986 0.2972973 0.30555556 0.27027027 0.38461538 0.32876712 0.37974684] mean value: 0.30909725595474036 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.] mean value: 0.1 key: train_precision value: [0.78571429 0.77777778 0.6875 0.6875 0.6875 0.78571429 0.625 0.75 0.8 0.71428571] mean value: 0.7300992063492063 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0.16666667 0. 0. ] mean value: 0.016666666666666666 key: train_recall value: [0.19298246 0.12280702 0.19298246 0.19298246 0.18965517 0.18965517 0.17241379 0.25862069 0.20689655 0.25862069] mean value: 0.19776164549304293 key: test_accuracy value: [0.76666667 0.7 0.76666667 0.73333333 0.76666667 0.73333333 0.8 0.83333333 0.73333333 0.7 ] mean value: 0.7533333333333333 key: train_accuracy value: [0.81851852 0.80740741 0.81111111 0.81111111 0.80740741 0.81481481 0.8 0.82222222 0.81851852 0.81851852] mean value: 0.812962962962963 key: test_roc_auc value: [0.5 0.45652174 0.5 0.47826087 0.47916667 0.45833333 0.5 0.58333333 0.45833333 0.4375 ] mean value: 0.48514492753623195 key: train_roc_auc value: [0.58944897 0.55670867 0.58475414 0.58475414 0.58303513 0.58775211 0.57205595 0.61751789 0.5963728 0.6151594 ] mean value: 0.5887559224010523 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0.16666667 0. 0. ] mean value: 0.016666666666666666 key: train_jcc value: [0.18333333 0.11864407 0.17741935 0.17741935 0.17460317 0.18032787 0.15625 0.23809524 0.19672131 0.234375 ] mean value: 0.18371887038336446 key: TN value: 225 mean value: 225.0 key: FP value: 63 mean value: 63.0 key: FN value: 11 mean value: 11.0 key: TP value: 1 mean value: 1.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.21 Accuracy on Blind test: 0.79 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.01497412 0.0135026 0.0124414 0.01308274 0.01273632 0.01267242 0.01280069 0.01290011 0.01269555 0.01251149] mean value: 0.013031744956970214 key: score_time value: [0.01012492 0.0097959 0.00969124 0.0095942 0.00979686 0.00987744 0.00988817 0.00966811 0.00955296 0.0096035 ] mean value: 0.009759330749511718 key: test_mcc value: [ 0. 0. 0. 0. 0. 0. 0. 0. -0.09284767 0. ] mean value: -0.009284766908852594 key: train_mcc value: [0.16699366 0.20490727 0.20490727 0.26553052 0.20265575 0.20265575 0.20265575 0.20265575 0.23444615 0.26261287] mean value: 0.21500207340515193 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0.06779661 0.1 0.1 0.16129032 0.09836066 0.09836066 0.09836066 0.09836066 0.12903226 0.15873016] mean value: 0.11102919724956313 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0.03508772 0.05263158 0.05263158 0.0877193 0.05172414 0.05172414 0.05172414 0.05172414 0.06896552 0.0862069 ] mean value: 0.059013914095583785 key: test_accuracy value: [0.76666667 0.76666667 0.76666667 0.76666667 0.8 0.8 0.8 0.8 0.76666667 0.8 ] mean value: 0.7833333333333334 key: train_accuracy value: [0.7962963 0.8 0.8 0.80740741 0.7962963 0.7962963 0.7962963 0.7962963 0.8 0.8037037 ] mean value: 0.7992592592592593 key: test_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.47916667 0.5 ] mean value: 0.4979166666666667 key: train_roc_auc value: [0.51754386 0.52631579 0.52631579 0.54385965 0.52586207 0.52586207 0.52586207 0.52586207 0.53448276 0.54310345] mean value: 0.5295069570477919 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0.03508772 0.05263158 0.05263158 0.0877193 0.05172414 0.05172414 0.05172414 0.05172414 0.06896552 0.0862069 ] mean value: 0.059013914095583785 key: TN value: 235 mean value: 235.0 key: FP value: 64 mean value: 64.0 key: FN value: 1 mean value: 1.0 key: TP value: 0 mean value: 0.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.79 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.28935218 1.11154175 1.17469454 0.87591124 1.31645083 1.11882138 1.28768206 1.17197323 1.22777867 1.14895582] mean value: 1.1723161697387696 key: score_time value: [0.01478457 0.01461887 0.01219559 0.01212907 0.0145092 0.01444316 0.01373696 0.01357079 0.01454949 0.01462388] mean value: 0.01391615867614746 key: test_mcc value: [0.48445214 0.44099379 0.51227176 0.34151451 0.66666667 0.20044593 0.53452248 0.4472136 0.51227176 0.53931937] mean value: 0.4679672017008586 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 0.989012 1. ] mean value: 0.9989011997122217 key: test_fscore value: [0.44444444 0.57142857 0.61538462 0.4 0.66666667 0.25 0.5 0.54545455 0.61538462 0.6 ] mean value: 0.5208763458763458 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 0.99130435 1. ] mean value: 0.9991304347826088 key: test_precision value: [1. 0.57142857 0.66666667 0.66666667 1. 0.5 1. 0.6 0.57142857 0.75 ] mean value: 0.7326190476190476 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.28571429 0.57142857 0.57142857 0.28571429 0.5 0.16666667 0.33333333 0.5 0.66666667 0.5 ] mean value: 0.43809523809523815 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 0.98275862 1. ] mean value: 0.9982758620689655 key: test_accuracy value: [0.83333333 0.8 0.83333333 0.8 0.9 0.8 0.86666667 0.83333333 0.83333333 0.86666667] mean value: 0.8366666666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 0.9962963 1. ] mean value: 0.9996296296296296 key: test_roc_auc value: [0.64285714 0.72049689 0.74223602 0.62111801 0.75 0.5625 0.66666667 0.70833333 0.77083333 0.72916667] mean value: 0.6914208074534162 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 0.99137931 1. ] mean value: 0.9991379310344828 key: test_jcc value: [0.28571429 0.4 0.44444444 0.25 0.5 0.14285714 0.33333333 0.375 0.44444444 0.42857143] mean value: 0.3604365079365079 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 0.98275862 1. ] mean value: 0.9982758620689655 key: TN value: 223 mean value: 223.0 key: FP value: 36 mean value: 36.0 key: FN value: 13 mean value: 13.0 key: TP value: 28 mean value: 28.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.62 Accuracy on Blind test: 0.88 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02025032 0.01830864 0.01398182 0.01478696 0.01364207 0.01290917 0.01366997 0.01493931 0.01325536 0.01472449] mean value: 0.015046811103820801 key: score_time value: [0.01177716 0.00908613 0.00855756 0.00851798 0.00853229 0.00851965 0.00853705 0.0085578 0.00852776 0.00925159] mean value: 0.008986496925354004 key: test_mcc value: [1. 0.84270097 0.81064348 0.90632697 0.89442719 0.70929937 0.66666667 0.8291562 0.79166667 0.79166667] mean value: 0.8242554176357084 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.875 0.83333333 0.92307692 0.90909091 0.76923077 0.66666667 0.85714286 0.83333333 0.83333333] mean value: 0.8500208125208125 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.77777778 1. 1. 1. 0.71428571 1. 0.75 0.83333333 0.83333333] mean value: 0.890873015873016 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.71428571 0.85714286 0.83333333 0.83333333 0.5 1. 0.83333333 0.83333333] mean value: 0.8404761904761905 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.93333333 0.93333333 0.96666667 0.96666667 0.9 0.9 0.93333333 0.93333333 0.93333333] mean value: 0.9400000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.95652174 0.85714286 0.92857143 0.91666667 0.875 0.75 0.95833333 0.89583333 0.89583333] mean value: 0.9033902691511388 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.77777778 0.71428571 0.85714286 0.83333333 0.625 0.5 0.75 0.71428571 0.71428571] mean value: 0.7486111111111111 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 228 mean value: 228.0 key: FP value: 10 mean value: 10.0 key: FN value: 8 mean value: 8.0 key: TP value: 54 mean value: 54.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.77 Accuracy on Blind test: 0.93 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.10482979 0.10370564 0.10213518 0.10077429 0.10230589 0.10169768 0.10185742 0.10176086 0.10234618 0.10370636] mean value: 0.10251193046569824 key: score_time value: [0.01763678 0.01745105 0.01738286 0.01766253 0.01771617 0.01758122 0.01727009 0.01756859 0.01742911 0.01736689] mean value: 0.0175065279006958 key: test_mcc value: [ 0.33660139 0.47913962 0.38769906 0. 0. -0.13363062 -0.09284767 0.37139068 0.11111111 0.11111111] mean value: 0.1570574668631064 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.25 0.54545455 0.5 0. 0. 0. 0. 0.28571429 0.22222222 0.22222222] mean value: 0.20256132756132755 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.75 0.6 0. 0. 0. 0. 1. 0.33333333 0.33333333] mean value: 0.40166666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.14285714 0.42857143 0.42857143 0. 0. 0. 0. 0.16666667 0.16666667 0.16666667] mean value: 0.15000000000000002 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.83333333 0.8 0.76666667 0.8 0.73333333 0.76666667 0.83333333 0.76666667 0.76666667] mean value: 0.7866666666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.57142857 0.69254658 0.67080745 0.5 0.5 0.45833333 0.47916667 0.58333333 0.54166667 0.54166667] mean value: 0.5538949275362319 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.14285714 0.375 0.33333333 0. 0. 0. 0. 0.16666667 0.125 0.125 ] mean value: 0.12678571428571428 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 226 mean value: 226.0 key: FP value: 54 mean value: 54.0 key: FN value: 10 mean value: 10.0 key: TP value: 10 mean value: 10.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.45 Accuracy on Blind test: 0.84 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01043725 0.01003408 0.01018763 0.01028419 0.00942492 0.01007652 0.00999904 0.01002502 0.01008296 0.00922561] mean value: 0.00997772216796875 key: score_time value: [0.00968361 0.00917816 0.00944519 0.00942183 0.00889969 0.00936246 0.00931597 0.0093205 0.00927258 0.00865602] mean value: 0.009255599975585938 key: test_mcc value: [-0.24671758 0.25465839 0.31524416 -0.27583864 -0.11306675 0. 0.35355339 0.51227176 0.31524416 0.4472136 ] mean value: 0.15625624819351644 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.42857143 0.46153846 0. 0.14285714 0.18181818 0.5 0.61538462 0.46153846 0.54545455] mean value: 0.33371628371628376 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0.42857143 0.5 0. 0.125 0.2 0.4 0.57142857 0.42857143 0.6 ] mean value: 0.32535714285714284 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.42857143 0.42857143 0. 0.16666667 0.16666667 0.66666667 0.66666667 0.5 0.5 ] mean value: 0.35238095238095235 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.73333333 0.76666667 0.56666667 0.6 0.7 0.73333333 0.83333333 0.76666667 0.83333333] mean value: 0.7133333333333333 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.39130435 0.62732919 0.64906832 0.36956522 0.4375 0.5 0.70833333 0.77083333 0.66666667 0.70833333] mean value: 0.5828933747412008 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0.27272727 0.3 0. 0.07692308 0.1 0.33333333 0.44444444 0.3 0.375 ] mean value: 0.22024281274281274 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 192 mean value: 192.0 key: FP value: 42 mean value: 42.0 key: FN value: 44 mean value: 44.0 key: TP value: 22 mean value: 22.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.22 Accuracy on Blind test: 0.75 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.42193198 1.48464036 1.5280869 1.39486265 1.38504148 1.38991213 1.4028635 1.4091928 1.39909554 1.406986 ]/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( mean value: 1.4222613334655763 key: score_time value: [0.09638333 0.09634948 0.0968647 0.09420729 0.08841085 0.09504485 0.0951705 0.09646654 0.09620643 0.09695005] mean value: 0.09520540237426758 key: test_mcc value: [0.60421798 0.51227176 0.60421798 0.48445214 0.53452248 0.38888889 0.66666667 0.53931937 0.67082039 0.67082039] mean value: 0.5676198059450792 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.6 0.61538462 0.6 0.44444444 0.5 0.44444444 0.66666667 0.6 0.72727273 0.72727273] mean value: 0.5925485625485626 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.66666667 1. 1. 1. 0.66666667 1. 0.75 0.8 0.8 ] mean value: 0.8683333333333334 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.42857143 0.57142857 0.42857143 0.28571429 0.33333333 0.33333333 0.5 0.5 0.66666667 0.66666667] mean value: 0.4714285714285714 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.86666667 0.83333333 0.86666667 0.83333333 0.86666667 0.83333333 0.9 0.86666667 0.9 0.9 ] mean value: 0.8666666666666668 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.71428571 0.74223602 0.71428571 0.64285714 0.66666667 0.64583333 0.75 0.72916667 0.8125 0.8125 ] mean value: 0.723033126293996 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.42857143 0.44444444 0.42857143 0.28571429 0.33333333 0.28571429 0.5 0.42857143 0.57142857 0.57142857] mean value: 0.42777777777777776 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 230 mean value: 230.0 key: FP value: 34 mean value: 34.0 key: FN value: 6 mean value: 6.0 key: TP value: 30 mean value: 30.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.7 Accuracy on Blind test: 0.91 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.86156774 0.85981154 0.84925723 0.92530513 0.88639402 0.90727282 0.91255879 0.89210582 0.88635182 0.97684216] mean value: 0.8957467079162598 key: score_time value: [0.18339157 0.14685249 0.18079305 0.21409535 0.17683268 0.17423201 0.16365194 0.18895435 0.19963431 0.14843202] mean value: 0.1776869773864746 key: test_mcc value: [0. 0.60421798 0.60421798 0.33660139 0.37139068 0.37139068 0.37139068 0.38888889 0.20044593 0.37139068] mean value: 0.3619934867309984 key: train_mcc value: [0.8760064 0.91023656 0.86450473 0.86352508 0.88822308 0.92214351 0.87770876 0.86638084 0.90023488 0.83209945] mean value: 0.8801063277277368 key: test_fscore value: [0. 0.6 0.6 0.25 0.28571429 0.28571429 0.28571429 0.44444444 0.25 0.28571429] mean value: 0.3287301587301587 key: train_fscore value: [0.89320388 0.9245283 0.88235294 0.88461538 0.90740741 0.93693694 0.8952381 0.88461538 0.91588785 0.85148515] mean value: 0.8976271334353759 key: test_precision value: [0. 1. 1. 1. 1. 1. 1. 0.66666667 0.5 1. ] mean value: 0.8166666666666667 key: train_precision value: [1. 1. 1. 0.9787234 0.98 0.98113208 1. 1. 1. 1. ] mean value: 0.9939855479727017 key: test_recall value: [0. 0.42857143 0.42857143 0.14285714 0.16666667 0.16666667 0.16666667 0.33333333 0.16666667 0.16666667] mean value: 0.21666666666666665 key: train_recall value: [0.80701754 0.85964912 0.78947368 0.80701754 0.84482759 0.89655172 0.81034483 0.79310345 0.84482759 0.74137931] mean value: 0.8194192377495464 key: test_accuracy value: [0.76666667 0.86666667 0.86666667 0.8 0.83333333 0.83333333 0.83333333 0.83333333 0.8 0.83333333] mean value: 0.8266666666666665 key: train_accuracy value: [0.95925926 0.97037037 0.95555556 0.95555556 0.96296296 0.97407407 0.95925926 0.95555556 0.96666667 0.94444444] mean value: 0.9603703703703704 key: test_roc_auc value: [0.5 0.71428571 0.71428571 0.57142857 0.58333333 0.58333333 0.58333333 0.64583333 0.5625 0.58333333] mean value: 0.6041666666666666 key: train_roc_auc value: [0.90350877 0.92982456 0.89473684 0.90116135 0.9200553 0.94591737 0.90517241 0.89655172 0.92241379 0.87068966] mean value: 0.9090031789775281 key: test_jcc value: [0. 0.42857143 0.42857143 0.14285714 0.16666667 0.16666667 0.16666667 0.28571429 0.14285714 0.16666667] mean value: 0.2095238095238095 key: train_jcc value: [0.80701754 0.85964912 0.78947368 0.79310345 0.83050847 0.88135593 0.81034483 0.79310345 0.84482759 0.74137931] mean value: 0.8150763378346509 key: TN value: 234 mean value: 234.0 key: FP value: 50 mean value: 50.0 key: FN value: 2 mean value: 2.0 key: TP value: 14 mean value: 14.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.49 Accuracy on Blind test: 0.85 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.10199094 0.05092549 0.05280399 0.05130363 0.05411649 0.05062795 0.05282617 0.08380771 0.21290278 0.04891729] mean value: 0.07602224349975586 key: score_time value: [0.01059747 0.01039028 0.01032424 0.01030087 0.01046681 0.01035547 0.01033735 0.01081228 0.01085591 0.01062322] mean value: 0.010506391525268555 key: test_mcc value: [1. 0.91485328 1. 1. 1. 0.70929937 0.66666667 0.90632697 0.79166667 1. ] mean value: 0.8988812944927872 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.93333333 1. 1. 1. 0.76923077 0.66666667 0.92307692 0.83333333 1. ] mean value: 0.9125641025641025 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.875 1. 1. 1. 0.71428571 1. 0.85714286 0.83333333 1. ] mean value: 0.9279761904761905 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 0.83333333 0.5 1. 0.83333333 1. ] mean value: 0.9166666666666667 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.96666667 1. 1. 1. 0.9 0.9 0.96666667 0.93333333 1. ] mean value: 0.9666666666666666 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.97826087 1. 1. 1. 0.875 0.75 0.97916667 0.89583333 1. ] mean value: 0.9478260869565218 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.875 1. 1. 1. 0.625 0.5 0.85714286 0.71428571 1. ] mean value: 0.8571428571428573 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 231 mean value: 231.0 key: FP value: 5 mean value: 5.0 key: FN value: 5 mean value: 5.0 key: TP value: 59 mean value: 59.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.85 Accuracy on Blind test: 0.95 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03237653 0.06756616 0.05314088 0.05011415 0.05858469 0.06775379 0.06915903 0.06523108 0.05823874 0.06052685] mean value: 0.058269190788269046 key: score_time value: [0.02251053 0.02317214 0.0225575 0.01193118 0.0209291 0.02144742 0.02248287 0.02148151 0.0118711 0.02320576] mean value: 0.020158910751342775 key: test_mcc value: [0.59917127 0.44099379 0.59917127 0.07881104 0.58333333 0.58333333 0.67082039 0.76376262 0.53033009 0.4472136 ] mean value: 0.5296940727221642 key: train_mcc value: [0.93435553 0.95631739 0.94596128 0.95753763 0.93515969 0.93412492 0.95685154 0.95685154 0.95685154 0.94661124] mean value: 0.9480622310357543 key: test_fscore value: [0.66666667 0.57142857 0.66666667 0.2 0.66666667 0.66666667 0.72727273 0.8 0.625 0.54545455] mean value: 0.6135822510822511 key: train_fscore value: [0.94827586 0.96551724 0.95726496 0.96610169 0.94915254 0.94827586 0.96610169 0.96610169 0.96610169 0.95798319] mean value: 0.9590876438093409 key: test_precision value: [0.8 0.57142857 0.8 0.33333333 0.66666667 0.66666667 0.8 0.66666667 0.5 0.6 ] mean value: 0.6404761904761904 key: train_precision value: [0.93220339 0.94915254 0.93333333 0.93442623 0.93333333 0.94827586 0.95 0.95 0.95 0.93442623] mean value: 0.9415150919955415 key: test_recall value: [0.57142857 0.57142857 0.57142857 0.14285714 0.66666667 0.66666667 0.66666667 1. 0.83333333 0.5 ] mean value: 0.619047619047619 key: train_recall value: [0.96491228 0.98245614 0.98245614 1. 0.96551724 0.94827586 0.98275862 0.98275862 0.98275862 0.98275862] mean value: 0.9774652147610405 key: test_accuracy value: [0.86666667 0.8 0.86666667 0.73333333 0.86666667 0.86666667 0.9 0.9 0.8 0.83333333] mean value: 0.8433333333333334 key: train_accuracy value: [0.97777778 0.98518519 0.98148148 0.98518519 0.97777778 0.97777778 0.98518519 0.98518519 0.98518519 0.98148148] mean value: 0.9822222222222221 key: test_roc_auc value: [0.76397516 0.72049689 0.76397516 0.52795031 0.79166667 0.79166667 0.8125 0.9375 0.8125 0.70833333] mean value: 0.7630564182194617 key: train_roc_auc value: [0.97306647 0.98418582 0.9818384 0.99061033 0.97332466 0.96706246 0.98430384 0.98430384 0.98430384 0.98194535] mean value: 0.9804944994878813 key: test_jcc value: [0.5 0.4 0.5 0.11111111 0.5 0.5 0.57142857 0.66666667 0.45454545 0.375 ] mean value: 0.4578751803751803 key: train_jcc value: [0.90163934 0.93333333 0.91803279 0.93442623 0.90322581 0.90163934 0.93442623 0.93442623 0.93442623 0.91935484] mean value: 0.9214930371937248 key: TN value: 214 mean value: 214.0 key: FP value: 25 mean value: 25.0 key: FN value: 22 mean value: 22.0 key: TP value: 39 mean value: 39.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.62 Accuracy on Blind test: 0.87 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.02837014 0.01165652 0.00915122 0.00917482 0.00916696 0.00895834 0.00898743 0.00897956 0.00888681 0.00905609] mean value: 0.011238789558410645 key: score_time value: [0.01515555 0.00896788 0.00874496 0.00878382 0.00883079 0.00866342 0.00871348 0.0085988 0.00838351 0.00861454] mean value: 0.009345674514770507 key: test_mcc value: [0. 0.16850509 0.07881104 0. 0. 0. 0. 0. 0.20044593 0.37139068] mean value: 0.0819152741160786 key: train_mcc value: [0.25784885 0.18040749 0.39237451 0.29029468 0.28103906 0.25454388 0.1745244 0.16586378 0.25714998 0.25454388] mean value: 0.2508590529267264 key: test_fscore value: [0. 0.22222222 0.2 0. 0. 0. 0. 0. 0.25 0.28571429] mean value: 0.09579365079365079 key: train_fscore value: [0.21212121 0.2 0.34285714 0.33333333 0.25714286 0.20895522 0.15151515 0.125 0.23188406 0.20895522] mean value: 0.22717642027019055 key: test_precision value: [0. 0.5 0.33333333 0. 0. 0. 0. 0. 0.5 1. ] mean value: 0.2333333333333333 key: train_precision value: [0.77777778 0.53846154 0.92307692 0.61904762 0.75 0.77777778 0.625 0.66666667 0.72727273 0.77777778] mean value: 0.7182858807858807 key: test_recall value: [0. 0.14285714 0.14285714 0. 0. 0. 0. 0. 0.16666667 0.16666667] mean value: 0.06190476190476189 key: train_recall value: [0.12280702 0.12280702 0.21052632 0.22807018 0.15517241 0.12068966 0.0862069 0.06896552 0.13793103 0.12068966] mean value: 0.13738656987295822 key: test_accuracy value: [0.76666667 0.76666667 0.73333333 0.76666667 0.8 0.8 0.8 0.8 0.8 0.83333333] mean value: 0.7866666666666666 key: train_accuracy value: [0.80740741 0.79259259 0.82962963 0.80740741 0.80740741 0.8037037 0.79259259 0.79259259 0.8037037 0.8037037 ] mean value: 0.8040740740740742 key: test_roc_auc value: [0.5 0.54968944 0.52795031 0.5 0.5 0.5 0.5 0.5 0.5625 0.58333333] mean value: 0.5223473084886128 key: train_roc_auc value: [0.55670867 0.547319 0.60291574 0.59525574 0.57051074 0.55562785 0.53602798 0.52976578 0.56189005 0.55562785] mean value: 0.561164938758784 key: test_jcc value: [0. 0.125 0.11111111 0. 0. 0. 0. 0. 0.14285714 0.16666667] mean value: 0.05456349206349206 key: train_jcc value: [0.11864407 0.11111111 0.20689655 0.2 0.14754098 0.11666667 0.08196721 0.06666667 0.13114754 0.11666667] mean value: 0.12973074683367772 key: TN value: 232 mean value: 232.0 key: FP value: 60 mean value: 60.0 key: FN value: 4 mean value: 4.0 key: TP value: 4 mean value: 4.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.12 Accuracy on Blind test: 0.78 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01243377 0.01764393 0.01851797 0.01582813 0.02090073 0.01800299 0.0202446 0.02028775 0.019696 0.01619339] mean value: 0.01797492504119873 key: score_time value: [0.00890422 0.01080108 0.01113772 0.01139307 0.01157093 0.01157641 0.01155877 0.01151228 0.01154518 0.0116353 ] mean value: 0.01116349697113037 key: test_mcc value: [0.33660139 0.55841694 0.47913962 0.34151451 0.78446454 0.79166667 0.66666667 0.79166667 0.58333333 0.53452248] mean value: 0.5867992806326103 key: train_mcc value: [0.60379629 0.94410289 0.95753763 0.86329914 0.95608328 0.93412492 0.96817595 0.97854339 0.933743 0.80660901] mean value: 0.894601550428449 key: test_fscore value: [0.25 0.66666667 0.54545455 0.4 0.8 0.83333333 0.66666667 0.83333333 0.66666667 0.5 ] mean value: 0.6162121212121212 key: train_fscore value: [0.59259259 0.95575221 0.96610169 0.88888889 0.96551724 0.94827586 0.97478992 0.98305085 0.94545455 0.83495146] mean value: 0.9055375257423632 key: test_precision value: [1. 0.625 0.75 0.66666667 1. 0.83333333 1. 0.83333333 0.66666667 1. ] mean value: 0.8375 key: train_precision value: [1. 0.96428571 0.93442623 0.94117647 0.96551724 0.94827586 0.95081967 0.96666667 1. 0.95555556] mean value: 0.9626723412183793 key: test_recall value: [0.14285714 0.71428571 0.42857143 0.28571429 0.66666667 0.83333333 0.5 0.83333333 0.66666667 0.33333333] mean value: 0.5404761904761906 key: train_recall value: [0.42105263 0.94736842 1. 0.84210526 0.96551724 0.94827586 1. 1. 0.89655172 0.74137931] mean value: 0.8762250453720508 key: test_accuracy value: [0.8 0.83333333 0.83333333 0.8 0.93333333 0.93333333 0.9 0.93333333 0.86666667 0.86666667] mean value: 0.8700000000000001 key: train_accuracy value: [0.87777778 0.98148148 0.98518519 0.95555556 0.98518519 0.97777778 0.98888889 0.99259259 0.97777778 0.93703704] mean value: 0.9659259259259259 key: test_roc_auc value: [0.57142857 0.79192547 0.69254658 0.62111801 0.83333333 0.89583333 0.75 0.89583333 0.79166667 0.66666667] mean value: 0.7510351966873706 key: train_roc_auc value: [0.71052632 0.96898937 0.99061033 0.91401038 0.97804164 0.96706246 0.99292453 0.99528302 0.94827586 0.86597267] mean value: 0.933169657950442 key: test_jcc value: [0.14285714 0.5 0.375 0.25 0.66666667 0.71428571 0.5 0.71428571 0.5 0.33333333] mean value: 0.46964285714285714 key: train_jcc value: [0.42105263 0.91525424 0.93442623 0.8 0.93333333 0.90163934 0.95081967 0.96666667 0.89655172 0.71666667] mean value: 0.8436410505573321 key: TN value: 227 mean value: 227.0 key: FP value: 30 mean value: 30.0 key: FN value: 9 mean value: 9.0 key: TP value: 34 mean value: 34.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.8 Accuracy on Blind test: 0.93 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01539302 0.01551223 0.01514006 0.0147841 0.01656079 0.01537895 0.01675487 0.01508403 0.01645827 0.01462555] mean value: 0.015569186210632325 key: score_time value: [0.01136613 0.01145267 0.0115869 0.01156878 0.01143694 0.01207328 0.01174545 0.01152802 0.01147032 0.01155066] mean value: 0.011577916145324708 key: test_mcc value: [0.71098137 0.51604685 0.34151451 0.34151451 0.53452248 0. 0.58191437 0.67082039 0.58333333 0.58333333] mean value: 0.48639811508822567 key: train_mcc value: [0.93435553 0.65869937 0.79428925 0.88679821 0.46369186 0.41232029 0.7215912 0.84179128 0.90023488 0.9034818 ] mean value: 0.7517253668919773 key: test_fscore value: [0.72727273 0.60869565 0.4 0.4 0.5 0. 0.66666667 0.72727273 0.66666667 0.66666667] mean value: 0.5363241106719367 key: train_fscore value: [0.94827586 0.7125 0.8125 0.90909091 0.4109589 0.34285714 0.76821192 0.87037037 0.91588785 0.92436975] mean value: 0.7615022707393229 key: test_precision value: [1. 0.4375 0.66666667 0.66666667 1. 0. 0.55555556 0.8 0.66666667 0.66666667] mean value: 0.6459722222222222 key: train_precision value: [0.93220339 0.55339806 1. 0.94339623 1. 1. 0.62365591 0.94 1. 0.90163934] mean value: 0.889429293273882 key: test_recall value: [0.57142857 1. 0.28571429 0.28571429 0.33333333 0. 0.83333333 0.66666667 0.66666667 0.66666667] mean value: 0.5309523809523811 key: train_recall value: [0.96491228 1. 0.68421053 0.87719298 0.25862069 0.20689655 1. 0.81034483 0.84482759 0.94827586] mean value: 0.7595281306715063 key: test_accuracy value: [0.9 0.7 0.8 0.8 0.86666667 0.8 0.83333333 0.9 0.86666667 0.86666667] mean value: 0.8333333333333334 key: train_accuracy value: [0.97777778 0.82962963 0.93333333 0.96296296 0.84074074 0.82962963 0.87037037 0.94814815 0.96666667 0.96666667] mean value: 0.9125925925925926 key: test_roc_auc value: [0.78571429 0.80434783 0.62111801 0.62111801 0.66666667 0.5 0.83333333 0.8125 0.79166667 0.79166667] mean value: 0.7228131469979296 key: train_roc_auc value: [0.97306647 0.89201878 0.84210526 0.93155424 0.62931034 0.60344828 0.91745283 0.89809694 0.92241379 0.95998699] mean value: 0.8569453922911965 key: test_jcc value: [0.57142857 0.4375 0.25 0.25 0.33333333 0. 0.5 0.57142857 0.5 0.5 ] mean value: 0.3913690476190476 key: train_jcc value: [0.90163934 0.55339806 0.68421053 0.83333333 0.25862069 0.20689655 0.62365591 0.7704918 0.84482759 0.859375 ] mean value: 0.6536448807007236 key: TN value: 216 mean value: 216.0 key: FP value: 30 mean value: 30.0 key: FN value: 20 mean value: 20.0 key: TP value: 34 mean value: 34.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.42 Accuracy on Blind test: 0.64 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.14356923 0.12530208 0.12540674 0.12698865 0.12595773 0.12572432 0.12548542 0.125597 0.12431383 0.12713146] mean value: 0.12754764556884765 key: score_time value: [0.01492476 0.0147512 0.01494718 0.01527262 0.01499748 0.01501465 0.014992 0.01485825 0.01556373 0.01499319] mean value: 0.01503150463104248 key: test_mcc value: [1. 0.84270097 0.90632697 0.90632697 1. 0.70929937 0.78446454 0.90632697 0.79166667 1. ] mean value: 0.8847112445959876 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.875 0.92307692 0.92307692 1. 0.76923077 0.8 0.92307692 0.83333333 1. ] mean value: 0.9046794871794871 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.77777778 1. 1. 1. 0.71428571 1. 0.85714286 0.83333333 1. ] mean value: 0.9182539682539683 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.85714286 0.85714286 1. 0.83333333 0.66666667 1. 0.83333333 1. ] mean value: 0.9047619047619048 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.93333333 0.96666667 0.96666667 1. 0.9 0.93333333 0.96666667 0.93333333 1. ] mean value: 0.96 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.95652174 0.92857143 0.92857143 1. 0.875 0.83333333 0.97916667 0.89583333 1. ] mean value: 0.9396997929606625 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.77777778 0.85714286 0.85714286 1. 0.625 0.66666667 0.85714286 0.71428571 1. ] mean value: 0.8355158730158732 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 230 mean value: 230.0 key: FP value: 6 mean value: 6.0 key: FN value: 6 mean value: 6.0 key: TP value: 58 mean value: 58.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.87 Accuracy on Blind test: 0.95 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.03868628 0.03752804 0.03799224 0.03717208 0.03749418 0.03673482 0.0372963 0.03851962 0.03842163 0.04086733] mean value: 0.03807125091552734 key: score_time value: [0.0216217 0.01853013 0.02497888 0.02284074 0.01729608 0.02156329 0.01863456 0.02311659 0.01805592 0.02187967] mean value: 0.020851755142211915 key: test_mcc value: [1. 0.91485328 0.90632697 1. 0.66666667 0.70929937 0.89442719 0.90632697 0.79166667 1. ] mean value: 0.8789567103102754 key: train_mcc value: [0.96691391 0.96648679 0.96648679 0.96648679 0.96691391 0.97800497 0.95608328 0.96691391 0.97804164 0.97804164] mean value: 0.9690373628729612 key: test_fscore value: [1. 0.93333333 0.92307692 1. 0.66666667 0.76923077 0.90909091 0.92307692 0.83333333 1. ] mean value: 0.8957808857808857 key: train_fscore value: [0.97391304 0.97345133 0.97345133 0.97345133 0.97391304 0.98245614 0.96551724 0.97391304 0.98275862 0.98275862] mean value: 0.9755583735845164 key: test_precision value: [1. 0.875 1. 1. 1. 0.71428571 1. 0.85714286 0.83333333 1. ] mean value: 0.9279761904761905 key: train_precision value: [0.96551724 0.98214286 0.98214286 0.98214286 0.98245614 1. 0.96551724 0.98245614 0.98275862 0.98275862] mean value: 0.9807892576268257 key: test_recall value: [1. 1. 0.85714286 1. 0.5 0.83333333 0.83333333 1. 0.83333333 1. ] mean value: 0.8857142857142858 key: train_recall value: [0.98245614 0.96491228 0.96491228 0.96491228 0.96551724 0.96551724 0.96551724 0.96551724 0.98275862 0.98275862] mean value: 0.9704779189352692 key: test_accuracy value: [1. 0.96666667 0.96666667 1. 0.9 0.9 0.96666667 0.96666667 0.93333333 1. ] mean value: 0.96 key: train_accuracy value: [0.98888889 0.98888889 0.98888889 0.98888889 0.98888889 0.99259259 0.98518519 0.98888889 0.99259259 0.99259259] mean value: 0.9896296296296295 key: test_roc_auc value: [1. 0.97826087 0.92857143 1. 0.75 0.875 0.91666667 0.97916667 0.89583333 1. ] mean value: 0.9323498964803314 key: train_roc_auc value: [0.98653323 0.98010872 0.98010872 0.98010872 0.98040013 0.98275862 0.97804164 0.98040013 0.98902082 0.98902082] mean value: 0.9826501562078243 key: test_jcc value: [1. 0.875 0.85714286 1. 0.5 0.625 0.83333333 0.85714286 0.71428571 1. ] mean value: 0.8261904761904763 key: train_jcc value: [0.94915254 0.94827586 0.94827586 0.94827586 0.94915254 0.96551724 0.93333333 0.94915254 0.96610169 0.96610169] mean value: 0.9523339177868693 key: TN value: 231 mean value: 231.0 key: FP value: 7 mean value: 7.0 key: FN value: 5 mean value: 5.0 key: TP value: 57 mean value: 57.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.82 Accuracy on Blind test: 0.94 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.0600543 0.10853124 0.06738281 0.04305983 0.0710392 0.10982752 0.06292367 0.03897572 0.03897929 0.08064604] mean value: 0.06814196109771728 key: score_time value: [0.02326965 0.02519727 0.01338458 0.02152944 0.02401328 0.03619766 0.01291752 0.01300049 0.01302505 0.02103305] mean value: 0.02035679817199707 key: test_mcc value: [ 0. 0.07881104 0.16850509 -0.1024439 0. 0.20044593 -0.09284767 -0.09284767 -0.16666667 -0.16666667] mean value: -0.01737105065888995 key: train_mcc value: [0.77032728 0.78234837 0.81794963 0.82967804 0.82056154 0.83209945 0.808962 0.7855616 0.808962 0.808962 ] mean value: 0.8065411893021752 key: test_fscore value: [0. 0.2 0.22222222 0. 0. 0.25 0. 0. 0. 0. ] mean value: 0.06722222222222222 key: train_fscore value: [0.78723404 0.8 0.83673469 0.84848485 0.84 0.85148515 0.82828283 0.80412371 0.82828283 0.82828283] mean value: 0.8252910929619134 key: test_precision value: [0. 0.33333333 0.5 0. 0. 0.5 0. 0. 0. 0. ] mean value: 0.13333333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.14285714 0.14285714 0. 0. 0.16666667 0. 0. 0. 0. ] mean value: 0.04523809523809523 key: train_recall value: [0.64912281 0.66666667 0.71929825 0.73684211 0.72413793 0.74137931 0.70689655 0.67241379 0.70689655 0.70689655] mean value: 0.7030550514216577 key: test_accuracy value: [0.76666667 0.73333333 0.76666667 0.73333333 0.8 0.8 0.76666667 0.76666667 0.7 0.7 ] mean value: 0.7533333333333334 key: train_accuracy value: [0.92592593 0.92962963 0.94074074 0.94444444 0.94074074 0.94444444 0.93703704 0.92962963 0.93703704 0.93703704] mean value: 0.9366666666666668 key: test_roc_auc value: [0.5 0.52795031 0.54968944 0.47826087 0.5 0.5625 0.47916667 0.47916667 0.4375 0.4375 ] mean value: 0.49517339544513456 key: train_roc_auc value: [0.8245614 0.83333333 0.85964912 0.86842105 0.86206897 0.87068966 0.85344828 0.8362069 0.85344828 0.85344828] mean value: 0.8515275257108289 key: test_jcc value: [0. 0.11111111 0.125 0. 0. 0.14285714 0. 0. 0. 0. ] mean value: 0.037896825396825394 key: train_jcc value: [0.64912281 0.66666667 0.71929825 0.73684211 0.72413793 0.74137931 0.70689655 0.67241379 0.70689655 0.70689655] mean value: 0.7030550514216577 key: TN value: 223 mean value: 223.0 key: FP value: 61 mean value: 61.0 key: FN value: 13 mean value: 13.0 key: TP value: 3 mean value: 3.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") mean value: 31.0 MCC on Blind test: 0.22 Accuracy on Blind test: 0.8 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.44956064 0.42286658 0.42664719 0.4276886 0.42576098 0.44159317 0.42826962 0.42824292 0.42826033 0.42644954] mean value: 0.4305339574813843 key: score_time value: [0.00901651 0.00893927 0.00908875 0.00892687 0.00897551 0.00908875 0.00901818 0.00894594 0.00910378 0.00907588] mean value: 0.0090179443359375 key: test_mcc value: [1. 0.84270097 0.8136646 1. 0.79166667 0.70929937 0.89442719 0.90632697 0.79166667 1. ] mean value: 0.8749752424997082 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.875 0.85714286 1. 0.83333333 0.76923077 0.90909091 0.92307692 0.83333333 1. ] mean value: 0.9000208125208126 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.77777778 0.85714286 1. 0.83333333 0.71428571 1. 0.85714286 0.83333333 1. ] mean value: 0.8873015873015874 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.85714286 1. 0.83333333 0.83333333 0.83333333 1. 0.83333333 1. ] mean value: 0.9190476190476191 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.93333333 0.93333333 1. 0.93333333 0.9 0.96666667 0.96666667 0.93333333 1. ] mean value: 0.9566666666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.95652174 0.9068323 1. 0.89583333 0.875 0.91666667 0.97916667 0.89583333 1. ] mean value: 0.9425854037267081 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.77777778 0.75 1. 0.71428571 0.625 0.83333333 0.85714286 0.71428571 1. ] mean value: 0.8271825396825397 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 228 mean value: 228.0 key: FP value: 5 mean value: 5.0 key: FN value: 8 mean value: 8.0 key: TP value: 59 mean value: 59.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.85 Accuracy on Blind test: 0.95 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01944017 0.02147317 0.02204108 0.02193952 0.02178168 0.02203751 0.0221312 0.02462649 0.02226901 0.02220225] mean value: 0.02199420928955078 key: score_time value: [0.01194978 0.01285076 0.01210022 0.01319599 0.01441765 0.0147438 0.01435232 0.01223373 0.01328063 0.01389551] mean value: 0.013302040100097657 key: test_mcc value: [ 0.01545612 -0.03524537 0.16850509 0.01545612 -0.13363062 0. 0.20044593 0.11111111 -0.16666667 -0.13363062] mean value: 0.004180109125903944 key: train_mcc value: [0.29142448 0.26553052 0.31537228 0.31537228 0.28822231 0.33407831 0.28822231 0.31190697 0.33407831 0.28822231] mean value: 0.303243008578363 key: test_fscore value: [0.18181818 0.16666667 0.22222222 0.18181818 0. 0.18181818 0.25 0.22222222 0. 0. ] mean value: 0.14065656565656565 key: train_fscore value: [0.19047619 0.16129032 0.21875 0.21875 0.1875 0.24242424 0.1875 0.21538462 0.24242424 0.1875 ] mean value: 0.20519996132899357 key: test_precision value: [0.25 0.2 0.5 0.25 0. 0.2 0.5 0.33333333 0. 0. ] mean value: 0.22333333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.14285714 0.14285714 0.14285714 0.14285714 0. 0.16666667 0.16666667 0.16666667 0. 0. ] mean value: 0.10714285714285714 key: train_recall value: [0.10526316 0.0877193 0.12280702 0.12280702 0.10344828 0.13793103 0.10344828 0.12068966 0.13793103 0.10344828] mean value: 0.11454930429522081 key: test_accuracy value: [0.7 0.66666667 0.76666667 0.7 0.73333333 0.7 0.8 0.76666667 0.7 0.73333333] mean value: 0.7266666666666668 key: train_accuracy value: [0.81111111 0.80740741 0.81481481 0.81481481 0.80740741 0.81481481 0.80740741 0.81111111 0.81481481 0.80740741] mean value: 0.8111111111111111 key: test_roc_auc value: [0.50621118 0.48447205 0.54968944 0.50621118 0.45833333 0.5 0.5625 0.54166667 0.4375 0.45833333] mean value: 0.500491718426501 key: train_roc_auc value: [0.55263158 0.54385965 0.56140351 0.56140351 0.55172414 0.56896552 0.55172414 0.56034483 0.56896552 0.55172414] mean value: 0.5572746521476104 key: test_jcc value: [0.1 0.09090909 0.125 0.1 0. 0.1 0.14285714 0.125 0. 0. ] mean value: 0.07837662337662338 key: train_jcc value: [0.10526316 0.0877193 0.12280702 0.12280702 0.10344828 0.13793103 0.10344828 0.12068966 0.13793103 0.10344828] mean value: 0.11454930429522081 key: TN value: 211 mean value: 211.0 key: FP value: 57 mean value: 57.0 key: FN value: 25 mean value: 25.0 key: TP value: 7 mean value: 7.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: -0.08 Accuracy on Blind test: 0.73 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02265096 0.03459263 0.03272867 0.03490329 0.03444433 0.03439617 0.03457141 0.03514528 0.03444648 0.03956985] mean value: 0.03374490737915039 key: score_time value: [0.02268505 0.02258205 0.02024865 0.02170777 0.02348852 0.02161622 0.02335501 0.02056766 0.02193522 0.02281809] mean value: 0.022100424766540526 key: test_mcc value: [0.60421798 0.55841694 0.34151451 0.16850509 0.78446454 0.79166667 0.78446454 0.90632697 0.58333333 0.53931937] mean value: 0.6062229936558883 key: train_mcc value: [0.88679821 0.89847907 0.91011834 0.89847907 0.87741118 0.92271775 0.88828904 0.94481583 0.91083445 0.87741118] mean value: 0.9015354123498576 key: test_fscore value: [0.6 0.66666667 0.4 0.22222222 0.8 0.83333333 0.8 0.92307692 0.66666667 0.6 ] mean value: 0.6511965811965812 key: train_fscore value: [0.90909091 0.91891892 0.92857143 0.91891892 0.90265487 0.93913043 0.91071429 0.95652174 0.92857143 0.90265487] mean value: 0.9215747798212208 key: test_precision value: [1. 0.625 0.66666667 0.5 1. 0.83333333 1. 0.85714286 0.66666667 0.75 ] mean value: 0.7898809523809525 key: train_precision value: [0.94339623 0.94444444 0.94545455 0.94444444 0.92727273 0.94736842 0.94444444 0.96491228 0.96296296 0.92727273] mean value: 0.9451973224465778 key: test_recall value: [0.42857143 0.71428571 0.28571429 0.14285714 0.66666667 0.83333333 0.66666667 1. 0.66666667 0.5 ] mean value: 0.5904761904761905 key: train_recall value: [0.87719298 0.89473684 0.9122807 0.89473684 0.87931034 0.93103448 0.87931034 0.94827586 0.89655172 0.87931034] mean value: 0.8992740471869327 key: test_accuracy value: [0.86666667 0.83333333 0.8 0.76666667 0.93333333 0.93333333 0.93333333 0.96666667 0.86666667 0.86666667] mean value: 0.8766666666666667 key: train_accuracy value: [0.96296296 0.96666667 0.97037037 0.96666667 0.95925926 0.97407407 0.96296296 0.98148148 0.97037037 0.95925926] mean value: 0.9674074074074074 key: test_roc_auc value: [0.71428571 0.79192547 0.62111801 0.54968944 0.83333333 0.89583333 0.83333333 0.97916667 0.79166667 0.72916667] mean value: 0.7739518633540373 key: train_roc_auc value: [0.93155424 0.94032617 0.9490981 0.94032617 0.93022121 0.95844177 0.9325797 0.96942095 0.94355888 0.93022121] mean value: 0.9425748391661479 key: test_jcc value: [0.42857143 0.5 0.25 0.125 0.66666667 0.71428571 0.66666667 0.85714286 0.5 0.42857143] mean value: 0.5136904761904761 key: train_jcc value: [0.83333333 0.85 0.86666667 0.85 0.82258065 0.8852459 0.83606557 0.91666667 0.86666667 0.82258065] mean value: 0.854980609906575 key: TN value: 226 mean value: 226.0 key: FP value: 27 mean value: 27.0 key: FN value: 10 mean value: 10.0 key: TP value: 37 mean value: 37.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.67 Accuracy on Blind test: 0.9 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.24151182 0.25762248 0.23883343 0.24260068 0.24528241 0.24203515 0.33177257 0.24591899 0.2431798 0.36651111] mean value: 0.2655268430709839 key: score_time value: [0.02142286 0.02139711 0.02129865 0.02379942 0.02299094 0.02134085 0.02335072 0.02251148 0.02318573 0.01824236] mean value: 0.02195401191711426 key: test_mcc value: [0.60421798 0.55841694 0.34151451 0.16850509 0.67082039 0.79166667 0.78446454 0.76376262 0.58191437 0.53931937] mean value: 0.5804602478536405 key: train_mcc value: [0.88679821 0.89847907 0.91011834 0.89847907 0.93515969 0.92271775 0.88828904 0.95685154 0.93412492 0.87741118] mean value: 0.9108428819808394 key: test_fscore value: [0.6 0.66666667 0.4 0.22222222 0.72727273 0.83333333 0.8 0.8 0.66666667 0.6 ] mean value: 0.6316161616161616 key: train_fscore value: [0.90909091 0.91891892 0.92857143 0.91891892 0.94915254 0.93913043 0.91071429 0.96610169 0.94827586 0.90265487] mean value: 0.9291529862610808 key: test_precision value: [1. 0.625 0.66666667 0.5 0.8 0.83333333 1. 0.66666667 0.55555556 0.75 ] mean value: 0.7397222222222222 key: train_precision value: [0.94339623 0.94444444 0.94545455 0.94444444 0.93333333 0.94736842 0.94444444 0.95 0.94827586 0.92727273] mean value: 0.9428434448930633 key: test_recall value: [0.42857143 0.71428571 0.28571429 0.14285714 0.66666667 0.83333333 0.66666667 1. 0.83333333 0.5 ] mean value: 0.6071428571428571 key: train_recall value: [0.87719298 0.89473684 0.9122807 0.89473684 0.96551724 0.93103448 0.87931034 0.98275862 0.94827586 0.87931034] mean value: 0.9165154264972776 key: test_accuracy value: [0.86666667 0.83333333 0.8 0.76666667 0.9 0.93333333 0.93333333 0.9 0.83333333 0.86666667] mean value: 0.8633333333333333 key: train_accuracy value: [0.96296296 0.96666667 0.97037037 0.96666667 0.97777778 0.97407407 0.96296296 0.98518519 0.97777778 0.95925926] mean value: 0.9703703703703702 key: test_roc_auc value: [0.71428571 0.79192547 0.62111801 0.54968944 0.8125 0.89583333 0.83333333 0.9375 0.83333333 0.72916667] mean value: 0.771868530020704 key: train_roc_auc value: [0.93155424 0.94032617 0.9490981 0.94032617 0.97332466 0.95844177 0.9325797 0.98430384 0.96706246 0.93022121] mean value: 0.9507238307081127 key: test_jcc value: [0.42857143 0.5 0.25 0.125 0.57142857 0.71428571 0.66666667 0.66666667 0.5 0.42857143] mean value: 0.4851190476190476 key: train_jcc value: [0.83333333 0.85 0.86666667 0.85 0.90322581 0.8852459 0.83606557 0.93442623 0.90163934 0.82258065] mean value: 0.8683183500793232 key: TN value: 221 mean value: 221.0 key: FP value: 26 mean value: 26.0 key: FN value: 15 mean value: 15.0 key: TP value: 38 mean value: 38.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:130: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:131: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_CV['Resampling'] = rs_none /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:136: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:137: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_BT['Resampling'] = rs_none /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.67 Accuracy on Blind test: 0.9 PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.07176328 0.12339711 0.08598828 0.03456306 0.04575658 0.054353 0.05356979 0.05598712 0.03727078 0.03516769] mean value: 0.059781670570373535 key: score_time value: [0.01241064 0.01525736 0.01444387 0.01452112 0.01287413 0.0130868 0.01322174 0.01300502 0.013304 0.01292682] mean value: 0.013505148887634277 key: test_mcc value: [0.91666667 0.91986621 0.95825929 0.8729597 0.95833333 0.95825929 0.91485507 0.87979456 0.7876601 0.79308818] mean value: 0.8959742397987605 key: train_mcc value: [0.95287259 0.95287259 0.94357214 0.9576579 0.95298417 0.93883426 0.9483278 0.95294092 0.95765696 0.93891474] mean value: 0.9496634068713254 key: test_fscore value: [0.95833333 0.96 0.97959184 0.93877551 0.9787234 0.97959184 0.95652174 0.93877551 0.88888889 0.88372093] mean value: 0.9462922989718086 key: train_fscore value: [0.97652582 0.97652582 0.97183099 0.97882353 0.97652582 0.96941176 0.97435897 0.97652582 0.97892272 0.96969697] mean value: 0.9749148227101694 key: test_precision value: [0.95833333 0.92307692 0.96 0.92 1. 0.96 0.95652174 0.88461538 0.90909091 0.95 ] mean value: 0.9421638289246983 key: train_precision value: [0.97196262 0.97196262 0.96728972 0.97652582 0.97196262 0.96713615 0.96759259 0.97652582 0.97663551 0.96296296] mean value: 0.9710556433094937 key: test_recall value: [0.95833333 1. 1. 0.95833333 0.95833333 1. 0.95652174 1. 0.86956522 0.82608696] mean value: 0.9527173913043478 key: train_recall value: [0.98113208 0.98113208 0.97641509 0.98113208 0.98113208 0.97169811 0.98122066 0.97652582 0.98122066 0.97652582] mean value: 0.9788134467180442 key: test_accuracy value: [0.95833333 0.95833333 0.9787234 0.93617021 0.9787234 0.9787234 0.95744681 0.93617021 0.89361702 0.89361702] mean value: 0.9469858156028369 key: train_accuracy value: [0.97641509 0.97641509 0.97176471 0.97882353 0.97647059 0.96941176 0.97411765 0.97647059 0.97882353 0.96941176] mean value: 0.9748124306326303 key: test_roc_auc value: [0.95833333 0.95833333 0.97826087 0.93568841 0.97916667 0.97826087 0.95742754 0.9375 0.89311594 0.89221014] mean value: 0.9468297101449276 key: train_roc_auc value: [0.97641509 0.97641509 0.97177562 0.97882895 0.97648153 0.96941713 0.97410089 0.97647046 0.97881788 0.96939499] mean value: 0.9748117636637434 key: test_jcc value: [0.92 0.92307692 0.96 0.88461538 0.95833333 0.96 0.91666667 0.88461538 0.8 0.79166667] mean value: 0.8998974358974359 key: train_jcc value: [0.95412844 0.95412844 0.94520548 0.95852535 0.95412844 0.94063927 0.95 0.95412844 0.9587156 0.94117647] mean value: 0.9510775922866967 key: TN value: 222 mean value: 222.0 key: FP value: 11 mean value: 11.0 key: FN value: 14 mean value: 14.0 key: TP value: 225 mean value: 225.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.71 Accuracy on Blind test: 0.9 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.77030396 0.77836871 0.9878099 0.79601216 0.92038512 0.77858377 0.77486515 0.94918537 0.7837646 0.77919817] mean value: 0.8318476915359497 key: score_time value: [0.0130918 0.01311946 0.01454043 0.01379514 0.01311183 0.01433945 0.01438642 0.01442361 0.01438904 0.01400876] mean value: 0.01392059326171875 key: test_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.9591663 0.9591663 0.95825929 0.91804649 0.91804649 0.95825929 1. 0.91833182 0.91833182 0.91804649] mean value: 0.942565431502137 key: train_mcc value: [0.98594778 1. 0.985981 1. 1. 1. 1. 0.98598008 1. 0.99063185] mean value: 0.994854070833143 key: test_fscore value: [0.97959184 0.97959184 0.97959184 0.96 0.96 0.97959184 1. 0.95833333 0.95833333 0.95454545] mean value: 0.9709579468150897 key: train_fscore value: [0.99297424 1. 0.99297424 1. 1. 1. 1. 0.99300699 1. 0.9953271 ] mean value: 0.9974282573562487 key: test_precision value: [0.96 0.96 0.96 0.92307692 0.92307692 0.96 1. 0.92 0.92 1. ] mean value: 0.9526153846153846 key: train_precision value: [0.98604651 1. 0.98604651 1. 1. 1. 1. 0.98611111 1. 0.99069767] mean value: 0.9948901808785531 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.91304348] mean value: 0.9913043478260869 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97916667 0.97916667 0.9787234 0.95744681 0.95744681 0.9787234 1. 0.95744681 0.95744681 0.95744681] mean value: 0.9703014184397164 key: train_accuracy value: [0.99292453 1. 0.99294118 1. 1. 1. 1. 0.99294118 1. 0.99529412] mean value: 0.9974100998890123 key: test_roc_auc value: [0.97916667 0.97916667 0.97826087 0.95652174 0.95652174 0.97826087 1. 0.95833333 0.95833333 0.95652174] mean value: 0.9701086956521741 key: train_roc_auc value: [0.99292453 1. 0.99295775 1. 1. 1. 1. 0.99292453 1. 0.99528302] mean value: 0.9974089821950571 key: test_jcc value: [0.96 0.96 0.96 0.92307692 0.92307692 0.96 1. 0.92 0.92 0.91304348] mean value: 0.9439197324414715 key: train_jcc value: [0.98604651 1. 0.98604651 1. 1. 1. 1. 0.98611111 1. 0.99069767] mean value: 0.9948901808785531 key: TN value: 225 mean value: 225.0 key: FP value: 3 mean value: 3.0 key: FN value: 11 mean value: 11.0 key: TP value: 233 mean value: 233.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.85 Accuracy on Blind test: 0.95 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01458406 0.01315212 0.01017809 0.01075673 0.00951028 0.00990939 0.00977349 0.00975919 0.00944519 0.00961947] mean value: 0.010668802261352538 key: score_time value: [0.01208496 0.00924253 0.00898147 0.00877953 0.00888085 0.00893021 0.00886679 0.00882578 0.00890112 0.00885224] mean value: 0.00923454761505127 key: test_mcc value: [0.46195658 0.50709255 0.49454913 0.53734864 0.37458096 0.58127976 0.20543379 0.49819858 0.5326087 0.32123465] mean value: 0.4514283323551038 key: train_mcc value: [0.56475627 0.58494523 0.50437209 0.61504595 0.53174355 0.51048267 0.52595168 0.5537337 0.53183435 0.53454055] mean value: 0.5457406041468285 key: test_fscore value: [0.74509804 0.76923077 0.76923077 0.75555556 0.72727273 0.80769231 0.64150943 0.76 0.76595745 0.66666667] mean value: 0.7408213715635257 key: train_fscore value: [0.79744136 0.80519481 0.76789588 0.80097087 0.78205128 0.77378436 0.7803838 0.79229122 0.78496868 0.78372591] mean value: 0.7868708170032758 key: test_precision value: [0.7037037 0.71428571 0.71428571 0.80952381 0.64516129 0.75 0.56666667 0.7037037 0.75 0.64 ] mean value: 0.6997330602491891 key: train_precision value: [0.72762646 0.744 0.71084337 0.825 0.71484375 0.70114943 0.71484375 0.72834646 0.70676692 0.72047244] mean value: 0.7293892572856329 key: test_recall value: [0.79166667 0.83333333 0.83333333 0.70833333 0.83333333 0.875 0.73913043 0.82608696 0.7826087 0.69565217] mean value: 0.7918478260869565 key: train_recall value: [0.88207547 0.87735849 0.83490566 0.77830189 0.86320755 0.86320755 0.85915493 0.8685446 0.88262911 0.85915493] mean value: 0.8568540171848703 key: test_accuracy value: [0.72916667 0.75 0.74468085 0.76595745 0.68085106 0.78723404 0.59574468 0.74468085 0.76595745 0.65957447] mean value: 0.7223847517730496 key: train_accuracy value: [0.7759434 0.78773585 0.74823529 0.80705882 0.76 0.74823529 0.75764706 0.77176471 0.75764706 0.76235294] mean value: 0.7676620421753607 key: test_roc_auc value: [0.72916667 0.75 0.74275362 0.76721014 0.67753623 0.78532609 0.59873188 0.74637681 0.76630435 0.66032609] mean value: 0.7223731884057971 key: train_roc_auc value: [0.7759434 0.78773585 0.74843875 0.80699132 0.76024227 0.74850518 0.75740765 0.77153645 0.75735229 0.76212463] mean value: 0.7676277792541413 key: test_jcc value: [0.59375 0.625 0.625 0.60714286 0.57142857 0.67741935 0.47222222 0.61290323 0.62068966 0.5 ] mean value: 0.5905555886611226 key: train_jcc value: [0.66312057 0.67391304 0.62323944 0.66801619 0.64210526 0.63103448 0.63986014 0.65602837 0.64604811 0.6443662 ] mean value: 0.6487731803525566 key: TN value: 154 mean value: 154.0 key: FP value: 49 mean value: 49.0 key: FN value: 82 mean value: 82.0 key: TP value: 187 mean value: 187.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.14 Accuracy on Blind test: 0.59 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00972295 0.0105269 0.01007748 0.00972223 0.00964475 0.00968885 0.01084328 0.00980568 0.01090193 0.00983763] mean value: 0.010077166557312011 key: score_time value: [0.00889754 0.0095396 0.00877905 0.00867987 0.00916791 0.00882196 0.00956964 0.0089047 0.00913191 0.00880623] mean value: 0.009029841423034668 key: test_mcc value: [0.33333333 0.54213748 0.27657348 0.44874504 0.23593505 0.40653424 0.45948781 0.40398551 0.40437762 0.23435724] mean value: 0.37454668007011227 key: train_mcc value: [0.44815808 0.45301166 0.46390277 0.44950269 0.4494076 0.45881962 0.46352644 0.44975749 0.46352644 0.49669838] mean value: 0.4596311163425316 key: test_fscore value: [0.66666667 0.7755102 0.66666667 0.74509804 0.60869565 0.69565217 0.74509804 0.69565217 0.68181818 0.57142857] mean value: 0.6852286369093091 key: train_fscore value: [0.72599532 0.73023256 0.73611111 0.72076372 0.72340426 0.72813239 0.73239437 0.73103448 0.73239437 0.75288684] mean value: 0.731334940276696 key: test_precision value: [0.66666667 0.76 0.62962963 0.7037037 0.63636364 0.72727273 0.67857143 0.69565217 0.71428571 0.63157895] mean value: 0.6843724627774972 key: train_precision value: [0.72093023 0.72018349 0.72272727 0.7294686 0.72511848 0.72985782 0.73239437 0.71621622 0.73239437 0.74090909] mean value: 0.727019993339497 key: test_recall value: [0.66666667 0.79166667 0.70833333 0.79166667 0.58333333 0.66666667 0.82608696 0.69565217 0.65217391 0.52173913] mean value: 0.6903985507246376 key: train_recall value: [0.73113208 0.74056604 0.75 0.71226415 0.72169811 0.72641509 0.73239437 0.74647887 0.73239437 0.76525822] mean value: 0.7358601293294358 key: test_accuracy value: [0.66666667 0.77083333 0.63829787 0.72340426 0.61702128 0.70212766 0.72340426 0.70212766 0.70212766 0.61702128] mean value: 0.6863031914893617 key: train_accuracy value: [0.7240566 0.72641509 0.73176471 0.72470588 0.72470588 0.72941176 0.73176471 0.72470588 0.73176471 0.74823529] mean value: 0.7297530521642619 key: test_roc_auc value: [0.66666667 0.77083333 0.63677536 0.72192029 0.61775362 0.70289855 0.72554348 0.70199275 0.70108696 0.61503623] mean value: 0.6860507246376811 key: train_roc_auc value: [0.7240566 0.72641509 0.73180751 0.72467668 0.72469882 0.72940473 0.73176322 0.72465453 0.73176322 0.74819515] mean value: 0.7297435556736646 key: test_jcc value: [0.5 0.63333333 0.5 0.59375 0.4375 0.53333333 0.59375 0.53333333 0.51724138 0.4 ] mean value: 0.5242241379310345 key: train_jcc value: [0.56985294 0.57509158 0.58241758 0.56343284 0.56666667 0.57249071 0.57777778 0.57608696 0.57777778 0.6037037 ] mean value: 0.5765298523273891 key: TN value: 161 mean value: 161.0 key: FP value: 73 mean value: 73.0 key: FN value: 75 mean value: 75.0 key: TP value: 163 mean value: 163.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.12 Accuracy on Blind test: 0.6 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00896263 0.00907159 0.00911069 0.00904894 0.00904322 0.00904942 0.00902128 0.00901937 0.00896335 0.00900316] mean value: 0.009029364585876465 key: score_time value: [0.01160336 0.0112009 0.01140761 0.01143956 0.01129055 0.01132679 0.01131845 0.01161385 0.01135063 0.01171207] mean value: 0.011426377296447753 key: test_mcc value: [0.45873171 0.58333333 0.62966842 0.57560058 0.5326087 0.58127976 0.23593505 0.45948781 0.58428436 0.57560058] mean value: 0.5216530296075821 key: train_mcc value: [0.68944668 0.68433541 0.6853504 0.70393272 0.69963015 0.70853552 0.71355137 0.6944692 0.68104277 0.7062955 ] mean value: 0.6966589720547952 key: test_fscore value: [0.73469388 0.79166667 0.83018868 0.8 0.76595745 0.80769231 0.625 0.74509804 0.8 0.77272727] mean value: 0.7673024289906747 key: train_fscore value: [0.84792627 0.84454756 0.84526559 0.85382831 0.85253456 0.85581395 0.85977011 0.84988453 0.84474886 0.85842697] mean value: 0.8512746708206658 key: test_precision value: [0.72 0.79166667 0.75862069 0.76923077 0.7826087 0.75 0.6 0.67857143 0.74074074 0.80952381] mean value: 0.740096280004076 key: train_precision value: [0.82882883 0.83105023 0.8280543 0.84018265 0.83333333 0.8440367 0.84234234 0.83636364 0.82222222 0.82327586] mean value: 0.8329690097761897 key: test_recall value: [0.75 0.79166667 0.91666667 0.83333333 0.75 0.875 0.65217391 0.82608696 0.86956522 0.73913043] mean value: 0.8003623188405797 key: train_recall value: [0.86792453 0.85849057 0.86320755 0.86792453 0.87264151 0.86792453 0.87793427 0.86384977 0.8685446 0.89671362] mean value: 0.8705155461068296 key: test_accuracy value: [0.72916667 0.79166667 0.80851064 0.78723404 0.76595745 0.78723404 0.61702128 0.72340426 0.78723404 0.78723404] mean value: 0.7584663120567378 key: train_accuracy value: [0.84433962 0.84198113 0.84235294 0.85176471 0.84941176 0.85411765 0.85647059 0.84705882 0.84 0.85176471] mean value: 0.8479261931187569 key: test_roc_auc value: [0.72916667 0.79166667 0.80615942 0.78623188 0.76630435 0.78532609 0.61775362 0.72554348 0.78894928 0.78623188] mean value: 0.7583333333333332 key: train_roc_auc value: [0.84433962 0.84198113 0.8424019 0.85180264 0.84946629 0.85415006 0.85641997 0.84701922 0.83993268 0.85165869] mean value: 0.8479172203029497 key: test_jcc value: [0.58064516 0.65517241 0.70967742 0.66666667 0.62068966 0.67741935 0.45454545 0.59375 0.66666667 0.62962963] mean value: 0.6254862421957805 key: train_jcc value: [0.736 0.73092369 0.732 0.74493927 0.74297189 0.74796748 0.75403226 0.73895582 0.7312253 0.7519685 ] mean value: 0.7410984214996559 key: TN value: 169 mean value: 169.0 key: FP value: 47 mean value: 47.0 key: FN value: 67 mean value: 67.0 key: TP value: 189 mean value: 189.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.08 Accuracy on Blind test: 0.64 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.02503657 0.01970744 0.01938605 0.01980352 0.02213597 0.01945448 0.01934409 0.0216291 0.01975822 0.01986694] mean value: 0.020612239837646484 key: score_time /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( value: [0.01221991 0.01162744 0.01127386 0.01149464 0.01130176 0.01127148 0.01140594 0.01146197 0.01157188 0.0117116 ] mean value: 0.01153404712677002 key: test_mcc value: [0.79235477 0.79235477 0.8729597 0.78804348 0.84254172 0.7085716 0.61706091 0.70289855 0.79308818 0.66801039] mean value: 0.7577884061466912 key: train_mcc value: [0.86367849 0.8823209 0.84973976 0.8594196 0.88721041 0.85456939 0.87906504 0.84585771 0.86886207 0.87768255] mean value: 0.8668405904224079 key: test_fscore value: [0.89795918 0.89361702 0.93877551 0.89361702 0.90909091 0.84444444 0.8 0.85106383 0.88372093 0.80952381] mean value: 0.8721812659509698 key: train_fscore value: [0.93045564 0.94033413 0.92344498 0.92788462 0.94285714 0.92565947 0.93719807 0.92048193 0.93301435 0.93925234] mean value: 0.9320582656969542 key: test_precision value: [0.88 0.91304348 0.92 0.91304348 1. 0.9047619 0.81818182 0.83333333 0.95 0.89473684] mean value: 0.902710085490406 key: train_precision value: [0.94634146 0.95169082 0.9368932 0.94607843 0.95192308 0.94146341 0.96517413 0.94554455 0.95121951 0.93488372] mean value: 0.9471212328417973 key: test_recall value: [0.91666667 0.875 0.95833333 0.875 0.83333333 0.79166667 0.7826087 0.86956522 0.82608696 0.73913043] mean value: 0.8467391304347827 key: train_recall value: [0.91509434 0.92924528 0.91037736 0.91037736 0.93396226 0.91037736 0.91079812 0.89671362 0.91549296 0.94366197] mean value: 0.9176100628930817 key: test_accuracy value: [0.89583333 0.89583333 0.93617021 0.89361702 0.91489362 0.85106383 0.80851064 0.85106383 0.89361702 0.82978723] mean value: 0.8770390070921985 key: train_accuracy value: [0.93160377 0.94103774 0.92470588 0.92941176 0.94352941 0.92705882 0.93882353 0.92235294 0.93411765 0.93882353] mean value: 0.9331465038845728 key: test_roc_auc value: [0.89583333 0.89583333 0.93568841 0.89402174 0.91666667 0.85235507 0.80797101 0.85144928 0.89221014 0.82789855] mean value: 0.8769927536231885 key: train_roc_auc value: [0.93160377 0.94103774 0.92467225 0.92936708 0.94350695 0.92701967 0.93888963 0.92241341 0.93416157 0.93881212] mean value: 0.9331484188147755 key: test_jcc value: [0.81481481 0.80769231 0.88461538 0.80769231 0.83333333 0.73076923 0.66666667 0.74074074 0.79166667 0.68 ] mean value: 0.7757991452991453 key: train_jcc value: [0.86995516 0.88738739 0.85777778 0.86547085 0.89189189 0.86160714 0.88181818 0.85267857 0.87443946 0.88546256] mean value: 0.8728488979079051 key: TN value: 214 mean value: 214.0 key: FP value: 36 mean value: 36.0 key: FN value: 22 mean value: 22.0 key: TP value: 200 mean value: 200.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.43 Accuracy on Blind test: 0.81 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.92997265 1.80154014 1.83383083 0.84829307 0.87547112 1.53885913 1.83645678 1.82473302 1.93474245 1.84138298] mean value: 1.6265282154083252 key: score_time value: [0.01523018 0.01491117 0.01497197 0.01244283 0.01243138 0.01260138 0.01497459 0.01503444 0.01501369 0.01504254] mean value: 0.01426541805267334 key: test_mcc value: [0.91986621 1. 0.87917396 0.82971014 0.8729597 0.95825929 0.8729597 0.87318841 0.87318841 0.8047833 ] mean value: 0.8884089115461405 key: train_mcc value: [0.99061012 0.99529409 0.99530516 0.93023921 0.95791435 0.99530516 1. 1. 0.99530506 1. ] mean value: 0.985997315463707 key: test_fscore value: [0.96 1. 0.94117647 0.91666667 0.93877551 0.97959184 0.93333333 0.93617021 0.93617021 0.87804878] mean value: 0.9419933023546732 key: train_fscore value: [0.99530516 0.99764706 0.99764706 0.96385542 0.97902098 0.99764706 1. 1. 0.99765808 1. ] mean value: 0.9928780821122857 key: test_precision value: [0.92307692 1. 0.88888889 0.91666667 0.92 0.96 0.95454545 0.91666667 0.91666667 1. ] mean value: 0.9396511266511267 key: train_precision value: [0.99065421 0.99530516 0.99530516 0.98522167 0.96774194 0.99530516 1. 1. 0.9953271 1. ] mean value: 0.992486041172968 key: test_recall value: [1. 1. 1. 0.91666667 0.95833333 1. 0.91304348 0.95652174 0.95652174 0.7826087 ] mean value: 0.9483695652173914 key: train_recall value: [1. 1. 1. 0.94339623 0.99056604 1. 1. 1. 1. 1. ] mean value: 0.9933962264150944 key: test_accuracy value: [0.95833333 1. 0.93617021 0.91489362 0.93617021 0.9787234 0.93617021 0.93617021 0.93617021 0.89361702] mean value: 0.9426418439716313 key: train_accuracy value: [0.99528302 0.99764151 0.99764706 0.96470588 0.97882353 0.99764706 1. 1. 0.99764706 1. ] mean value: 0.9929395116537181 key: test_roc_auc value: [0.95833333 1. 0.93478261 0.91485507 0.93568841 0.97826087 0.93568841 0.9365942 0.9365942 0.89130435] mean value: 0.9422101449275363 key: train_roc_auc value: [0.99528302 0.99764151 0.99765258 0.96465586 0.97885109 0.99765258 1. 1. 0.99764151 1. ] mean value: 0.9929378155726812 key: test_jcc value: [0.92307692 1. 0.88888889 0.84615385 0.88461538 0.96 0.875 0.88 0.88 0.7826087 ] mean value: 0.8920343738387218 key: train_jcc value: [0.99065421 0.99530516 0.99530516 0.93023256 0.95890411 0.99530516 1. 1. 0.9953271 1. ] mean value: 0.9861033469097537 key: TN value: 221 mean value: 221.0 key: FP value: 12 mean value: 12.0 key: FN value: 15 mean value: 15.0 key: TP value: 224 mean value: 224.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.73 Accuracy on Blind test: 0.91 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.03088903 0.02440882 0.02251101 0.0216732 0.02350116 0.01992917 0.02361035 0.02364278 0.02026367 0.02351403] mean value: 0.023394322395324706 key: score_time value: [0.01192951 0.00916386 0.00897527 0.00892878 0.00879836 0.00887275 0.00888252 0.00896573 0.00882912 0.00882077] mean value: 0.009216666221618652 key: test_mcc value: [1. 0.9591663 0.91833182 0.91485507 0.95833333 0.91804649 0.95833333 1. 0.91833182 0.91804649] mean value: 0.946344467300326 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.97959184 0.95652174 0.95833333 0.9787234 0.96 0.9787234 1. 0.95833333 0.95454545] mean value: 0.9724772505587888 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.96 1. 0.95833333 1. 0.92307692 0.95833333 1. 0.92 1. ] mean value: 0.971974358974359 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.91666667 0.95833333 0.95833333 1. 1. 1. 1. 0.91304348] mean value: 0.9746376811594203 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.97916667 0.95744681 0.95744681 0.9787234 0.95744681 0.9787234 1. 0.95744681 0.95744681] mean value: 0.9723847517730497 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.97916667 0.95833333 0.95742754 0.97916667 0.95652174 0.97916667 1. 0.95833333 0.95652174] mean value: 0.9724637681159422 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.96 0.91666667 0.92 0.95833333 0.92307692 0.95833333 1. 0.92 0.91304348] mean value: 0.9469453734671125 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 229 mean value: 229.0 key: FP value: 6 mean value: 6.0 key: FN value: 7 mean value: 7.0 key: TP value: 230 mean value: 230.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.78 Accuracy on Blind test: 0.93 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.11807203 0.11743093 0.11652422 0.11758637 0.11543202 0.115242 0.11911607 0.11675596 0.1182425 0.11732697] mean value: 0.11717290878295898 key: score_time value: [0.01863527 0.01757407 0.01749945 0.0174973 0.01749563 0.01756263 0.01906514 0.01817083 0.01755691 0.01753497] mean value: 0.017859220504760742 key: test_mcc value: [0.87576054 0.91666667 0.8729597 0.87917396 0.87979456 0.91485507 0.74682354 0.84254172 0.87318841 0.87318841] mean value: 0.8674952558647572 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.93877551 0.95833333 0.93877551 0.94117647 0.93333333 0.95833333 0.86363636 0.92 0.93617021 0.93617021] mean value: 0.9324704280164676 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.92 0.95833333 0.92 0.88888889 1. 0.95833333 0.9047619 0.85185185 0.91666667 0.91666667] mean value: 0.9235502645502646 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95833333 0.95833333 0.95833333 1. 0.875 0.95833333 0.82608696 1. 0.95652174 0.95652174] mean value: 0.9447463768115943 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9375 0.95833333 0.93617021 0.93617021 0.93617021 0.95744681 0.87234043 0.91489362 0.93617021 0.93617021] mean value: 0.932136524822695 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9375 0.95833333 0.93568841 0.93478261 0.9375 0.95742754 0.87137681 0.91666667 0.9365942 0.9365942 ] mean value: 0.9322463768115942 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.88461538 0.92 0.88461538 0.88888889 0.875 0.92 0.76 0.85185185 0.88 0.88 ] mean value: 0.874497150997151 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 217 mean value: 217.0 key: FP value: 13 mean value: 13.0 key: FN value: 19 mean value: 19.0 key: TP value: 223 mean value: 223.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.49 Accuracy on Blind test: 0.84 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00970769 0.0100112 0.01110268 0.01015735 0.00985336 0.01009297 0.00997114 0.01000333 0.00984669 0.0098896 ] mean value: 0.010063600540161134 key:/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( score_time value: [0.00882339 0.00872707 0.00935602 0.008955 0.00875282 0.00874496 0.00857615 0.00883889 0.00879312 0.00873065] mean value: 0.008829808235168457 key: test_mcc value: [0.55381862 0.62554324 0.66801039 0.53176131 0.50321854 0.57427536 0.48913043 0.45173716 0.23994123 0.70289855] mean value: 0.534033484076412 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.74418605 0.80851064 0.84615385 0.7755102 0.77777778 0.79166667 0.73913043 0.73469388 0.64 0.85106383] mean value: 0.7708693321610286 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.84210526 0.82608696 0.78571429 0.76 0.7 0.79166667 0.73913043 0.69230769 0.59259259 0.83333333] mean value: 0.7562937225076813 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.79166667 0.91666667 0.79166667 0.875 0.79166667 0.73913043 0.7826087 0.69565217 0.86956522] mean value: 0.7920289855072463 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.77083333 0.8125 0.82978723 0.76595745 0.74468085 0.78723404 0.74468085 0.72340426 0.61702128 0.85106383] mean value: 0.7647163120567376 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.77083333 0.8125 0.82789855 0.76539855 0.74184783 0.78713768 0.74456522 0.72463768 0.61865942 0.85144928] mean value: 0.7644927536231885 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.59259259 0.67857143 0.73333333 0.63333333 0.63636364 0.65517241 0.5862069 0.58064516 0.47058824 0.74074074] mean value: 0.6307547771864332 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 174 mean value: 174.0 key: FP value: 49 mean value: 49.0 key: FN value: 62 mean value: 62.0 key: TP value: 187 mean value: 187.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.2 Accuracy on Blind test: 0.69 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.85506344 1.83201098 1.86094165 1.80816388 1.83163047 1.80685401 1.81309462 1.77139688 1.80277801 1.81867528] mean value: 1.820060920715332 key: score_time value: [0.09151292 0.09343147 0.09155345 0.09221554 0.09903717 0.09667325 0.08981729 0.08958125 0.09671235 0.09113455] mean value: 0.09316692352294922 key: test_mcc value: [1. 0.9591663 0.95833333 0.95825929 1. 0.95825929 0.8729597 0.95833333 0.87318841 0.91804649] mean value: 0.9456546149002671 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.9787234 0.9787234 0.97959184 1. 0.97959184 0.93333333 0.9787234 0.93617021 0.95454545] mean value: 0.971940288688009 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 0.96 1. 0.96 0.95454545 0.95833333 0.91666667 1. ] mean value: 0.9749545454545455 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.95833333 0.95833333 1. 1. 1. 0.91304348 1. 0.95652174 0.91304348] mean value: 0.9699275362318842 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.97916667 0.9787234 0.9787234 1. 0.9787234 0.93617021 0.9787234 0.93617021 0.95744681] mean value: 0.9723847517730497 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.97916667 0.97916667 0.97826087 1. 0.97826087 0.93568841 0.97916667 0.9365942 0.95652174] mean value: 0.9722826086956522 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.95833333 0.95833333 0.96 1. 0.96 0.875 0.95833333 0.88 0.91304348] mean value: 0.946304347826087 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 230 mean value: 230.0 key: FP value: 7 mean value: 7.0 key: FN value: 6 mean value: 6.0 key: TP value: 229 mean value: 229.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.71 Accuracy on Blind test: 0.91 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.9310863 0.99813843 0.94916224 0.92142129 0.94790554 0.9610889 0.94120288 0.96842337 0.9388566 1.01775265] mean value: 0.9575038194656372 key: score_time value: [0.20758581 0.20433855 0.20573854 0.19960141 0.2133472 0.18238068 0.21888518 0.21074367 0.20781922 0.20387912] mean value: 0.20543193817138672 key: test_mcc value: [0.9591663 1. 0.95833333 0.91485507 1. 0.95825929 0.8729597 0.91833182 0.87318841 0.84147165] mean value: 0.929656557184863 key: train_mcc value: [0.97651287 0.98117574 0.98589335 0.97656856 0.98122024 0.99063227 0.98121941 0.97656701 0.98589304 0.98134942] mean value: 0.9817031903143704 key: test_fscore value: [0.97959184 1. 0.9787234 0.95833333 1. 0.97959184 0.93333333 0.95833333 0.93617021 0.9047619 ] mean value: 0.9628839195252569 key: train_fscore value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.9882904 0.99061033 0.99294118 0.9882904 0.99061033 0.99530516 0.99065421 0.98834499 0.99297424 0.99069767] mean value: 0.9908718901566708 key: test_precision value: [0.96 1. 1. 0.95833333 1. 0.96 0.95454545 0.92 0.91666667 1. ] mean value: 0.9669545454545455 key: train_precision value: [0.98139535 0.98598131 0.99061033 0.98139535 0.98598131 0.99065421 0.98604651 0.98148148 0.99065421 0.98156682] mean value: 0.9855766867736186 key: test_recall value: [1. 1. 0.95833333 0.95833333 1. 1. 0.91304348 1. 0.95652174 0.82608696] mean value: 0.9612318840579711 key: train_recall value: [0.99528302 0.99528302 0.99528302 0.99528302 0.99528302 1. 0.99530516 0.99530516 0.99530516 1. ] mean value: 0.9962330587297368 key: test_accuracy value: [0.97916667 1. 0.9787234 0.95744681 1. 0.9787234 0.93617021 0.95744681 0.93617021 0.91489362] mean value: 0.9638741134751774 key: train_accuracy value: [0.98820755 0.99056604 0.99294118 0.98823529 0.99058824 0.99529412 0.99058824 0.98823529 0.99294118 0.99058824] mean value: 0.9908185349611542 key: test_roc_auc value: [0.97916667 1. 0.97916667 0.95742754 1. 0.97826087 0.93568841 0.95833333 0.9365942 0.91304348] mean value: 0.9637681159420289 key: train_roc_auc value: [0.98820755 0.99056604 0.99294667 0.98825184 0.99059926 0.99530516 0.99057711 0.98821862 0.9929356 0.99056604] mean value: 0.9908173886083798 key: test_jcc value: [0.96 1. 0.95833333 0.92 1. 0.96 0.875 0.92 0.88 0.82608696] mean value: 0.9299420289855073 key: train_jcc value: [0.97685185 0.98139535 0.98598131 0.97685185 0.98139535 0.99065421 0.98148148 0.97695853 0.98604651 0.98156682] mean value: 0.9819183254128323 key: TN value: 228 mean value: 228.0 key: FP value: 9 mean value: 9.0 key: FN value: 8 mean value: 8.0 key: TP value: 227 mean value: 227.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.68 Accuracy on Blind test: 0.9 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.08386326 0.06949043 0.07181358 0.06469345 0.0743928 0.06188083 0.06902647 0.23564076 0.05683398 0.06208444] mean value: 0.08497200012207032 key: score_time value: [0.01136065 0.01149487 0.01105356 0.01126266 0.01093841 0.01075006 0.01065063 0.01099658 0.01124334 0.01101613] mean value: 0.011076688766479492 key: test_mcc value: [1. 1. 0.91485507 0.95825929 1. 0.91804649 1. 1. 0.91833182 0.95825929] mean value: 0.9667751969880165 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 0.95833333 0.97959184 1. 0.96 1. 1. 0.95833333 0.97777778] mean value: 0.9834036281179138 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.95833333 0.96 1. 0.92307692 1. 1. 0.92 1. ] mean value: 0.9761410256410257 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.95833333 1. 1. 1. 1. 1. 1. 0.95652174] mean value: 0.991485507246377 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 0.95744681 0.9787234 1. 0.95744681 1. 1. 0.95744681 0.9787234 ] mean value: 0.9829787234042554 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.95742754 0.97826087 1. 0.95652174 1. 1. 0.95833333 0.97826087] mean value: 0.9828804347826086 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 0.92 0.96 1. 0.92307692 1. 1. 0.92 0.95652174] mean value: 0.9679598662207359 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 230 mean value: 230.0 key: FP value: 2 mean value: 2.0 key: FN value: 6 mean value: 6.0 key: TP value: 234 mean value: 234.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.85 Accuracy on Blind test: 0.95 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.0377264 0.06563306 0.05789685 0.07437158 0.06598306 0.07598257 0.0666585 0.07467961 0.06921768 0.06565714] mean value: 0.0653806447982788 key: score_time value: [0.02289009 0.0222764 0.02233481 0.02446914 0.02388811 0.02250719 0.01784444 0.01587081 0.0215292 0.01247811] mean value: 0.02060883045196533 key: test_mcc value: [0.9591663 0.9591663 0.95825929 0.84147165 0.91485507 0.91804649 1. 0.84254172 0.87979456 0.87979456] mean value: 0.915309594361706 key: train_mcc value: [0.97187112 0.98130676 0.98135106 0.985981 0.97674215 0.985981 0.98134942 0.98134942 0.99063185 0.9767396 ] mean value: 0.9813303399182998 key: test_fscore value: [0.97959184 0.97959184 0.97959184 0.92307692 0.95833333 0.96 1. 0.92 0.93877551 0.93877551] mean value: 0.95777367870225 key: train_fscore value: [0.98598131 0.99065421 0.99065421 0.99297424 0.98834499 0.99297424 0.99069767 0.99069767 0.9953271 0.98839907] mean value: 0.9906704709289615 key: test_precision value: [0.96 0.96 0.96 0.85714286 0.95833333 0.92307692 1. 0.85185185 0.88461538 0.88461538] mean value: 0.9239635734635735 key: train_precision value: [0.97685185 0.98148148 0.98148148 0.98604651 0.97695853 0.98604651 0.98156682 0.98156682 0.99069767 0.97706422] mean value: 0.9819761898571338 key: test_recall value: [1. 1. 1. 1. 0.95833333 1. 1. 1. 1. 1. ] mean value: 0.9958333333333333 key: train_recall value: [0.99528302 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9995283018867924 key: test_accuracy value: [0.97916667 0.97916667 0.9787234 0.91489362 0.95744681 0.95744681 1. 0.91489362 0.93617021 0.93617021] mean value: 0.9554078014184398 key: train_accuracy value: [0.98584906 0.99056604 0.99058824 0.99294118 0.98823529 0.99294118 0.99058824 0.99058824 0.99529412 0.98823529] mean value: 0.9905826859045506 key: test_roc_auc value: [0.97916667 0.97916667 0.97826087 0.91304348 0.95742754 0.95652174 1. 0.91666667 0.9375 0.9375 ] mean value: 0.9555253623188407 key: train_roc_auc value: [0.98584906 0.99056604 0.99061033 0.99295775 0.98826291 0.99295775 0.99056604 0.99056604 0.99528302 0.98820755] mean value: 0.9905826468243422 key: test_jcc value: [0.96 0.96 0.96 0.85714286 0.92 0.92307692 1. 0.85185185 0.88461538 0.88461538] mean value: 0.9201302401302403 key: train_jcc value: [0.97235023 0.98148148 0.98148148 0.98604651 0.97695853 0.98604651 0.98156682 0.98156682 0.99069767 0.97706422] mean value: 0.9815260277134232 key: TN value: 216 mean value: 216.0 key: FP value: 1 mean value: 1.0 key: FN value: 20 mean value: 20.0 key: TP value: 235 mean value: 235.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.65 Accuracy on Blind test: 0.88 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.02088475 0.00992966 0.00963473 0.01010895 0.00942349 0.00943804 0.00965643 0.00975943 0.00966811 0.00966311] mean value: 0.010816669464111328 key: score_time value: [0.01168203 0.00901556 0.00874496 0.0086658 0.0086267 0.00870442 0.00942421 0.00881624 0.00882101 0.00985885] mean value: 0.009235978126525879 key: test_mcc value: [0.58333333 0.46861576 0.44874504 0.4899891 0.19493499 0.36699609 0.27717391 0.19202899 0.19490273 0.32605546] mean value: 0.35427753956239433 key: train_mcc value: [0.44436622 0.43444585 0.44575704 0.37891473 0.32915125 0.33634235 0.42648362 0.40951959 0.35491596 0.38647587] mean value: 0.3946372484323964 key: test_fscore value: [0.79166667 0.75471698 0.74509804 0.76 0.65454545 0.71698113 0.63829787 0.59574468 0.6122449 0.6 ] mean value: 0.6869295724786029 key: train_fscore value: [0.73059361 0.7235023 0.73059361 0.69158879 0.69098712 0.68995633 0.72146119 0.72 0.7 0.71081678] mean value: 0.7109499724403869 key: test_precision value: [0.79166667 0.68965517 0.7037037 0.73076923 0.58064516 0.65517241 0.625 0.58333333 0.57692308 0.70588235] mean value: 0.6642751111834407 key: train_precision value: [0.7079646 0.70720721 0.7079646 0.68518519 0.63385827 0.64227642 0.70222222 0.6835443 0.65182186 0.67083333] mean value: 0.679287800811418 key: test_recall value: [0.79166667 0.83333333 0.79166667 0.79166667 0.75 0.79166667 0.65217391 0.60869565 0.65217391 0.52173913] mean value: 0.7184782608695651 key: train_recall value: [0.75471698 0.74056604 0.75471698 0.69811321 0.75943396 0.74528302 0.74178404 0.76056338 0.75586854 0.75586854] mean value: 0.7466914695721499 key: test_accuracy value: [0.79166667 0.72916667 0.72340426 0.74468085 0.59574468 0.68085106 0.63829787 0.59574468 0.59574468 0.65957447] mean value: 0.6754875886524822 key: train_accuracy value: [0.72169811 0.71698113 0.72235294 0.68941176 0.66117647 0.66588235 0.71294118 0.70352941 0.67529412 0.69176471] mean value: 0.6961032186459489 key: test_roc_auc value: [0.79166667 0.72916667 0.72192029 0.74365942 0.5923913 0.67844203 0.63858696 0.59601449 0.59692029 0.6567029 ] mean value: 0.6745471014492754 key: train_roc_auc value: [0.72169811 0.71698113 0.72242891 0.68943219 0.66140712 0.66606874 0.71287315 0.7033949 0.67510408 0.69161352] mean value: 0.696100186021791 key: test_jcc value: [0.65517241 0.60606061 0.59375 0.61290323 0.48648649 0.55882353 0.46875 0.42424242 0.44117647 0.42857143] mean value: 0.5275936584960501 key: train_jcc value: [0.57553957 0.566787 0.57553957 0.52857143 0.52786885 0.52666667 0.56428571 0.5625 0.53846154 0.55136986] mean value: 0.5517590203758819 key: TN value: 149 mean value: 149.0 key: FP value: 66 mean value: 66.0 key: FN value: 87 mean value: 87.0 key: TP value: 170 mean value: 170.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.22 Accuracy on Blind test: 0.61 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01839757 0.0254066 0.02377772 0.02271795 0.02948928 0.02503753 0.02638626 0.02682137 0.02718377 0.02452397] mean value: 0.02497420310974121 key: score_time value: [0.00948644 0.01130271 0.0117383 0.01180124 0.01184773 0.01192331 0.01176596 0.01187563 0.01197004 0.01187992] mean value: 0.011559128761291504 key: test_mcc value: [0.9591663 0.91986621 0.95825929 0.91804649 0.95825929 0.95825929 0.95833333 0.91833182 0.40290954 0.91804649] mean value: 0.8869478067076157 key: train_mcc value: [0.98594778 0.97668677 0.98135106 0.97674215 0.985981 0.99063227 0.98598008 0.98598008 0.51345631 0.98134942] mean value: 0.9364106910161993 key: test_fscore value: [0.97959184 0.96 0.97959184 0.96 0.97959184 0.97959184 0.9787234 0.95833333 0.57142857 0.95454545] mean value: 0.9301398110501454 key: train_fscore value: [0.99297424 0.98834499 0.99065421 0.98834499 0.99297424 0.99530516 0.99300699 0.99300699 0.58940397 0.99069767] mean value: 0.9514713458310984 key: test_precision value: [0.96 0.92307692 0.96 0.92307692 0.96 0.96 0.95833333 0.92 0.83333333 1. ] mean value: 0.9397820512820513 key: train_precision value: [0.98604651 0.97695853 0.98148148 0.97695853 0.98604651 0.99065421 0.98611111 0.98611111 1. 0.98156682] mean value: 0.9851934803534738 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 0.43478261 0.91304348] mean value: 0.9347826086956521 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 0.41784038 1. ] mean value: 0.9417840375586854 key: test_accuracy value: [0.97916667 0.95833333 0.9787234 0.95744681 0.9787234 0.9787234 0.9787234 0.95744681 0.68085106 0.95744681] mean value: 0.9405585106382979 key: train_accuracy value: [0.99292453 0.98820755 0.99058824 0.98823529 0.99294118 0.99529412 0.99294118 0.99294118 0.70823529 0.99058824] mean value: 0.9632896781354052 key: test_roc_auc value: [0.97916667 0.95833333 0.97826087 0.95652174 0.97826087 0.97826087 0.97916667 0.95833333 0.67572464 0.95652174] mean value: 0.9398550724637682 key: train_roc_auc value: [0.99292453 0.98820755 0.99061033 0.98826291 0.99295775 0.99530516 0.99292453 0.99292453 0.70892019 0.99056604] mean value: 0.963360350783949 key: test_jcc value: [0.96 0.92307692 0.96 0.92307692 0.96 0.96 0.95833333 0.92 0.4 0.91304348] mean value: 0.887753065774805 key: train_jcc value: [0.98604651 0.97695853 0.98148148 0.97695853 0.98604651 0.99065421 0.98611111 0.98611111 0.41784038 0.98156682] mean value: 0.9269775179121591 key: TN value: 223 mean value: 223.0 key: FP value: 15 mean value: 15.0 key: FN value: 13 mean value: 13.0 key: TP value: 221 mean value: 221.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.85 Accuracy on Blind test: 0.95 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01678896 0.01649952 0.01591492 0.01822972 0.01876831 0.01699924 0.01787925 0.01754594 0.01638031 0.0158534 ] mean value: 0.017085957527160644 key: score_time value: [0.01152825 0.01157141 0.01164985 0.01152349 0.01157451 0.0115478 0.01151896 0.01157546 0.01188564 0.01181984] mean value: 0.01161952018737793 key: test_mcc value: [0.91666667 0.91986621 0.91485507 0.91804649 0.95825929 0.91485507 0.91485507 0.64404991 0.83303222 0.5646597 ] mean value: 0.8499145717462007 key: train_mcc value: [0.95389198 0.95389198 0.94849477 0.98135106 0.98135106 0.95790971 0.96706971 0.81557001 0.92474809 0.47960727] mean value: 0.896388565223219 key: test_fscore value: [0.95833333 0.96 0.95833333 0.96 0.97959184 0.95833333 0.95652174 0.76923077 0.91666667 0.64705882] mean value: 0.9064069835291976 key: train_fscore value: [0.97695853 0.97695853 0.97374702 0.99065421 0.99065421 0.97852029 0.98352941 0.89175258 0.96226415 0.55405405] mean value: 0.9279092959090567 key: test_precision value: [0.95833333 0.92307692 0.95833333 0.92307692 0.96 0.95833333 0.95652174 0.9375 0.88 1. ] mean value: 0.9455175585284282 key: train_precision value: [0.95495495 0.95495495 0.98550725 0.98148148 0.98148148 0.99033816 0.98584906 0.98857143 0.96682464 0.98795181] mean value: 0.9777915220454773 key: test_recall value: [0.95833333 1. 0.95833333 1. 1. 0.95833333 0.95652174 0.65217391 0.95652174 0.47826087] mean value: 0.8918478260869567 key: train_recall value: [1. 1. 0.96226415 1. 1. 0.96698113 0.98122066 0.81220657 0.95774648 0.38497653] mean value: 0.9065395517760653 key: test_accuracy value: [0.95833333 0.95833333 0.95744681 0.95744681 0.9787234 0.95744681 0.95744681 0.80851064 0.91489362 0.74468085] mean value: 0.9193262411347517 key: train_accuracy value: [0.97641509 0.97641509 0.97411765 0.99058824 0.99058824 0.97882353 0.98352941 0.90117647 0.96235294 0.68941176] mean value: 0.9423418423973363 key: test_roc_auc value: [0.95833333 0.95833333 0.95742754 0.95652174 0.97826087 0.95742754 0.95742754 0.80525362 0.91576087 0.73913043] mean value: 0.9183876811594203 key: train_roc_auc value: [0.97641509 0.97641509 0.97408982 0.99061033 0.99061033 0.97879573 0.98353486 0.90138631 0.96236381 0.69012977] mean value: 0.9424351138276197 key: test_jcc value: [0.92 0.92307692 0.92 0.92307692 0.96 0.92 0.91666667 0.625 0.84615385 0.47826087] mean value: 0.8432235228539577 key: train_jcc value: [0.95495495 0.95495495 0.94883721 0.98148148 0.98148148 0.95794393 0.96759259 0.80465116 0.92727273 0.38317757] mean value: 0.886234806015832 key: TN value: 223 mean value: 223.0 key: FP value: 25 mean value: 25.0 key: FN value: 13 mean value: 13.0 key: TP value: 211 mean value: 211.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.6 Accuracy on Blind test: 0.88 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.22470832 0.20434523 0.20656443 0.20643067 0.20798802 0.20693946 0.21186805 0.20994353 0.21088386 0.21096039] mean value: 0.21006319522857667 key: score_time value: [0.01509857 0.01499677 0.01507831 0.01502872 0.01508951 0.01544309 0.01509452 0.01587653 0.01597238 0.01538754] mean value: 0.015306591987609863 key: test_mcc value: [1. 0.9591663 0.95833333 0.87318841 1. 0.91804649 0.95833333 1. 0.91833182 0.95825929] mean value: 0.954365898313708 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.9787234 0.9787234 0.93617021 1. 0.96 0.9787234 1. 0.95833333 0.97777778] mean value: 0.9768451536643026 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 0.95652174 1. 0.92307692 0.95833333 1. 0.92 1. ] mean value: 0.9757931995540691 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.95833333 0.95833333 0.91666667 1. 1. 1. 1. 1. 0.95652174] mean value: 0.9789855072463769 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.97916667 0.9787234 0.93617021 1. 0.95744681 0.9787234 1. 0.95744681 0.9787234 ] mean value: 0.9766400709219859 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.97916667 0.97916667 0.9365942 1. 0.95652174 0.97916667 1. 0.95833333 0.97826087] mean value: 0.9767210144927535 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.95833333 0.95833333 0.88 1. 0.92307692 0.95833333 1. 0.92 0.95652174] mean value: 0.955459866220736 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 230 mean value: 230.0 key: FP value: 5 mean value: 5.0 key: FN value: 6 mean value: 6.0 key: TP value: 231 mean value: 231.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.85 Accuracy on Blind test: 0.95 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.04095602 0.05502033 0.05681586 0.06088853 0.05758262 0.05486798 0.07888913 0.07030559 0.06846881 0.05174327] mean value: 0.05955381393432617 key: score_time value: [0.01834273 0.01944971 0.02726221 0.01841426 0.01991796 0.02375793 0.03805304 0.02091193 0.02872539 0.02132058] mean value: 0.023615574836730956 key: test_mcc value: [1. 1. 0.87979456 0.91485507 0.95833333 0.91804649 0.95833333 0.95825929 0.87318841 0.91804649] mean value: 0.9378856980120954 key: train_mcc value: [0.99529409 0.99061012 0.99530506 1. 1. 1. 0.99530506 0.99530516 0.99530506 0.99530506] mean value: 0.9962429618909043 key: test_fscore value: [1. 1. 0.93333333 0.95833333 0.9787234 0.96 0.9787234 0.97777778 0.93617021 0.95454545] mean value: 0.9677606920266495 key: train_fscore value: [0.99764706 0.99530516 0.99763593 1. 1. 1. 0.99765808 0.99764706 0.99765808 0.99765808] mean value: 0.9981209454648333 key: test_precision value: [1. 1. 1. 0.95833333 1. 0.92307692 0.95833333 1. 0.91666667 1. ] mean value: 0.9756410256410255 key: train_precision value: [0.99530516 0.99065421 1. 1. 1. 1. 0.9953271 1. 0.9953271 0.9953271 ] mean value: 0.997194067833794 key: test_recall value: [1. 1. 0.875 0.95833333 0.95833333 1. 1. 0.95652174 0.95652174 0.91304348] mean value: 0.9617753623188406 key: train_recall value: [1. 1. 0.99528302 1. 1. 1. 1. 0.99530516 1. 1. ] mean value: 0.9990588183187175 key: test_accuracy value: [1. 1. 0.93617021 0.95744681 0.9787234 0.95744681 0.9787234 0.9787234 0.93617021 0.95744681] mean value: 0.9680851063829788 key: train_accuracy value: [0.99764151 0.99528302 0.99764706 1. 1. 1. 0.99764706 0.99764706 0.99764706 0.99764706] mean value: 0.9981159822419533 key: test_roc_auc value: [1. 1. 0.9375 0.95742754 0.97916667 0.95652174 0.97916667 0.97826087 0.9365942 0.95652174] mean value: 0.9681159420289855 key: train_roc_auc value: [0.99764151 0.99528302 0.99764151 1. 1. 1. 0.99764151 0.99765258 0.99764151 0.99764151] mean value: 0.998114314819736 key: test_jcc value: [1. 1. 0.875 0.92 0.95833333 0.92307692 0.95833333 0.95652174 0.88 0.91304348] mean value: 0.9384308807134895 key: train_jcc value: [0.99530516 0.99065421 0.99528302 1. 1. 1. 0.9953271 0.99530516 0.9953271 0.9953271 ] mean value: 0.9962528861525113 key: TN value: 230 mean value: 230.0 key: FP value: 9 mean value: 9.0 key: FN value: 6 mean value: 6.0 key: TP value: 227 mean value: 227.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.85 Accuracy on Blind test: 0.95 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.12498903 0.13653755 0.14616394 0.13924384 0.14144564 0.10727668 0.14344168 0.10445166 0.16795921 0.14799881] mean value: 0.13595080375671387 key: score_time value: [0.02295804 0.02838063 0.02303791 0.02607822 0.02326965 0.01507163 0.02788782 0.01454759 0.02842116 0.01466465] mean value: 0.02243173122406006 key: test_mcc value: [0.54594868 0.71393289 0.66801039 0.75474102 0.57427536 0.65942029 0.36612568 0.59613578 0.74682354 0.61706091] mean value: 0.6242474541008988 key: train_mcc value: [0.9435642 0.94377428 0.92942088 0.94356964 0.95294092 0.93883291 0.93907982 0.9435291 0.95310772 0.92950167] mean value: 0.9417321140302078 key: test_fscore value: [0.78431373 0.8627451 0.84615385 0.88461538 0.79166667 0.83333333 0.69387755 0.80769231 0.86363636 0.8 ] mean value: 0.8168034276647722 key: train_fscore value: [0.97196262 0.97209302 0.96453901 0.97156398 0.97641509 0.96926714 0.96983759 0.97183099 0.97674419 0.96503497] mean value: 0.9709288586036555 key: test_precision value: [0.74074074 0.81481481 0.78571429 0.82142857 0.79166667 0.83333333 0.65384615 0.72413793 0.9047619 0.81818182] mean value: 0.7888626220522772 key: train_precision value: [0.96296296 0.9587156 0.96682464 0.97619048 0.97641509 0.97156398 0.9587156 0.97183099 0.96774194 0.95833333] mean value: 0.9669294606478728 key: test_recall value: [0.83333333 0.91666667 0.91666667 0.95833333 0.79166667 0.83333333 0.73913043 0.91304348 0.82608696 0.7826087 ] mean value: 0.8510869565217393 key: train_recall value: [0.98113208 0.98584906 0.96226415 0.96698113 0.97641509 0.96698113 0.98122066 0.97183099 0.98591549 0.97183099] mean value: 0.975042076357516 key: test_accuracy value: [0.77083333 0.85416667 0.82978723 0.87234043 0.78723404 0.82978723 0.68085106 0.78723404 0.87234043 0.80851064] mean value: 0.8093085106382978 key: train_accuracy value: [0.97169811 0.97169811 0.96470588 0.97176471 0.97647059 0.96941176 0.96941176 0.97176471 0.97647059 0.96470588] mean value: 0.9708102108768035 key: test_roc_auc value: [0.77083333 0.85416667 0.82789855 0.87047101 0.78713768 0.82971014 0.68206522 0.78985507 0.87137681 0.80797101] mean value: 0.8091485507246376 key: train_roc_auc value: [0.97169811 0.97169811 0.96470015 0.97175348 0.97647046 0.96940606 0.96938391 0.97176455 0.97644831 0.96468908] mean value: 0.970801222428913 key: test_jcc value: [0.64516129 0.75862069 0.73333333 0.79310345 0.65517241 0.71428571 0.53125 0.67741935 0.76 0.66666667] mean value: 0.6935012911171142 key: train_jcc value: [0.94545455 0.94570136 0.93150685 0.94470046 0.95391705 0.94036697 0.94144144 0.94520548 0.95454545 0.93243243] mean value: 0.9435272044104863 key: TN value: 181 mean value: 181.0 key: FP value: 35 mean value: 35.0 key: FN value: 55 mean value: 55.0 key: TP value: 201 mean value: 201.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.11 Accuracy on Blind test: 0.68 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.84788013 0.82336068 0.82631397 0.82613254 0.83214068 0.82518101 0.84120417 0.82660365 0.82076645 0.8304646 ] mean value: 0.8300047874450683 key: score_time value: [0.00928783 0.00998926 0.00946808 0.00917816 0.00920033 0.00939775 0.00932908 0.00917006 0.00909996 0.00968266] mean value: 0.009380316734313965 key: test_mcc value: [1. 1. 0.95825929 0.91485507 0.95833333 0.91804649 0.91833182 0.95825929 0.91833182 0.95825929] mean value: 0.9502676415549521 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 0.97959184 0.95833333 0.9787234 0.96 0.95833333 0.97777778 0.95833333 0.97777778] mean value: 0.9748870796545569 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.96 0.95833333 1. 0.92307692 0.92 1. 0.92 1. ] mean value: 0.9681410256410257 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.95833333 0.95833333 1. 1. 0.95652174 1. 0.95652174] mean value: 0.9829710144927537 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 0.9787234 0.95744681 0.9787234 0.95744681 0.95744681 0.9787234 0.95744681 0.9787234 ] mean value: 0.9744680851063829 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.97826087 0.95742754 0.97916667 0.95652174 0.95833333 0.97826087 0.95833333 0.97826087] mean value: 0.9744565217391304 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 0.96 0.92 0.95833333 0.92307692 0.92 0.95652174 0.92 0.95652174] mean value: 0.9514453734671127 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 228 mean value: 228.0 key: FP value: 4 mean value: 4.0 key: FN value: 8 mean value: 8.0 key: TP value: 232 mean value: 232.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.85 Accuracy on Blind test: 0.95 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02677035 0.02802253 0.02849007 0.02773046 0.02764273 0.02758217 0.02782226 0.04608202 0.03823519 0.04036951] mean value: 0.03187472820281982 key: score_time value: [0.01233816 0.01240921 0.01265359 0.01354814 0.01499772 0.01506662 0.01908278 0.01705933 0.01990175 0.0129528 ] mean value: 0.015001010894775391 key: test_mcc value: [0.9591663 1. 0.91804649 0.95825929 0.95825929 1. 0.91833182 0.91833182 1. 0.95825929] mean value: 0.9588654314414964 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97959184 1. 0.96 0.97959184 0.97959184 1. 0.95833333 0.95833333 1. 0.97777778] mean value: 0.9793219954648527 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.96 1. 0.92307692 0.96 0.96 1. 0.92 0.92 1. 1. ] mean value: 0.9643076923076922 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.95652174] mean value: 0.9956521739130435 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97916667 1. 0.95744681 0.9787234 0.9787234 1. 0.95744681 0.95744681 1. 0.9787234 ] mean value: 0.9787677304964539 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97916667 1. 0.95652174 0.97826087 0.97826087 1. 0.95833333 0.95833333 1. 0.97826087] mean value: 0.978713768115942 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.96 1. 0.92307692 0.96 0.96 1. 0.92 0.92 1. 0.95652174] mean value: 0.9599598662207358 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 227 mean value: 227.0 key: FP value: 1 mean value: 1.0 key: FN value: 9 mean value: 9.0 key: TP value: 235 mean value: 235.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: -0.1 Accuracy on Blind test: 0.76 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.0153389 0.01543188 0.0312953 0.03483987 0.03201866 0.03430152 0.03340054 0.03347754 0.03270888 0.03707957] mean value: 0.02998926639556885 key: score_time value: [0.01203394 0.01204205 0.02133679 0.02125907 0.02122617 0.02147245 0.01996827 0.02125692 0.01610708 0.02232218] mean value: 0.01890249252319336 key: test_mcc value: [0.9591663 0.9591663 0.91804649 0.91804649 0.95825929 0.91804649 1. 0.91833182 0.91833182 1. ] mean value: 0.9467395023615742 key: train_mcc value: [0.97668677 0.97668677 0.97674215 0.98135106 0.97674215 0.98135106 0.9767396 0.98134942 0.98598008 0.9767396 ] mean value: 0.979036867016519 key: test_fscore value: [0.97959184 0.97959184 0.96 0.96 0.97959184 0.96 1. 0.95833333 0.95833333 1. ] mean value: 0.973544217687075 key: train_fscore value: [0.98834499 0.98834499 0.98834499 0.99065421 0.98834499 0.99065421 0.98839907 0.99069767 0.99300699 0.98839907] mean value: 0.9895191175872012 key: test_precision value: [0.96 0.96 0.92307692 0.92307692 0.96 0.92307692 1. 0.92 0.92 1. ] mean value: 0.9489230769230769 key: train_precision value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97695853 0.98148148 0.97706422 0.98156682 0.98611111 0.97706422] mean value: 0.9792603436100032 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97916667 0.97916667 0.95744681 0.95744681 0.9787234 0.95744681 1. 0.95744681 0.95744681 1. ] mean value: 0.9724290780141844 key: train_accuracy value: [0.98820755 0.98820755 0.98823529 0.99058824 0.98823529 0.99058824 0.98823529 0.99058824 0.99294118 0.98823529] mean value: 0.9894062153163151 key: test_roc_auc value: [0.97916667 0.97916667 0.95652174 0.95652174 0.97826087 0.95652174 1. 0.95833333 0.95833333 1. ] mean value: 0.9722826086956522 key: train_roc_auc value: [0.98820755 0.98820755 0.98826291 0.99061033 0.98826291 0.99061033 0.98820755 0.99056604 0.99292453 0.98820755] mean value: 0.9894067233590219 key: test_jcc value: [0.96 0.96 0.92307692 0.92307692 0.96 0.92307692 1. 0.92 0.92 1. ] mean value: 0.9489230769230769 key: train_jcc value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97695853 0.98148148 0.97706422 0.98156682 0.98611111 0.97706422] mean value: 0.9792603436100032 key: TN value: 223 mean value: 223.0 key: FP value: 0 mean value: 0.0 key: FN value: 13 mean value: 13.0 key: TP value: 236 mean value: 236.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.81 Accuracy on Blind test: 0.93 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:206: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:207: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_CV['Resampling'] = rs_smnc /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:212: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:213: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_BT['Resampling'] = rs_smnc /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.26434445 0.32251453 0.15206838 0.24239111 0.28772497 0.20101905 0.18936872 0.27143574 0.1601553 0.28233242] mean value: 0.237335467338562 key: score_time value: [0.02213812 0.02163053 0.01248097 0.02126622 0.02346134 0.01217699 0.02457643 0.02345705 0.01250052 0.02009153] mean value: 0.019377970695495607 key: test_mcc value: [0.9591663 0.9591663 0.91804649 0.91804649 0.95825929 0.91804649 1. 0.91833182 0.91833182 1. ] mean value: 0.9467395023615742 key: train_mcc value: [0.97668677 0.97668677 0.97674215 0.98135106 0.97674215 0.98135106 0.9767396 0.98134942 0.98598008 0.9767396 ] mean value: 0.979036867016519 key: test_fscore value: [0.97959184 0.97959184 0.96 0.96 0.97959184 0.96 1. 0.95833333 0.95833333 1. ] mean value: 0.973544217687075 key: train_fscore value: [0.98834499 0.98834499 0.98834499 0.99065421 0.98834499 0.99065421 0.98839907 0.99069767 0.99300699 0.98839907] mean value: 0.9895191175872012 key: test_precision value: [0.96 0.96 0.92307692 0.92307692 0.96 0.92307692 1. 0.92 0.92 1. ] mean value: 0.9489230769230769 key: train_precision value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97695853 0.98148148 0.97706422 0.98156682 0.98611111 0.97706422] mean value: 0.9792603436100032 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97916667 0.97916667 0.95744681 0.95744681 0.9787234 0.95744681 1. 0.95744681 0.95744681 1. ] mean value: 0.9724290780141844 key: train_accuracy value: [0.98820755 0.98820755 0.98823529 0.99058824 0.98823529 0.99058824 0.98823529 0.99058824 0.99294118 0.98823529] mean value: 0.9894062153163151 key: test_roc_auc value: [0.97916667 0.97916667 0.95652174 0.95652174 0.97826087 0.95652174 1. 0.95833333 0.95833333 1. ] mean value: 0.9722826086956522 key: train_roc_auc value: [0.98820755 0.98820755 0.98826291 0.99061033 0.98826291 0.99061033 0.98820755 0.99056604 0.99292453 0.98820755] mean value: 0.9894067233590219 key: test_jcc value: [0.96 0.96 0.92307692 0.92307692 0.96 0.92307692 1. 0.92 0.92 1. ] mean value: 0.9489230769230769 key: train_jcc value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97695853 0.98148148 0.97706422 0.98156682 0.98611111 0.97706422] mean value: 0.9792603436100032 key: TN value: 223 mean value: 223.0 key: FP value: 0 mean value: 0.0 key: FN value: 13 mean value: 13.0 key: TP value: 236 mean value: 236.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.81 Accuracy on Blind test: 0.93 PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.04575014 0.03561878 0.03952789 0.03515625 0.03371787 0.03832698 0.03580189 0.03812385 0.03297853 0.03531456] mean value: 0.0370316743850708 key: score_time value: [0.01466537 0.0143652 0.01469421 0.01486397 0.01183844 0.01454234 0.01436806 0.01438093 0.01181459 0.01306009] mean value: 0.013859319686889648 key: test_mcc value: [0.87576054 0.87576054 0.91833182 0.87917396 0.91833182 0.78804348 0.82971014 0.91833182 0.87318841 0.82971014] mean value: 0.8706342679311371 key: train_mcc value: [0.93877324 0.93877324 0.95311186 0.9576579 0.94824493 0.92942088 0.94824493 0.95298209 0.96235273 0.94356964] mean value: 0.9473131457063468 key: test_fscore value: [0.93877551 0.93617021 0.95652174 0.94117647 0.95652174 0.89361702 0.91304348 0.95833333 0.93617021 0.91304348] mean value: 0.9343373195716769 key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( train_fscore value: [0.96955504 0.96955504 0.97663551 0.97882353 0.97399527 0.96453901 0.97423888 0.97663551 0.98122066 0.97196262] mean value: 0.9737161056644213 key: test_precision value: [0.92 0.95652174 1. 0.88888889 1. 0.91304348 0.91304348 0.92 0.91666667 0.91304348] mean value: 0.9341207729468598 key: train_precision value: [0.9627907 0.9627907 0.96759259 0.97652582 0.97630332 0.96682464 0.97196262 0.97209302 0.98122066 0.96744186] mean value: 0.9705545929443339 key: test_recall value: [0.95833333 0.91666667 0.91666667 1. 0.91666667 0.875 0.91304348 1. 0.95652174 0.91304348] mean value: 0.9365942028985508 key: train_recall value: [0.97641509 0.97641509 0.98584906 0.98113208 0.97169811 0.96226415 0.97652582 0.98122066 0.98122066 0.97652582] mean value: 0.976926654265214 key: test_accuracy value: [0.9375 0.9375 0.95744681 0.93617021 0.95744681 0.89361702 0.91489362 0.95744681 0.93617021 0.91489362] mean value: 0.9343085106382978 key: train_accuracy value: [0.96933962 0.96933962 0.97647059 0.97882353 0.97411765 0.96470588 0.97411765 0.97647059 0.98117647 0.97176471] mean value: 0.973632630410655 key: test_roc_auc value: [0.9375 0.9375 0.95833333 0.93478261 0.95833333 0.89402174 0.91485507 0.95833333 0.9365942 0.91485507] mean value: 0.9345108695652172 key: train_roc_auc value: [0.96933962 0.96933962 0.9764926 0.97882895 0.97411197 0.96470015 0.97411197 0.97645939 0.98117637 0.97175348] mean value: 0.9736314111081583 key: test_jcc value: [0.88461538 0.88 0.91666667 0.88888889 0.91666667 0.80769231 0.84 0.92 0.88 0.84 ] mean value: 0.8774529914529914 key: train_jcc value: [0.94090909 0.94090909 0.9543379 0.95852535 0.94930876 0.93150685 0.94977169 0.9543379 0.96313364 0.94545455] mean value: 0.9488194807107753 key: TN value: 220 mean value: 220.0 key: FP value: 15 mean value: 15.0 key: FN value: 16 mean value: 16.0 key: TP value: 221 mean value: 221.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.7 Accuracy on Blind test: 0.89 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.8614018 0.77416611 0.771734 0.95600319 0.76238513 0.77007675 0.90413976 0.76445174 0.77944541 0.86435747] mean value: 0.8208161354064941 key: score_time value: [0.01345611 0.01349068 0.01326203 0.01331139 0.01330566 0.01315808 0.01339507 0.0134182 0.01340961 0.01469398] mean value: 0.013490080833435059 key: test_mcc value: [1. 0.9591663 0.95833333 0.91804649 0.95825929 0.95825929 0.95833333 0.95833333 0.91833182 0.95833333] mean value: 0.9545396535412275 key: train_mcc value: [0.98594778 0.98594778 0.985981 1. 0.985981 1. 1. 0.98598008 0.99063185 1. ] mean value: 0.992046948762083 key: test_fscore value: [1. 0.97959184 0.9787234 0.96 0.97959184 0.97959184 0.9787234 0.9787234 0.95833333 0.9787234 ] mean value: 0.9772002460558692 key: train_fscore value: [0.99297424 0.99297424 0.99297424 1. 0.99297424 1. 1. 0.99300699 0.9953271 1. ] mean value: 0.9960231051314243 key: test_precision value: [1. 0.96 1. 0.92307692 0.96 0.96 0.95833333 0.95833333 0.92 0.95833333] mean value: 0.9598076923076924 key: train_precision value: [0.98604651 0.98604651 0.98604651 1. 0.98604651 1. 1. 0.98611111 0.99069767 1. ] mean value: 0.9920994832041344 key: test_recall value: [1. 1. 0.95833333 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9958333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.97916667 0.9787234 0.95744681 0.9787234 0.9787234 0.9787234 0.9787234 0.95744681 0.9787234 ] mean value: 0.9766400709219859 key: train_accuracy value: [0.99292453 0.99292453 0.99294118 1. 0.99294118 1. 1. 0.99294118 0.99529412 1. ] mean value: 0.9959966703662598 key: test_roc_auc value: [1. 0.97916667 0.97916667 0.95652174 0.97826087 0.97826087 0.97916667 0.97916667 0.95833333 0.97916667] mean value: 0.9767210144927537 key: train_roc_auc value: [0.99292453 0.99292453 0.99295775 1. 0.99295775 1. 1. 0.99292453 0.99528302 1. ] mean value: 0.9959972096731331 key: test_jcc value: [1. 0.96 0.95833333 0.92307692 0.96 0.96 0.95833333 0.95833333 0.92 0.95833333] mean value: 0.9556410256410258 key: train_jcc value: [0.98604651 0.98604651 0.98604651 1. 0.98604651 1. 1. 0.98611111 0.99069767 1. ] mean value: 0.9920994832041344 key: TN value: 226 mean value: 226.0 key: FP value: 1 mean value: 1.0 key: FN value: 10 mean value: 10.0 key: TP value: 235 mean value: 235.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.81 Accuracy on Blind test: 0.93 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01357484 0.01358032 0.01013279 0.00968552 0.00968051 0.00999379 0.0106163 0.0103898 0.01098871 0.01038814] mean value: 0.010903072357177735 key: score_time value: [0.01210165 0.01052999 0.00906539 0.0088408 0.00886726 0.00886559 0.00925517 0.00962162 0.0091269 0.00949335] mean value: 0.009576773643493653 key: test_mcc value: [0.62554324 0.51639778 0.66801039 0.62296012 0.79308818 0.54211097 0.4121128 0.45173716 0.48913043 0.58127976] mean value: 0.5702370834691168 key: train_mcc value: [0.66037736 0.67007974 0.68970563 0.62608853 0.6525241 0.66709242 0.69884615 0.68471521 0.64738091 0.66604642] mean value: 0.6662856464972534 key: test_fscore value: [0.80851064 0.71428571 0.84615385 0.8 0.90196078 0.79245283 0.72 0.73469388 0.73913043 0.76190476] mean value: 0.781909288747823 key: train_fscore value: [0.83018868 0.8372093 0.84651163 0.77866667 0.82949309 0.84070796 0.85046729 0.84235294 0.82678984 0.83526682] mean value: 0.8317654218882868 key: test_precision value: [0.82608696 0.83333333 0.78571429 0.85714286 0.85185185 0.72413793 0.66666667 0.69230769 0.73913043 0.84210526] mean value: 0.7818477272513412 key: train_precision value: [0.83018868 0.82568807 0.83486239 0.89570552 0.81081081 0.79166667 0.84651163 0.84433962 0.81363636 0.82568807] mean value: 0.8319097824490095 key: test_recall value: [0.79166667 0.625 0.91666667 0.75 0.95833333 0.875 0.7826087 0.7826087 0.73913043 0.69565217] mean value: 0.7916666666666666 key: train_recall value: [0.83018868 0.8490566 0.85849057 0.68867925 0.8490566 0.89622642 0.85446009 0.84037559 0.84037559 0.84507042] mean value: 0.8351979803348393 key: test_accuracy value: [0.8125 0.75 0.82978723 0.80851064 0.89361702 0.76595745 0.70212766 0.72340426 0.74468085 0.78723404] mean value: 0.7817819148936171 key: train_accuracy value: [0.83018868 0.83490566 0.84470588 0.80470588 0.82588235 0.83058824 0.84941176 0.84235294 0.82352941 0.83294118] mean value: 0.8319211986681465 key: test_roc_auc value: [0.8125 0.75 0.82789855 0.80978261 0.89221014 0.76358696 0.70380435 0.72463768 0.74456522 0.78532609] mean value: 0.7814311594202898 key: train_roc_auc value: [0.83018868 0.83490566 0.84473824 0.80443352 0.82593675 0.83074232 0.84939986 0.8423576 0.82348968 0.83291257] mean value: 0.8319104880857473 key: test_jcc value: [0.67857143 0.55555556 0.73333333 0.66666667 0.82142857 0.65625 0.5625 0.58064516 0.5862069 0.61538462] mean value: 0.6456542228782217 key: train_jcc value: [0.70967742 0.72 0.73387097 0.63755459 0.70866142 0.72519084 0.7398374 0.72764228 0.70472441 0.71713147] mean value: 0.7124290787616256 key: TN value: 182 mean value: 182.0 key: FP value: 49 mean value: 49.0 key: FN value: 54 mean value: 54.0 key: TP value: 187 mean value: 187.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.24 Accuracy on Blind test: 0.68 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01090574 0.01078701 0.01001048 0.00974464 0.00974822 0.00984216 0.0097878 0.01360297 0.00971985 0.00994635] mean value: 0.01040952205657959 key: score_time value: [0.00956082 0.00963616 0.00891781 0.00883818 0.00886345 0.00890994 0.01088643 0.01318765 0.00889277 0.00891685] mean value: 0.009661006927490234 key: test_mcc value: [0.58536941 0.34426519 0.45455353 0.36612568 0.28602655 0.53734864 0.53734864 0.44646172 0.44874504 0.44874504] mean value: 0.44549894448919625 key: train_mcc value: [0.51000165 0.5311697 0.49206576 0.50190856 0.5353736 0.49927415 0.465305 0.4867519 0.50899412 0.51535889] mean value: 0.50462033294049 key: test_fscore value: [0.8 0.61904762 0.75471698 0.66666667 0.60465116 0.75555556 0.7755102 0.71111111 0.69767442 0.69767442] mean value: 0.7082608137594661 key: train_fscore value: [0.74879227 0.75124378 0.74038462 0.74271845 0.75794621 0.73316708 0.72058824 0.72361809 0.73945409 0.75650118] mean value: 0.7414414008247979 key: test_precision value: [0.76923077 0.72222222 0.68965517 0.71428571 0.68421053 0.80952381 0.73076923 0.72727273 0.75 0.75 ] mean value: 0.7347170172034057 key: train_precision value: [0.76732673 0.79473684 0.75490196 0.765 0.78680203 0.77777778 0.75384615 0.77837838 0.78421053 0.76190476] mean value: 0.7724885164242559 key: test_recall value: [0.83333333 0.54166667 0.83333333 0.625 0.54166667 0.70833333 0.82608696 0.69565217 0.65217391 0.65217391] mean value: 0.6909420289855073 key: train_recall value: [0.73113208 0.71226415 0.72641509 0.72169811 0.73113208 0.69339623 0.69014085 0.67605634 0.69953052 0.75117371] mean value: 0.7132939144299761 key: test_accuracy value: [0.79166667 0.66666667 0.72340426 0.68085106 0.63829787 0.76595745 0.76595745 0.72340426 0.72340426 0.72340426] mean value: 0.7203014184397164 key: train_accuracy value: [0.75471698 0.76415094 0.74588235 0.75058824 0.76705882 0.74823529 0.73176471 0.74117647 0.75294118 0.75764706] mean value: 0.7514162042175362 key: test_roc_auc value: [0.79166667 0.66666667 0.72101449 0.68206522 0.64039855 0.76721014 0.76721014 0.72282609 0.72192029 0.72192029] mean value: 0.7202898550724637 key: train_roc_auc value: [0.75471698 0.76415094 0.74583666 0.75052042 0.76697449 0.74810656 0.73186288 0.74133006 0.75306715 0.75766233] mean value: 0.7514228452475862 key: test_jcc value: [0.66666667 0.44827586 0.60606061 0.5 0.43333333 0.60714286 0.63333333 0.55172414 0.53571429 0.53571429] mean value: 0.5517965367965367 key: train_jcc value: [0.5984556 0.60159363 0.58778626 0.59073359 0.61023622 0.57874016 0.56321839 0.56692913 0.58661417 0.60836502] mean value: 0.5892672169084556 key: TN value: 177 mean value: 177.0 key: FP value: 73 mean value: 73.0 key: FN value: 59 mean value: 59.0 key: TP value: 163 mean value: 163.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.03 Accuracy on Blind test: 0.57 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.02165532 0.00901961 0.01004386 0.00999832 0.01018262 0.01008296 0.00927544 0.01035762 0.0091846 0.01037288] mean value: 0.011017322540283203 key: score_time value: [0.01685476 0.01256466 0.01446056 0.01262641 0.01310086 0.01363015 0.01195931 0.01255512 0.01195836 0.01244521] mean value: 0.01321554183959961 key: test_mcc value: [0.37532595 0.54213748 0.55422693 0.59180008 0.49183384 0.49454913 0.47117841 0.53734864 0.33346345 0.64834149] mean value: 0.5040205390753851 key: train_mcc value: [0.69567994 0.68472112 0.68044488 0.66992632 0.69556724 0.68709327 0.69628526 0.70058158 0.69538058 0.71556485] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( mean value: 0.6921245003133125 key: test_fscore value: [0.69387755 0.7755102 0.8 0.81481481 0.73913043 0.76923077 0.75471698 0.7755102 0.69230769 0.83018868] mean value: 0.7645287330696917 key: train_fscore value: [0.85462555 0.85042735 0.84810127 0.84279476 0.8539823 0.85097192 0.85526316 0.85775862 0.85462555 0.86451613] mean value: 0.8533066608144875 key: test_precision value: [0.68 0.76 0.70967742 0.73333333 0.77272727 0.71428571 0.66666667 0.73076923 0.62068966 0.73333333] mean value: 0.7121482625642803 key: train_precision value: [0.80165289 0.77734375 0.76717557 0.78455285 0.80416667 0.78486056 0.80246914 0.79282869 0.80497925 0.79761905] mean value: 0.7917648406837627 key: test_recall value: [0.70833333 0.79166667 0.91666667 0.91666667 0.70833333 0.83333333 0.86956522 0.82608696 0.7826087 0.95652174] mean value: 0.8309782608695653 key: train_recall value: [0.91509434 0.93867925 0.94811321 0.91037736 0.91037736 0.92924528 0.91549296 0.9342723 0.91079812 0.94366197] mean value: 0.9256112144565506 key: test_accuracy value: [0.6875 0.77083333 0.76595745 0.78723404 0.74468085 0.74468085 0.72340426 0.76595745 0.65957447 0.80851064] mean value: 0.7458333333333333 key: train_accuracy value: [0.84433962 0.83490566 0.83058824 0.83058824 0.84470588 0.83764706 0.84470588 0.84470588 0.84470588 0.85176471] mean value: 0.840865704772475 key: test_roc_auc value: [0.6875 0.77083333 0.76268116 0.78442029 0.74547101 0.74275362 0.72644928 0.76721014 0.66213768 0.8115942 ] mean value: 0.7461050724637681 key: train_roc_auc value: [0.84433962 0.83490566 0.83086412 0.83077553 0.84486004 0.83786208 0.84453893 0.84449464 0.84455 0.85154797] mean value: 0.8408738595092569 key: test_jcc value: [0.53125 0.63333333 0.66666667 0.6875 0.5862069 0.625 0.60606061 0.63333333 0.52941176 0.70967742] mean value: 0.6208440020006385 key: train_jcc value: [0.74615385 0.73977695 0.73626374 0.72830189 0.74517375 0.7406015 0.74712644 0.7509434 0.74615385 0.76136364] mean value: 0.7441858985341548 key: TN value: 156 mean value: 156.0 key: FP value: 40 mean value: 40.0 key: FN value: 80 mean value: 80.0 key: TP value: 196 mean value: 196.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: -0.04 Accuracy on Blind test: 0.57 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.02334023 0.01960754 0.01953459 0.01931095 0.0190649 0.0195787 0.01955843 0.01917648 0.01953411 0.01945996] mean value: 0.01981658935546875 key: score_time value: [0.01181769 0.0121665 0.01234674 0.01127791 0.01118779 0.01132178 0.01210308 0.01147628 0.01121712 0.01117063] mean value: 0.011608552932739259 key: test_mcc value: [0.79235477 0.63902148 0.66121206 0.57713344 0.7196797 0.74773263 0.65942029 0.70289855 0.7876601 0.62966842] mean value: 0.6916781436411534 key: train_mcc value: [0.81626479 0.83052137 0.81668554 0.77495013 0.82595793 0.81668554 0.82596948 0.8406091 0.84941093 0.78867849] mean value: 0.8185733306374121 key: test_fscore value: [0.89795918 0.79069767 0.84 0.7826087 0.8372093 0.86956522 0.82608696 0.85106383 0.88888889 0.7804878 ] mean value: 0.8364567553537045 key: train_fscore value: [0.90692124 0.9138756 0.90692124 0.88405797 0.91211401 0.90692124 0.91252955 0.91866029 0.92488263 0.89260143] mean value: 0.9079485205500408 key: test_precision value: [0.88 0.89473684 0.80769231 0.81818182 0.94736842 0.90909091 0.82608696 0.83333333 0.90909091 0.88888889] mean value: 0.87144703859578 key: train_precision value: [0.9178744 0.92718447 0.9178744 0.90594059 0.91866029 0.9178744 0.91904762 0.93658537 0.92488263 0.90776699] mean value: 0.9193691139866482 key: test_recall value: [0.91666667 0.70833333 0.875 0.75 0.75 0.83333333 0.82608696 0.86956522 0.86956522 0.69565217] mean value: 0.8094202898550724 key: train_recall value: [0.89622642 0.9009434 0.89622642 0.86320755 0.90566038 0.89622642 0.90610329 0.90140845 0.92488263 0.87793427] mean value: 0.896881920453539 key: test_accuracy value: [0.89583333 0.8125 0.82978723 0.78723404 0.85106383 0.87234043 0.82978723 0.85106383 0.89361702 0.80851064] mean value: 0.8431737588652481 key: train_accuracy value: [0.90801887 0.91509434 0.90823529 0.88705882 0.91294118 0.90823529 0.91294118 0.92 0.92470588 0.89411765] mean value: 0.9091348501664817 key: test_roc_auc value: [0.89583333 0.8125 0.82880435 0.78804348 0.85326087 0.87318841 0.82971014 0.85144928 0.89311594 0.80615942] mean value: 0.8432065217391305 key: train_roc_auc value: [0.90801887 0.91509434 0.9082071 0.88700283 0.91292409 0.9082071 0.9129573 0.92004385 0.92470547 0.89415582] mean value: 0.9091316768535742 key: test_jcc value: [0.81481481 0.65384615 0.72413793 0.64285714 0.72 0.76923077 0.7037037 0.74074074 0.8 0.64 ] mean value: 0.7209331256227808 key: train_jcc value: [0.82969432 0.84140969 0.82969432 0.79220779 0.83842795 0.82969432 0.83913043 0.84955752 0.86026201 0.80603448] mean value: 0.8316112849267064 key: TN value: 207 mean value: 207.0 key: FP value: 45 mean value: 45.0 key: FN value: 29 mean value: 29.0 key: TP value: 191 mean value: 191.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.38 Accuracy on Blind test: 0.79 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.38979816 1.88678241 1.85302711 1.83790088 1.86066747 1.58438802 1.89584589 1.81168294 1.87610316 1.85480547] mean value: 1.7851001501083374 key: score_time value: [0.0121727 0.01449251 0.01456285 0.01456189 0.01456237 0.01217008 0.0145936 0.01232529 0.01464391 0.01461911] mean value: 0.013870429992675782 key: test_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( [0.9591663 0.9591663 0.91485507 0.87917396 1. 0.87917396 0.87979456 0.91833182 0.91833182 0.95833333] mean value: 0.9266327142476708 key: train_mcc value: [0.98594778 0.99061012 1. 0.99530516 0.99530516 0.99530516 0.99530506 0.99063185 0.99063185 0.99063185] mean value: 0.9929674015085231 key: test_fscore value: [0.97959184 0.97959184 0.95833333 0.94117647 1. 0.94117647 0.93877551 0.95833333 0.95833333 0.9787234 ] mean value: 0.963403552910526 key: train_fscore value: [0.99297424 0.99530516 1. 0.99764706 0.99764706 0.99764706 0.99765808 0.9953271 0.9953271 0.9953271 ] mean value: 0.9964859967702223 key: test_precision value: [0.96 0.96 0.95833333 0.88888889 1. 0.88888889 0.88461538 0.92 0.92 0.95833333] mean value: 0.9339059829059829 key: train_precision value: [0.98604651 0.99065421 1. 0.99530516 0.99530516 0.99530516 0.9953271 0.99069767 0.99069767 0.99069767] mean value: 0.9930036336252683 key: test_recall value: [1. 1. 0.95833333 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9958333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97916667 0.97916667 0.95744681 0.93617021 1. 0.93617021 0.93617021 0.95744681 0.95744681 0.9787234 ] mean value: 0.9617907801418442 key: train_accuracy value: [0.99292453 0.99528302 1. 0.99764706 0.99764706 0.99764706 0.99764706 0.99529412 0.99529412 0.99529412] mean value: 0.9964678135405107 key: test_roc_auc value: [0.97916667 0.97916667 0.95742754 0.93478261 1. 0.93478261 0.9375 0.95833333 0.95833333 0.97916667] mean value: 0.9618659420289856 key: train_roc_auc value: [0.99292453 0.99528302 1. 0.99765258 0.99765258 0.99765258 0.99764151 0.99528302 0.99528302 0.99528302] mean value: 0.996465585968642 key: test_jcc value: [0.96 0.96 0.92 0.88888889 1. 0.88888889 0.88461538 0.92 0.92 0.95833333] mean value: 0.9300726495726496 key: train_jcc value: [0.98604651 0.99065421 1. 0.99530516 0.99530516 0.99530516 0.9953271 0.99069767 0.99069767 0.99069767] mean value: 0.9930036336252683 key: TN value: 219 mean value: 219.0 key: FP value: 1 mean value: 1.0 key: FN value: 17 mean value: 17.0 key: TP value: 235 mean value: 235.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.69 Accuracy on Blind test: 0.89 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02435589 0.01885676 0.01731181 0.01705623 0.01679707 0.01767826 0.01765943 0.01808286 0.01462436 0.01631927] mean value: 0.01787419319152832 key: score_time value: [0.0118289 0.00927591 0.00880337 0.00892401 0.00854754 0.00902939 0.00864697 0.00859952 0.00880909 0.00884032] mean value: 0.009130501747131347 key: test_mcc value: [0.9591663 0.9591663 0.95833333 0.91804649 1. 0.91804649 0.95833333 0.95833333 0.91833182 1. ] mean value: 0.9547757417604974 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97959184 0.97959184 0.9787234 0.96 1. 0.96 0.9787234 0.9787234 0.95833333 1. ] mean value: 0.9773687219568679 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.96 0.96 1. 0.92307692 1. 0.92307692 0.95833333 0.95833333 0.92 1. ] mean value: 0.9602820512820512 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.95833333 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9958333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97916667 0.97916667 0.9787234 0.95744681 1. 0.95744681 0.9787234 0.9787234 0.95744681 1. ] mean value: 0.9766843971631205 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97916667 0.97916667 0.97916667 0.95652174 1. 0.95652174 0.97916667 0.97916667 0.95833333 1. ] mean value: 0.9767210144927537 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.96 0.96 0.95833333 0.92307692 1. 0.92307692 0.95833333 0.95833333 0.92 1. ] mean value: 0.9561153846153847 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 226 mean value: 226.0 key: FP value: 1 mean value: 1.0 key: FN value: 10 mean value: 10.0 key: TP value: 235 mean value: 235.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.76 Accuracy on Blind test: 0.92 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.11192679 0.11509895 0.11650324 0.11855555 0.11755848 0.11214924 0.11129642 0.11113 0.11224771 0.11210442] mean value: 0.1138570785522461 key: score_time value: [0.01835728 0.0193646 0.0183847 0.01931548 0.01799941 0.01849747 0.01754355 0.01751018 0.01742411 0.01748538] mean value: 0.018188214302062987 key: test_mcc value: [1. 0.87576054 0.87318841 1. 0.95833333 0.95825929 0.91833182 0.87318841 0.91833182 0.95833333] mean value: 0.9333726950567002 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.93617021 0.93617021 1. 0.9787234 0.97959184 0.95833333 0.93617021 0.95833333 0.9787234 ] mean value: 0.9662215950209871 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.95652174 0.95652174 1. 1. 0.96 0.92 0.91666667 0.92 0.95833333] mean value: 0.958804347826087 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.91666667 0.91666667 1. 0.95833333 1. 1. 0.95652174 1. 1. ] mean value: 0.9748188405797101 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.9375 0.93617021 1. 0.9787234 0.9787234 0.95744681 0.93617021 0.95744681 0.9787234 ] mean value: 0.9660904255319149 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.9375 0.9365942 1. 0.97916667 0.97826087 0.95833333 0.9365942 0.95833333 0.97916667] mean value: 0.9663949275362318 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.88 0.88 1. 0.95833333 0.96 0.92 0.88 0.92 0.95833333] mean value: 0.9356666666666668 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 226 mean value: 226.0 key: FP value: 6 mean value: 6.0 key: FN value: 10 mean value: 10.0 key: TP value: 230 mean value: 230.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.4 Accuracy on Blind test: 0.83 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00963283 0.00949097 0.00959659 0.01043701 0.01073122 0.00953889 0.00993323 0.00965047 0.00965047 0.01083255] mean value: 0.009949421882629395 key: score_time value: [0.00854564 0.0085845 0.00857139 0.00928402 0.00901961 0.00862765 0.00914121 0.00857615 0.00881815 0.00919294] mean value: 0.008836126327514649 key: test_mcc value: [0.84515425 0.70710678 0.7876601 0.91804649 0.73387289 0.91804649 0.87979456 0.84254172 0.80641033 0.87979456] mean value: 0.8318428175010515 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.92307692 0.85714286 0.89795918 0.96 0.87272727 0.96 0.93877551 0.92 0.90196078 0.93877551] mean value: 0.917041804134241 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.75 0.88 0.92307692 0.77419355 0.92307692 0.88461538 0.85185185 0.82142857 0.88461538] mean value: 0.8550001444194993 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.91666667 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9916666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.91666667 0.83333333 0.89361702 0.95744681 0.85106383 0.95744681 0.93617021 0.91489362 0.89361702 0.93617021] mean value: 0.9090425531914892 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.91666667 0.83333333 0.89311594 0.95652174 0.84782609 0.95652174 0.9375 0.91666667 0.89583333 0.9375 ] mean value: 0.9091485507246377 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.85714286 0.75 0.81481481 0.92307692 0.77419355 0.92307692 0.88461538 0.85185185 0.82142857 0.88461538] mean value: 0.8484816259009808 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 195 mean value: 195.0 key: FP value: 2 mean value: 2.0 key: FN value: 41 mean value: 41.0 key: TP value: 234 mean value: 234.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.19 Accuracy on Blind test: 0.74 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.57338715 1.54828095 1.5590179 1.55561304 1.52688169 1.5289619 1.55676508 1.60693359 1.57568049 1.57357478] mean value: 1.5605096578598023 key: score_time value: [0.09393835 0.09686708 0.09653354 0.09693933 0.09467936 0.09551907 0.09138155 0.09113574 0.0913012 0.09131646] mean value: 0.09396116733551026 key: test_mcc value: [1. 0.91986621 0.95833333 0.95825929 1. 0.95825929 0.95833333 1. 0.91833182 1. ] mean value: 0.9671383281416119 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.95652174 0.9787234 0.97959184 1. 0.97959184 0.9787234 1. 0.95833333 1. ] mean value: 0.9831485554443795 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 0.96 1. 0.96 0.95833333 1. 0.92 1. ] mean value: 0.9798333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.91666667 0.95833333 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9875 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.95833333 0.9787234 0.9787234 1. 0.9787234 0.9787234 1. 0.95744681 1. ] mean value: 0.9830673758865249 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.95833333 0.97916667 0.97826087 1. 0.97826087 0.97916667 1. 0.95833333 1. ] mean value: 0.9831521739130435 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.91666667 0.95833333 0.96 1. 0.96 0.95833333 1. 0.92 1. ] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( mean value: 0.9673333333333334 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 231 mean value: 231.0 key: FP value: 3 mean value: 3.0 key: FN value: 5 mean value: 5.0 key: TP value: 233 mean value: 233.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.68 Accuracy on Blind test: 0.9 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.92463493 0.91933489 0.90347314 0.90513873 0.96518564 0.93658257 0.92193604 0.89846778 0.90099645 0.93485951] mean value: 0.9210609674453736 key: score_time value: [0.22926402 0.18491554 0.19461608 0.21672845 0.18792701 0.20230293 0.20627904 0.17378759 0.19786906 0.21795678] mean value: 0.2011646509170532 key: test_mcc value: [0.9591663 0.91986621 0.95833333 0.95825929 0.91833182 0.95825929 0.95833333 0.91833182 0.91833182 1. ] mean value: 0.9467213227939449 key: train_mcc value: [0.99061012 0.98586002 0.99063227 0.99063227 0.98589335 0.99058818 0.98598008 0.98134942 0.99063185 0.99063185] mean value: 0.9882809416190621 key: test_fscore value: [0.97959184 0.95652174 0.9787234 0.97959184 0.95652174 0.97959184 0.9787234 0.95833333 0.95833333 1. ] mean value: 0.9725932463642257 key: train_fscore value: [0.99530516 0.99294118 0.99530516 0.99530516 0.99294118 0.99528302 0.99300699 0.99069767 0.9953271 0.9953271 ] mean value: 0.9941439737799922 key: test_precision value: [0.96 1. 1. 0.96 1. 0.96 0.95833333 0.92 0.92 1. ] mean value: 0.9678333333333333 key: train_precision value: [0.99065421 0.99061033 0.99065421 0.99065421 0.99061033 0.99528302 0.98611111 0.98156682 0.99069767 0.99069767] mean value: 0.9897539573192169 key: test_recall value: [1. 0.91666667 0.95833333 1. 0.91666667 1. 1. 1. 1. 1. ] mean value: 0.9791666666666666 key: train_recall value: [1. 0.99528302 1. 1. 0.99528302 0.99528302 1. 1. 1. 1. ] mean value: 0.9985849056603774 key: test_accuracy value: [0.97916667 0.95833333 0.9787234 0.9787234 0.95744681 0.9787234 0.9787234 0.95744681 0.95744681 1. ] mean value: 0.9724734042553191 key: train_accuracy value: [0.99528302 0.99292453 0.99529412 0.99529412 0.99294118 0.99529412 0.99294118 0.99058824 0.99529412 0.99529412] mean value: 0.9941148723640401 key: test_roc_auc value: [0.97916667 0.95833333 0.97916667 0.97826087 0.95833333 0.97826087 0.97916667 0.95833333 0.95833333 1. ] mean value: 0.9727355072463769 key: train_roc_auc value: [0.99528302 0.99292453 0.99530516 0.99530516 0.99294667 0.99529409 0.99292453 0.99056604 0.99528302 0.99528302] mean value: 0.9941115244928691 key: test_jcc value: [0.96 0.91666667 0.95833333 0.96 0.91666667 0.96 0.95833333 0.92 0.92 1. ] mean value: 0.9470000000000001 key: train_jcc value: [0.99065421 0.98598131 0.99065421 0.99065421 0.98598131 0.99061033 0.98611111 0.98156682 0.99069767 0.99069767] mean value: 0.9883608842508176 key: TN value: 228 mean value: 228.0 key: FP value: 5 mean value: 5.0 key: FN value: 8 mean value: 8.0 key: TP value: 231 mean value: 231.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.73 Accuracy on Blind test: 0.91 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.06745553 0.05746651 0.05764294 0.05900121 0.05914187 0.05862188 0.05908942 0.0608089 0.05977464 0.06338382] mean value: 0.06023867130279541 key: score_time value: [0.0108037 0.01069331 0.01060319 0.01084948 0.01076293 0.01081204 0.01059222 0.01054955 0.01077199 0.01091146] mean value: 0.010734987258911134 key: test_mcc value: [0.9591663 1. 0.95833333 0.95825929 1. 0.91804649 1. 1. 0.91833182 1. ] mean value: 0.9712137244006647 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97959184 1. 0.9787234 0.97959184 1. 0.96 1. 1. 0.95833333 1. ] mean value: 0.985624041105804 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.96 1. 1. 0.96 1. 0.92307692 1. 1. 0.92 1. ] mean value: 0.9763076923076923 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.95833333 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9958333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97916667 1. 0.9787234 0.9787234 1. 0.95744681 1. 1. 0.95744681 1. ] mean value: 0.9851507092198583 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97916667 1. 0.97916667 0.97826087 1. 0.95652174 1. 1. 0.95833333 1. ] mean value: 0.9851449275362318 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.96 1. 0.95833333 0.96 1. 0.92307692 1. 1. 0.92 1. ] mean value: 0.9721410256410257 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 230 mean value: 230.0 key: FP value: 1 mean value: 1.0 key: FN value: 6 mean value: 6.0 key: TP value: 235 mean value: 235.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.85 Accuracy on Blind test: 0.95 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03682876 0.07974267 0.05892038 0.04035926 0.04105043 0.06086922 0.0796988 0.049788 0.03683519 0.03657722] mean value: 0.052066993713378903 key: score_time value: [0.02253962 0.0241096 0.01197815 0.01230359 0.01230454 0.01259184 0.02162075 0.0121882 0.01248527 0.01208448] mean value: 0.01542060375213623 key: test_mcc value: [0.9591663 0.8819171 0.83243502 0.84147165 0.95825929 0.91804649 0.91833182 0.95833333 0.87979456 0.84254172] mean value: 0.899029728310231 key: train_mcc value: [0.97668677 0.97668677 0.97674215 0.98135106 0.97674215 0.98135106 0.97215032 0.9767396 0.98134942 0.97215032] mean value: 0.9771949634162567 key: test_fscore value: [0.97959184 0.94117647 0.92 0.92307692 0.97959184 0.96 0.95833333 0.9787234 0.93877551 0.92 ] mean value: 0.9499269314927281 key: train_fscore value: [0.98834499 0.98834499 0.98834499 0.99065421 0.98834499 0.99065421 0.98611111 0.98839907 0.99069767 0.98611111] mean value: 0.9886007333161488 key: test_precision value: [0.96 0.88888889 0.88461538 0.85714286 0.96 0.92307692 0.92 0.95833333 0.88461538 0.85185185] mean value: 0.9088524623524623 key: train_precision value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97695853 0.98148148 0.97260274 0.97706422 0.98156682 0.97260274] mean value: 0.9774633584257492 key: test_recall value: [1. 1. 0.95833333 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9958333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97916667 0.9375 0.91489362 0.91489362 0.9787234 0.95744681 0.95744681 0.9787234 0.93617021 0.91489362] mean value: 0.9469858156028368 key: train_accuracy value: [0.98820755 0.98820755 0.98823529 0.99058824 0.98823529 0.99058824 0.98588235 0.98823529 0.99058824 0.98588235] mean value: 0.988465038845727 key: test_roc_auc value: [0.97916667 0.9375 0.91394928 0.91304348 0.97826087 0.95652174 0.95833333 0.97916667 0.9375 0.91666667] mean value: 0.9470108695652174 key: train_roc_auc value: [0.98820755 0.98820755 0.98826291 0.99061033 0.98826291 0.99061033 0.98584906 0.98820755 0.99056604 0.98584906] mean value: 0.988463327132607 key: test_jcc value: [0.96 0.88888889 0.85185185 0.85714286 0.96 0.92307692 0.92 0.95833333 0.88461538 0.85185185] mean value: 0.905576109076109 key: train_jcc value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97695853 0.98148148 0.97260274 0.97706422 0.98156682 0.97260274] mean value: 0.9774633584257492 key: TN value: 212 mean value: 212.0 key: FP value: 1 mean value: 1.0 key: FN value: 24 mean value: 24.0 key: TP value: 235 mean value: 235.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.7 Accuracy on Blind test: 0.9 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01340699 0.01024008 0.00958824 0.0094533 0.01009774 0.00923991 0.00937963 0.00952458 0.00938821 0.00920439] mean value: 0.009952306747436523 key: score_time value: [0.01165438 0.00972748 0.00859261 0.00851846 0.00869226 0.00849319 0.00853133 0.00925541 0.00852942 0.0085206 ] mean value: 0.009051513671875 key: test_mcc value: [0.50709255 0.44194174 0.53176131 0.36231884 0.23369565 0.36116212 0.5326087 0.58428436 0.4899891 0.36265926] mean value: 0.44075136351965527 key: train_mcc value: [0.49490154 0.49625189 0.48033439 0.48845269 0.54396959 0.48764745 0.5548004 0.53004837 0.53464113 0.52575614] mean value: 0.5136803598430164 key: test_fscore value: [0.72727273 0.65 0.7755102 0.68085106 0.625 0.69387755 0.76595745 0.8 0.72727273 0.65116279] mean value: 0.7096904510983468 key: train_fscore value: [0.72727273 0.7244898 0.72319202 0.71794872 0.76626506 0.73607748 0.76772616 0.75961538 0.76258993 0.75544794] mean value: 0.7440625219101846 key: test_precision value: [0.8 0.8125 0.76 0.69565217 0.625 0.68 0.75 0.74074074 0.76190476 0.7 ] mean value: 0.7325797676558546 key: train_precision value: [0.7826087 0.78888889 0.76719577 0.78651685 0.78325123 0.75621891 0.80102041 0.77832512 0.77941176 0.78 ] mean value: 0.7803437638691002 key: test_recall value: [0.66666667 0.54166667 0.79166667 0.66666667 0.625 0.70833333 0.7826087 0.86956522 0.69565217 0.60869565] mean value: 0.6956521739130435 key: train_recall value: [0.67924528 0.66981132 0.68396226 0.66037736 0.75 0.71698113 0.7370892 0.74178404 0.74647887 0.73239437] mean value: 0.7118123837363806 key: test_accuracy value: [0.75 0.70833333 0.76595745 0.68085106 0.61702128 0.68085106 0.76595745 0.78723404 0.74468085 0.68085106] mean value: 0.7181737588652483 key: train_accuracy value: [0.74528302 0.74528302 0.73882353 0.74117647 0.77176471 0.74352941 0.77647059 0.76470588 0.76705882 0.76235294] mean value: 0.7556448390677025 key: test_roc_auc value: [0.75 0.70833333 0.76539855 0.68115942 0.61684783 0.68025362 0.76630435 0.78894928 0.74365942 0.67934783] mean value: 0.7180253623188406 key: train_roc_auc value: [0.74528302 0.74528302 0.73869475 0.7409868 0.77171362 0.74346709 0.77656347 0.76475994 0.76710736 0.7624236 ] mean value: 0.7556282664540703 key: test_jcc value: [0.57142857 0.48148148 0.63333333 0.51612903 0.45454545 0.53125 0.62068966 0.66666667 0.57142857 0.48275862] mean value: 0.5529711387004211 key: train_jcc value: [0.57142857 0.568 0.56640625 0.56 0.62109375 0.58237548 0.62301587 0.6124031 0.61627907 0.60700389] mean value: 0.5928005984964867 key: TN value: 175 mean value: 175.0 key: FP value: 72 mean value: 72.0 key: FN value: 61 mean value: 61.0 key: TP value: 164 mean value: 164.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.29 Accuracy on Blind test: 0.72 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01910973 0.02240539 0.02354193 0.02688932 0.02919555 0.02573752 0.0232811 0.02313137 0.02699065 0.0267837 ] mean value: 0.02470662593841553 key: score_time value: [0.00947046 0.01099229 0.01149726 0.01185322 0.01161885 0.0115099 0.01150322 0.01151037 0.01153064 0.0115459 ] mean value: 0.011303210258483886 key: test_mcc value: [0.9591663 0.91986621 0.95833333 0.91804649 0.91833182 0.91804649 0.95833333 0.95833333 0.38895926 0.91804649] mean value: 0.8815463075360693 key: train_mcc value: [0.97208751 0.98130676 0.985981 0.99063227 0.98589335 0.97674215 0.97648101 0.98598008 0.45056456 0.95311186] mean value: 0.9258780536304961 key: test_fscore value: [0.97959184 0.96 0.9787234 0.96 0.95652174 0.96 0.9787234 0.9787234 0.5 0.95454545] mean value: 0.9206829243176541 key: train_fscore value: [0.98604651 0.99065421 0.99297424 0.99530516 0.99294118 0.98834499 0.9882904 0.99300699 0.50526316 0.97630332] mean value: 0.9409130151809825 key: test_precision value: [0.96 0.92307692 1. 0.92307692 1. 0.92307692 0.95833333 0.95833333 0.88888889 1. ] mean value: 0.9534786324786324 key: train_precision value: [0.97247706 0.98148148 0.98604651 0.99065421 0.99061033 0.97695853 0.98598131 0.98611111 1. 0.98564593] mean value: 0.9855966469457847 key: test_recall value: [1. 1. 0.95833333 1. 0.91666667 1. 1. 1. 0.34782609 0.91304348] mean value: 0.913586956521739 key: train_recall value: [1. 1. 1. 1. 0.99528302 1. 0.99061033 1. 0.33802817 0.96713615] mean value: 0.9291057666755249 key: test_accuracy value: [0.97916667 0.95833333 0.9787234 0.95744681 0.95744681 0.95744681 0.9787234 0.9787234 0.65957447 0.95744681] mean value: 0.9363031914893618 key: train_accuracy value: [0.98584906 0.99056604 0.99294118 0.99529412 0.99294118 0.98823529 0.98823529 0.99294118 0.66823529 0.97647059] mean value: 0.9571709211986681 key: test_roc_auc value: [0.97916667 0.95833333 0.97916667 0.95652174 0.95833333 0.95652174 0.97916667 0.97916667 0.65307971 0.95652174] mean value: 0.9355978260869566 key: train_roc_auc value: [0.98584906 0.99056604 0.99295775 0.99530516 0.99294667 0.98826291 0.98822969 0.99292453 0.66901408 0.9764926 ] mean value: 0.9572548498538399 key: test_jcc value: [0.96 0.92307692 0.95833333 0.92307692 0.91666667 0.92307692 0.95833333 0.95833333 0.33333333 0.91304348] mean value: 0.8767274247491639 key: train_jcc value: [0.97247706 0.98148148 0.98604651 0.99065421 0.98598131 0.97695853 0.97685185 0.98611111 0.33802817 0.9537037 ] mean value: 0.9148293932374637 key: TN value: 226 mean value: 226.0 key: FP value: 20 mean value: 20.0 key: FN value: 10 mean value: 10.0 key: TP value: 216 mean value: 216.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.82 Accuracy on Blind test: 0.94 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01817179 0.01651382 0.02004027 0.0179913 0.01704335 0.01677728 0.01724768 0.01694465 0.01827598 0.01648617] mean value: 0.01754922866821289 key: score_time value: [0.01151705 0.01156211 0.0117805 0.01157784 0.01157165 0.01157284 0.01156044 0.01203346 0.01163268 0.01156354] mean value: 0.011637210845947266 key: test_mcc value: [0.9591663 0.84515425 0.91485507 0.87917396 0.87979456 0.91804649 0.73692303 0.5732115 0.91833182 0.26673253] mean value: 0.7891389538646197 key: train_mcc value: [0.98130676 0.95389198 0.97674215 0.98135106 0.95765696 0.94504426 0.85916204 0.71378158 0.99063185 0.55691515] mean value: 0.8916483794877926 key: test_fscore value: [0.97959184 0.92307692 0.95833333 0.94117647 0.93333333 0.96 0.86792453 0.79310345 0.95833333 0.23076923] mean value: 0.8545642437746832 key: train_fscore value: [0.99065421 0.97695853 0.98834499 0.99065421 0.9787234 0.97247706 0.930131 0.86060606 0.9953271 0.64984227] mean value: 0.9333718832451053 key: test_precision value: [0.96 0.85714286 0.95833333 0.88888889 1. 0.92307692 0.76666667 0.65714286 0.92 1. ] mean value: 0.8931251526251526 key: train_precision value: [0.98148148 0.95495495 0.97695853 0.98148148 0.98104265 0.94642857 0.86938776 0.75531915 0.99069767 0.99038462] mean value: 0.942813686256198 key: test_recall value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] [1. 1. 0.95833333 1. 0.875 1. 1. 1. 1. 0.13043478] mean value: 0.8963768115942029 key: train_recall value: [1. 1. 1. 1. 0.97641509 1. 1. 1. 1. 0.48356808] mean value: 0.9459983169456991 key: test_accuracy value: [0.97916667 0.91666667 0.95744681 0.93617021 0.93617021 0.95744681 0.85106383 0.74468085 0.95744681 0.57446809] mean value: 0.8810726950354612 key: train_accuracy value: [0.99056604 0.97641509 0.98823529 0.99058824 0.97882353 0.97176471 0.92470588 0.83764706 0.99529412 0.73882353] mean value: 0.9392863485016647 key: test_roc_auc value: [0.97916667 0.91666667 0.95742754 0.93478261 0.9375 0.95652174 0.85416667 0.75 0.95833333 0.56521739] mean value: 0.8809782608695652 key: train_roc_auc value: [0.99056604 0.97641509 0.98826291 0.99061033 0.97881788 0.97183099 0.9245283 0.83726415 0.99528302 0.73942555] mean value: 0.9393004251926653 key: test_jcc value: [0.96 0.85714286 0.92 0.88888889 0.875 0.92307692 0.76666667 0.65714286 0.92 0.13043478] mean value: 0.7898352975526889 key: train_jcc value: [0.98148148 0.95495495 0.97695853 0.98148148 0.95833333 0.94642857 0.86938776 0.75531915 0.99069767 0.48130841] mean value: 0.8896351337697215 key: TN value: 204 mean value: 204.0 key: FP value: 24 mean value: 24.0 key: FN value: 32 mean value: 32.0 key: TP value: 212 mean value: 212.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.75 Accuracy on Blind test: 0.91 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.16770864 0.14934564 0.14924645 0.1498208 0.14950657 0.14951468 0.15319133 0.1516273 0.15055633 0.15102768] mean value: 0.152154541015625 key: score_time value: [0.01524901 0.01509094 0.0154016 0.01516843 0.01500225 0.01513433 0.01561236 0.01504779 0.01508641 0.0153389 ] mean value: 0.015213203430175782 key: test_mcc value: [0.9591663 0.9591663 0.91485507 0.95825929 1. 0.91804649 1. 1. 0.91833182 1. ] mean value: 0.9627825287799624 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97959184 0.97959184 0.95833333 0.97959184 1. 0.96 1. 1. 0.95833333 1. ] mean value: 0.9815442176870748 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.96 0.96 0.95833333 0.96 1. 0.92307692 1. 1. 0.92 1. ] mean value: 0.9681410256410257 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.95833333 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9958333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97916667 0.97916667 0.95744681 0.9787234 1. 0.95744681 1. 1. 0.95744681 1. ] mean value: 0.9809397163120568 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97916667 0.97916667 0.95742754 0.97826087 1. 0.95652174 1. 1. 0.95833333 1. ] mean value: 0.9808876811594203 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.96 0.96 0.92 0.96 1. 0.92307692 1. 1. 0.92 1. ] mean value: 0.9643076923076924 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 228 mean value: 228.0 key: FP value: 1 mean value: 1.0 key: FN value: 8 mean value: 8.0 key: TP value: 235 mean value: 235.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.78 Accuracy on Blind test: 0.93 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.04388475 0.07044077 0.06779981 0.05081892 0.0576489 0.04585266 0.04862523 0.05665183 0.06696415 0.0718863 ] mean value: 0.058057332038879396 key: score_time value: [0.02634716 0.03491426 0.01997948 0.02338076 0.02006984 0.02316165 0.0306685 0.02496171 0.02564192 0.03851509] mean value: 0.02676403522491455 key: test_mcc value: [0.9591663 0.9591663 0.91833182 0.95825929 1. 0.91804649 0.95833333 0.95833333 0.91833182 1. ] mean value: 0.9547968702932919 key: train_mcc value: [0.98117574 0.99061012 1. 0.99530506 0.99530506 0.99530506 0.99530516 0.99530516 0.99058818 0.99058818] mean value: 0.9929487736067821 key: test_fscore value: [0.97959184 0.97959184 0.95652174 0.97959184 1. 0.96 0.9787234 0.9787234 0.95833333 1. ] mean value: 0.9771077391178489 key: train_fscore value: [0.99061033 0.99530516 1. 0.99763593 0.99763593 0.99763593 0.99764706 0.99764706 0.99530516 0.99530516] mean value: 0.9964727740661742 key: test_precision value: [0.96 0.96 1. 0.96 1. 0.92307692 0.95833333 0.95833333 0.92 1. ] mean value: 0.963974358974359 key: train_precision value: [0.98598131 0.99065421 1. 1. 1. 1. 1. 1. 0.99530516 0.99530516] mean value: 0.9967245842657189 key: test_recall value: [1. 1. 0.91666667 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9916666666666666 key: train_recall value: [0.99528302 1. 1. 0.99528302 0.99528302 0.99528302 0.99530516 0.99530516 0.99530516 0.99530516] mean value: 0.9962352732748692 key: test_accuracy value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] [0.97916667 0.97916667 0.95744681 0.9787234 1. 0.95744681 0.9787234 0.9787234 0.95744681 1. ] mean value: 0.9766843971631205 key: train_accuracy value: [0.99056604 0.99528302 1. 0.99764706 0.99764706 0.99764706 0.99764706 0.99764706 0.99529412 0.99529412] mean value: 0.996467258601554 key: test_roc_auc value: [0.97916667 0.97916667 0.95833333 0.97826087 1. 0.95652174 0.97916667 0.97916667 0.95833333 1. ] mean value: 0.9768115942028986 key: train_roc_auc value: [0.99056604 0.99528302 1. 0.99764151 0.99764151 0.99764151 0.99765258 0.99765258 0.99529409 0.99529409] mean value: 0.9964666932412085 key: test_jcc value: [0.96 0.96 0.91666667 0.96 1. 0.92307692 0.95833333 0.95833333 0.92 1. ] mean value: 0.9556410256410256 key: train_jcc value: [0.98139535 0.99065421 1. 0.99528302 0.99528302 0.99528302 0.99530516 0.99530516 0.99065421 0.99065421] mean value: 0.992981735090191 key: TN value: 227 mean value: 227.0 key: FP value: 2 mean value: 2.0 key: FN value: 9 mean value: 9.0 key: TP value: 234 mean value: 234.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.76 Accuracy on Blind test: 0.92 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.12236857 0.10208821 0.11127877 0.08595467 0.113271 0.17878008 0.09545183 0.11677861 0.08445358 0.15772724] mean value: 0.11681525707244873 key: score_time value: [0.02326179 0.01438665 0.02932024 0.01423693 0.02347517 0.03062963 0.01435137 0.02352238 0.01427579 0.02511668] mean value: 0.021257662773132326 key: test_mcc value: [0.797925 0.83333333 0.74682354 0.8047833 0.57713344 0.87917396 0.68369322 0.67037015 0.61775362 0.87979456] mean value: 0.7490784119796873 key: train_mcc value: [0.95806958 0.94491118 0.953621 0.92992145 0.92507925 0.92992145 0.94908163 0.94874597 0.92991066 0.9253549 ] mean value: 0.9394617058916153 key: test_fscore value: [0.90196078 0.91666667 0.88 0.90566038 0.7826087 0.94117647 0.84615385 0.84 0.80851064 0.93877551] mean value: 0.8761512989235092 key: train_fscore value: [0.97911833 0.97247706 0.97685185 0.96519722 0.9627907 0.96519722 0.97471264 0.97459584 0.96535797 0.96313364] mean value: 0.9699432469622048 key: test_precision value: [0.85185185 0.91666667 0.84615385 0.82758621 0.81818182 0.88888889 0.75862069 0.77777778 0.79166667 0.88461538] mean value: 0.8362009797354626 key: train_precision value: [0.96347032 0.94642857 0.95909091 0.94977169 0.94954128 0.94977169 0.95495495 0.95909091 0.95 0.94570136] mean value: 0.9527821685065214 key: test_recall value: [0.95833333 0.91666667 0.91666667 1. 0.75 1. 0.95652174 0.91304348 0.82608696 1. ] mean value: 0.9237318840579709 key: train_recall value: [0.99528302 1. 0.99528302 0.98113208 0.97641509 0.98113208 0.99530516 0.99061033 0.98122066 0.98122066] mean value: 0.9877602090530606 key: test_accuracy value: [0.89583333 0.91666667 0.87234043 0.89361702 0.78723404 0.93617021 0.82978723 0.82978723 0.80851064 0.93617021] mean value: 0.8706117021276596 key: train_accuracy value: [0.97877358 0.97169811 0.97647059 0.96470588 0.96235294 0.96470588 0.97411765 0.97411765 0.96470588 0.96235294] mean value: 0.9694001109877914 key: test_roc_auc value: [0.89583333 0.91666667 0.87137681 0.89130435 0.78804348 0.93478261 0.83242754 0.83152174 0.80887681 0.9375 ] mean value: 0.8708333333333333 key: train_roc_auc value: [0.97877358 0.97169811 0.97651475 0.96474444 0.96238595 0.96474444 0.97406768 0.97407875 0.96466693 0.96230844] mean value: 0.9693983080875188 key: test_jcc value: [0.82142857 0.84615385 0.78571429 0.82758621 0.64285714 0.88888889 0.73333333 0.72413793 0.67857143 0.88461538] mean value: 0.7833287019493916 key: train_jcc value: [0.95909091 0.94642857 0.95475113 0.93273543 0.92825112 0.93273543 0.95067265 0.95045045 0.93303571 0.92888889] mean value: 0.9417040284200333 key: TN value: 193 mean value: 193.0 key: FP value: 18 mean value: 18.0 key: FN value: 43 mean value: 43.0 key: TP value: 218 mean value: 218.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.07 Accuracy on Blind test: 0.68 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.57085848 0.55797744 0.54987121 0.55645442 0.55771732 0.56728172 0.56024218 0.55635619 0.56131268 0.5593884 ] mean value: 0.5597460031509399 key: score_time value: [0.00907898 0.00924873 0.0092032 0.00910139 0.00924087 0.01006508 0.01004052 0.00920558 0.00913525 0.00903201] mean value: 0.009335160255432129 key: test_mcc value: [1. 0.9591663 0.95833333 0.95825929 1. 0.91804649 0.95833333 1. 0.91833182 1. ] mean value: 0.967047057733998 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.97959184 0.9787234 0.97959184 1. 0.96 0.9787234 1. 0.95833333 1. ] mean value: 0.983496381531336 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.96 1. 0.96 1. 0.92307692 0.95833333 1. 0.92 1. ] mean value: 0.9721410256410257 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.95833333 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9958333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.97916667 0.9787234 0.9787234 1. 0.95744681 0.9787234 1. 0.95744681 1. ] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") 0.98302304964539 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.97916667 0.97916667 0.97826087 1. 0.95652174 0.97916667 1. 0.95833333 1. ] mean value: 0.9830615942028986 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.96 0.95833333 0.96 1. 0.92307692 0.95833333 1. 0.92 1. ] mean value: 0.9679743589743589 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 229 mean value: 229.0 key: FP value: 1 mean value: 1.0 key: FN value: 7 mean value: 7.0 key: TP value: 235 mean value: 235.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.85 Accuracy on Blind test: 0.95 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02258968 0.02946877 0.02697659 0.02740622 0.04164696 0.03037763 0.04019165 0.04432678 0.02746391 0.02753806] mean value: 0.03179862499237061 key: score_time value: [0.01176834 0.01242232 0.01303983 0.01382589 0.01480007 0.01367903 0.01615787 0.02514005 0.014853 0.01391315] mean value: 0.014959955215454101 key: test_mcc value: [1. 0.91986621 0.82971014 1. 0.95833333 1. 0.91833182 0.95825929 1. 1. ] mean value: 0.9584500801604692 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.95652174 0.91666667 1. 0.9787234 1. 0.95833333 0.97777778 1. 1. ] mean value: 0.9788022921163531 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.91666667 1. 1. 1. 0.92 1. 1. 1. ] mean value: 0.9836666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.91666667 0.91666667 1. 0.95833333 1. 1. 0.95652174 1. 1. ] mean value: 0.9748188405797101 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.95833333 0.91489362 1. 0.9787234 1. 0.95744681 0.9787234 1. 1. ] mean value: 0.9788120567375886 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.95833333 0.91485507 1. 0.97916667 1. 0.95833333 0.97826087 1. 1. ] mean value: 0.978894927536232 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.91666667 0.84615385 1. 0.95833333 1. 0.92 0.95652174 1. 1. ] mean value: 0.9597675585284282 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 233 mean value: 233.0 key: FP value: 6 mean value: 6.0 key: FN value: 3 mean value: 3.0 key: TP value: 230 mean value: 230.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: -0.04 Accuracy on Blind test: 0.78 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.0237093 0.03062272 0.03110003 0.03046179 0.03772449 0.03272963 0.03654647 0.03618479 0.05121851 0.03163123] mean value: 0.034192895889282225 key: score_time value: [0.02175546 0.02137756 0.02146101 0.02175856 0.0223124 0.02355242 0.02141953 0.02142358 0.02148175 0.02138925] mean value: 0.021793150901794435 key: test_mcc value: [1. 0.9591663 0.91485507 0.87917396 0.87318841 0.78804348 1. 0.95833333 0.91833182 0.95833333] mean value: 0.9249425713309061 key: train_mcc value: [0.97668677 0.97668677 0.97674215 0.98135106 0.96715612 0.9576579 0.9767396 0.98134942 0.98134942 0.9767396 ] mean value: 0.9752458821450679 key: test_fscore value: [1. 0.97959184 0.95833333 0.94117647 0.93617021 0.89361702 1. 0.9787234 0.95833333 0.9787234 ] mean value: 0.9624669016542787 key: train_fscore value: [0.98834499 0.98834499 0.98834499 0.99065421 0.98360656 0.97882353 0.98839907 0.99069767 0.99069767 0.98839907] mean value: 0.9876312750119973 key: test_precision value: [1. 0.96 0.95833333 0.88888889 0.95652174 0.91304348 1. 0.95833333 0.92 0.95833333] mean value: 0.9513454106280195 key: train_precision value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97674419 0.97652582 0.97706422 0.98156682 0.98156682 0.97706422] mean value: 0.9782889146081072 key: test_recall value: [1. 1. 0.95833333 1. 0.91666667 0.875 1. 1. 1. 1. ] mean value: 0.975 key: train_recall value: [1. 1. 1. 1. 0.99056604 0.98113208 1. 1. 1. 1. ] mean value: 0.9971698113207548 key: test_accuracy value: [1. 0.97916667 0.95744681 0.93617021 0.93617021 0.89361702 1. 0.9787234 0.95744681 0.9787234 ] mean value: 0.9617464539007093 key: train_accuracy value: [0.98820755 0.98820755 0.98823529 0.99058824 0.98352941 0.97882353 0.98823529 0.99058824 0.99058824 0.98823529] mean value: 0.9875238623751388 key: test_roc_auc value: [1. 0.97916667 0.95742754 0.93478261 0.9365942 0.89402174 1. 0.97916667 0.95833333 0.97916667] mean value: 0.9618659420289856 key: train_roc_auc value: [0.98820755 0.98820755 0.98826291 0.99061033 0.98354593 0.97882895 0.98820755 0.99056604 0.99056604 0.98820755] mean value: 0.987521038178758 key: test_jcc value: [1. 0.96 0.92 0.88888889 0.88 0.80769231 1. 0.95833333 0.92 0.95833333] mean value: 0.9293247863247863 key: train_jcc value: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:282: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:283: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_CV['Resampling'] = rs_ros /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:288: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:289: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_BT['Resampling'] = rs_ros [0.97695853 0.97695853 0.97695853 0.98148148 0.96774194 0.95852535 0.97706422 0.98156682 0.98156682 0.97706422] mean value: 0.9755886419544307 key: TN value: 224 mean value: 224.0 key: FP value: 6 mean value: 6.0 key: FN value: 12 mean value: 12.0 key: TP value: 230 mean value: 230.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.78 Accuracy on Blind test: 0.92 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.14134121 0.1367569 0.25494242 0.31138849 0.2787056 0.21976233 0.18788242 0.32086945 0.32341385 0.30260015] mean value: 0.24776628017425537 key: score_time value: [0.01222801 0.0124011 0.01748705 0.02304387 0.02139544 0.01204228 0.0219698 0.02384806 0.02312064 0.02127814] mean value: 0.01888144016265869 key: test_mcc value: [1. 0.9591663 0.91485507 0.87917396 0.87318841 0.78804348 1. 0.95833333 0.91833182 0.95833333] mean value: 0.9249425713309061 key: train_mcc value: [0.97668677 0.97668677 0.97674215 0.98135106 0.96715612 0.9576579 0.9767396 0.98134942 0.98134942 0.9767396 ] mean value: 0.9752458821450679 key: test_fscore value: [1. 0.97959184 0.95833333 0.94117647 0.93617021 0.89361702 1. 0.9787234 0.95833333 0.9787234 ] mean value: 0.9624669016542787 key: train_fscore value: [0.98834499 0.98834499 0.98834499 0.99065421 0.98360656 0.97882353 0.98839907 0.99069767 0.99069767 0.98839907] mean value: 0.9876312750119973 key: test_precision value: [1. 0.96 0.95833333 0.88888889 0.95652174 0.91304348 1. 0.95833333 0.92 0.95833333] mean value: 0.9513454106280195 key: train_precision value: [0.97695853 0.97695853 0.97695853 0.98148148 0.97674419 0.97652582 0.97706422 0.98156682 0.98156682 0.97706422] mean value: 0.9782889146081072 key: test_recall value: [1. 1. 0.95833333 1. 0.91666667 0.875 1. 1. 1. 1. ] mean value: 0.975 key: train_recall value: [1. 1. 1. 1. 0.99056604 0.98113208 1. 1. 1. 1. ] mean value: 0.9971698113207548 key: test_accuracy value: [1. 0.97916667 0.95744681 0.93617021 0.93617021 0.89361702 1. 0.9787234 0.95744681 0.9787234 ] mean value: 0.9617464539007093 key: train_accuracy value: [0.98820755 0.98820755 0.98823529 0.99058824 0.98352941 0.97882353 0.98823529 0.99058824 0.99058824 0.98823529] mean value: 0.9875238623751388 key: test_roc_auc value: [1. 0.97916667 0.95742754 0.93478261 0.9365942 0.89402174 1. 0.97916667 0.95833333 0.97916667] mean value: 0.9618659420289856 key: train_roc_auc value: [0.98820755 0.98820755 0.98826291 0.99061033 0.98354593 0.97882895 0.98820755 0.99056604 0.99056604 0.98820755] mean value: 0.987521038178758 key: test_jcc value: [1. 0.96 0.92 0.88888889 0.88 0.80769231 1. 0.95833333 0.92 0.95833333] mean value: 0.9293247863247863 key: train_jcc value: [0.97695853 0.97695853 0.97695853 0.98148148 0.96774194 0.95852535 0.97706422 0.98156682 0.98156682 0.97706422] mean value: 0.9755886419544307 key: TN value: 224 mean value: 224.0 key: FP value: 6 mean value: 6.0 key: FN value: 12 mean value: 12.0 key: TP value: 230 mean value: 230.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.78 Accuracy on Blind test: 0.92 PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( fit_time value: [0.02548289 0.02497411 0.02377176 0.02586055 0.02631164 0.02622843 0.02287912 0.02469206 0.02876091 0.02566028] mean value: 0.02546217441558838 key: score_time value: [0.01147556 0.01142359 0.01143599 0.01148558 0.01146889 0.01144385 0.01146317 0.01166201 0.01145411 0.01142836] mean value: 0.011474108695983887 key: test_mcc value: [0.54761905 0.41475753 0.21957752 1. 0.7200823 0.73192505 0.50709255 0.54761905 0.50709255 1. ] mean value: 0.6195765602863357 key: train_mcc value: [0.87950384 0.93042952 0.89564428 0.91318814 0.89615538 0.89775899 0.91316192 0.89615538 0.87944107 0.87944107] mean value: 0.8980879593254129 key: test_fscore value: [0.76923077 0.71428571 0.54545455 1. 0.875 0.83333333 0.6 0.76923077 0.72727273 1. ] mean value: 0.783380785880786 key: train_fscore value: [0.9380531 0.96551724 0.94827586 0.95652174 0.94642857 0.94545455 0.95575221 0.94642857 0.93913043 0.93913043] mean value: 0.9480692710190131 key: test_precision value: [0.71428571 0.625 0.6 1. 0.77777778 1. 1. 0.83333333 0.8 1. ] mean value: 0.8350396825396824 key: train_precision value: [0.96363636 0.96551724 0.94827586 0.96491228 0.96363636 0.98113208 0.96428571 0.96363636 0.94736842 0.94736842] mean value: 0.9609769106921797 key: test_recall value: [0.83333333 0.83333333 0.5 1. 1. 0.71428571 0.42857143 0.71428571 0.66666667 1. ] mean value: 0.7690476190476191 key: train_recall value: [0.9137931 0.96551724 0.94827586 0.94827586 0.92982456 0.9122807 0.94736842 0.92982456 0.93103448 0.93103448] mean value: 0.9357229280096794 key: test_accuracy value: [0.76923077 0.69230769 0.61538462 1. 0.84615385 0.84615385 0.69230769 0.76923077 0.75 1. ] mean value: 0.7980769230769231 key: train_accuracy value: [0.93913043 0.96521739 0.94782609 0.95652174 0.94782609 0.94782609 0.95652174 0.94782609 0.93965517 0.93965517] mean value: 0.94880059970015 key: test_roc_auc value: [0.77380952 0.70238095 0.60714286 1. 0.83333333 0.85714286 0.71428571 0.77380952 0.75 1. ] mean value: 0.8011904761904762 key: train_roc_auc value: [0.93935269 0.96521476 0.94782214 0.95659407 0.9476709 0.94751966 0.95644283 0.9476709 0.93965517 0.93965517] mean value: 0.9487598306110103 key: test_jcc value: [0.625 0.55555556 0.375 1. 0.77777778 0.71428571 0.42857143 0.625 0.57142857 1. ] mean value: 0.6672619047619047 key: train_jcc value: [0.88333333 0.93333333 0.90163934 0.91666667 0.89830508 0.89655172 0.91525424 0.89830508 0.8852459 0.8852459 ] mean value: 0.9013880611791908 key: TN value: 53 mean value: 53.0 key: FP value: 15 mean value: 15.0 key: FN value: 11 mean value: 11.0 key: TP value: 49 mean value: 49.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.59 Accuracy on Blind test: 0.8 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.55759573 0.70166278 0.56455135 0.56107998 0.57032299 0.6551044 0.56000018 0.56036115 0.56738853 0.66849685] mean value: 0.5966563940048217 key: score_time value: [0.0117178 0.01296282 0.01171088 0.01284122 0.01298141 0.01283264 0.012954 0.01287794 0.012851 0.01291609] mean value: 0.012664580345153808 key: test_mcc value: [0.73192505 0.85714286 0.73192505 1. 0.53674504 0.41475753 0.73192505 0.54761905 0.84515425 0.84515425] mean value: 0.7242348149477957 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.85714286 0.92307692 0.85714286 1. 0.8 0.66666667 0.83333333 0.76923077 0.92307692 0.92307692] mean value: 0.8552747252747253 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.85714286 0.75 1. 0.75 0.8 1. 0.83333333 0.85714286 0.85714286] mean value: 0.8454761904761904 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 0.85714286 0.57142857 0.71428571 0.71428571 1. 1. ] mean value: 0.8857142857142858 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.84615385 0.92307692 0.84615385 1. 0.76923077 0.69230769 0.84615385 0.76923077 0.91666667 0.91666667] mean value: 0.8525641025641025 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.85714286 0.92857143 0.85714286 1. 0.76190476 0.70238095 0.85714286 0.77380952 0.91666667 0.91666667] mean value: 0.8571428571428573 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.75 0.85714286 0.75 1. 0.66666667 0.5 0.71428571 0.625 0.85714286 0.85714286] mean value: 0.7577380952380952 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 53 mean value: 53.0 key: FP value: 7 mean value: 7.0 key: FN value: 11 mean value: 11.0 key: TP value: 57 mean value: 57.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.65 Accuracy on Blind test: 0.84 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01163244 0.01148558 0.00906825 0.00943017 0.00903463 0.00944757 0.00886846 0.00889182 0.00922656 0.00919008] mean value: 0.009627556800842286 key: score_time value: [0.01144195 0.01121354 0.00962234 0.00908494 0.00913692 0.00922203 0.00858068 0.00846982 0.0084188 0.00912619] mean value: 0.009431719779968262 key: test_mcc value: [ 0.22537447 0.53674504 0.21957752 0.23809524 0.53674504 0.09759001 0.50709255 -0.23809524 0.84515425 0.50709255] mean value: 0.34753714322820006 key: train_mcc value: [0.65827364 0.69801188 0.51354191 0.59642005 0.60560491 0.62065383 0.67489964 0.52793171 0.6457464 0.6040687 ] mean value: 0.6145152675573603 key: test_fscore value: [0.44444444 0.72727273 0.54545455 0.61538462 0.8 0.5 0.6 0.42857143 0.90909091 0.72727273] mean value: 0.6297491397491397 key: train_fscore value: [0.79207921 0.83018868 0.65934066 0.76923077 0.75510204 0.76767677 0.84297521 0.72 0.78 0.76470588] mean value: 0.7681299213195109 key: test_precision value: [0.66666667 0.8 0.6 0.57142857 0.75 0.6 1. 0.42857143 1. 0.8 ] mean value: 0.7216666666666666 key: train_precision value: [0.93023256 0.91666667 0.90909091 0.86956522 0.90243902 0.9047619 0.796875 0.8372093 0.92857143 0.88636364] mean value: 0.8881775647701209 key: test_recall value: [0.33333333 0.66666667 0.5 0.66666667 0.85714286 0.42857143 0.42857143 0.42857143 0.83333333 0.66666667] mean value: 0.5809523809523809 key: train_recall value: [0.68965517 0.75862069 0.51724138 0.68965517 0.64912281 0.66666667 0.89473684 0.63157895 0.67241379 0.67241379] mean value: 0.6842105263157895 key: test_accuracy value: [0.61538462 0.76923077 0.61538462 0.61538462 0.76923077 0.53846154 0.69230769 0.38461538 0.91666667 0.75 ] mean value: 0.6666666666666667 key: train_accuracy value: [0.8173913 0.84347826 0.73043478 0.79130435 0.79130435 0.8 0.83478261 0.75652174 0.81034483 0.79310345] mean value: 0.7968665667166417 key: test_roc_auc value: [0.5952381 0.76190476 0.60714286 0.61904762 0.76190476 0.54761905 0.71428571 0.38095238 0.91666667 0.75 ] mean value: 0.6654761904761906 key: train_roc_auc value: [0.8185118 0.84422263 0.7323049 0.79219601 0.79007864 0.79885057 0.83529946 0.75544465 0.81034483 0.79310345] mean value: 0.7970356926799758 key: test_jcc value: [0.28571429 0.57142857 0.375 0.44444444 0.66666667 0.33333333 0.42857143 0.27272727 0.83333333 0.57142857] mean value: 0.47826479076479067 key: train_jcc value: [0.6557377 0.70967742 0.49180328 0.625 0.60655738 0.62295082 0.72857143 0.5625 0.63934426 0.61904762] mean value: 0.6261189909596837 key: TN value: 48 mean value: 48.0 key: FP value: 27 mean value: 27.0 key: FN value: 16 mean value: 16.0 key: TP value: 37 mean value: 37.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.27 Accuracy on Blind test: 0.72 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00853348 0.00885534 0.00906277 0.0089674 0.00840831 0.00923443 0.00851059 0.00884318 0.00853682 0.00857449] mean value: 0.008752679824829102 key: score_time value: [0.00847602 0.00842738 0.00847268 0.00882149 0.00835872 0.00905609 0.00877452 0.00874686 0.00880098 0.0091145 ] mean value: 0.008704924583435058 key: test_mcc value: [ 0.38575837 0.09759001 0.21957752 -0.09759001 -0.07142857 -0.07142857 0.09759001 0.50709255 -0.19245009 0.35355339] mean value: 0.12282646094568686 key: train_mcc value: [0.52166881 0.43213628 0.42809663 0.53276944 0.49667181 0.52793171 0.49561273 0.5202221 0.53647994 0.54103611] mean value: 0.5032625556468886 key: test_fscore value: [0.6 0.57142857 0.54545455 0.36363636 0.46153846 0.46153846 0.5 0.6 0.22222222 0.6 ] mean value: 0.4925818625818626 key: train_fscore value: [0.73584906 0.69158879 0.7027027 0.75675676 0.69387755 0.72 0.74336283 0.73076923 0.75675676 0.74766355] mean value: 0.7279327222916634 key: test_precision value: [0.75 0.5 0.6 0.4 0.5 0.5 0.6 1. 0.33333333 0.75 ] mean value: 0.5933333333333333 key: train_precision value: [0.8125 0.75510204 0.73584906 0.79245283 0.82926829 0.8372093 0.75 0.80851064 0.79245283 0.81632653] mean value: 0.7929671521716084 key: test_recall value: [0.5 0.66666667 0.5 0.33333333 0.42857143 0.42857143 0.42857143 0.42857143 0.16666667 0.5 ] mean value: 0.43809523809523804 key: train_recall value: [0.67241379 0.63793103 0.67241379 0.72413793 0.59649123 0.63157895 0.73684211 0.66666667 0.72413793 0.68965517] mean value: 0.6752268602540834 key: test_accuracy value: [0.69230769 0.53846154 0.61538462 0.46153846 0.46153846 0.46153846 0.53846154 0.69230769 0.41666667 0.66666667] mean value: 0.5544871794871795 key: train_accuracy value: [0.75652174 0.71304348 0.71304348 0.76521739 0.73913043 0.75652174 0.74782609 0.75652174 0.76724138 0.76724138] mean value: 0.7482308845577211 key: test_roc_auc value: [0.67857143 0.54761905 0.60714286 0.45238095 0.46428571 0.46428571 0.54761905 0.71428571 0.41666667 0.66666667] mean value: 0.555952380952381 key: train_roc_auc value: [0.75725953 0.71370236 0.71339988 0.76557774 0.73790079 0.75544465 0.7477314 0.75574713 0.76724138 0.76724138] mean value: 0.7481246218995766 key: test_jcc value: [0.42857143 0.4 0.375 0.22222222 0.3 0.3 0.33333333 0.42857143 0.125 0.42857143] mean value: 0.3341269841269841 key: train_jcc value: [0.58208955 0.52857143 0.54166667 0.60869565 0.53125 0.5625 0.5915493 0.57575758 0.60869565 0.59701493] mean value: 0.5727790748730086 key: TN value: 43 mean value: 43.0 key: FP value: 36 mean value: 36.0 key: FN value: 21 mean value: 21.0 key: TP value: 28 mean value: 28.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.05 Accuracy on Blind test: 0.54 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00907969 0.00883484 0.00880814 0.00789714 0.00841284 0.00867152 0.00813246 0.00892329 0.00887847 0.00889778] mean value: 0.008653616905212403 key: score_time value: [0.00998592 0.00991344 0.0096209 0.00929761 0.00940967 0.00916767 0.01009822 0.01010013 0.01001501 0.00996399] mean value: 0.009757256507873536 key: test_mcc /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( value: [-0.23809524 0.54761905 0.05143445 0.53674504 0.38095238 0.50709255 0.28288947 0.14085904 0.16903085 0.50709255] mean value: 0.2885620154590426 key: train_mcc value: [0.48354418 0.46484968 0.52541766 0.43847995 0.47616964 0.41195157 0.48484331 0.44946451 0.40690442 0.38769906] mean value: 0.45293239771525967 key: test_fscore value: [0.33333333 0.76923077 0.4 0.72727273 0.71428571 0.6 0.54545455 0.4 0.54545455 0.72727273] mean value: 0.5762304362304362 key: train_fscore value: [0.72222222 0.71559633 0.73076923 0.67961165 0.68686869 0.67924528 0.71153846 0.69230769 0.66019417 0.65384615] mean value: 0.6932199886089263 key: test_precision value: [0.33333333 0.71428571 0.5 0.8 0.71428571 1. 0.75 0.66666667 0.6 0.8 ] mean value: 0.6878571428571428 key: train_precision value: [0.78 0.76470588 0.82608696 0.77777778 0.80952381 0.73469388 0.78723404 0.76595745 0.75555556 0.73913043] mean value: 0.7740665783427154 key: test_recall value: [0.33333333 0.83333333 0.33333333 0.66666667 0.71428571 0.42857143 0.42857143 0.28571429 0.5 0.66666667] mean value: 0.5190476190476191 key: train_recall value: [0.67241379 0.67241379 0.65517241 0.60344828 0.59649123 0.63157895 0.64912281 0.63157895 0.5862069 0.5862069 ] mean value: 0.6284633998790079 key: test_accuracy value: [0.38461538 0.76923077 0.53846154 0.76923077 0.69230769 0.69230769 0.61538462 0.53846154 0.58333333 0.75 ] mean value: 0.6333333333333333 key: train_accuracy value: [0.73913043 0.73043478 0.75652174 0.71304348 0.73043478 0.70434783 0.73913043 0.72173913 0.69827586 0.68965517] mean value: 0.7222713643178411 key: test_roc_auc value: [0.38095238 0.77380952 0.52380952 0.76190476 0.69047619 0.71428571 0.63095238 0.55952381 0.58333333 0.75 ] mean value: 0.636904761904762 key: train_roc_auc value: [0.73971567 0.73094374 0.75741077 0.71400484 0.7292801 0.70372051 0.73835451 0.72096189 0.69827586 0.68965517] mean value: 0.7222323049001815 key: test_jcc value: [0.2 0.625 0.25 0.57142857 0.55555556 0.42857143 0.375 0.25 0.375 0.57142857] mean value: 0.4201984126984127 key: train_jcc value: [0.56521739 0.55714286 0.57575758 0.51470588 0.52307692 0.51428571 0.55223881 0.52941176 0.49275362 0.48571429] mean value: 0.5310304823499082 key: TN value: 48 mean value: 48.0 key: FP value: 31 mean value: 31.0 key: FN value: 16 mean value: 16.0 key: TP value: 33 mean value: 33.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.03 Accuracy on Blind test: 0.56 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.00929236 0.0091424 0.00922894 0.00920606 0.00914025 0.00913072 0.00918317 0.00913858 0.00915313 0.00918818] mean value: 0.009180378913879395 key: score_time value: [0.00859475 0.00858831 0.00861931 0.00862646 0.00869465 0.0085423 0.00871539 0.00858855 0.00859594 0.00857806] mean value: 0.008614373207092286 key: test_mcc value: [ 0.07142857 0.23809524 0.22537447 0.21957752 0.38575837 0.41475753 0.28288947 -0.07142857 0.50709255 0.66666667] mean value: 0.2940211822778897 key: train_mcc value: [0.63744225 0.65603842 0.63744225 0.56987466 0.60692685 0.58540009 0.60404888 0.63913253 0.60789179 0.57594167] mean value: 0.6120139388631578 key: test_fscore value: [0.5 0.61538462 0.44444444 0.54545455 0.75 0.66666667 0.54545455 0.46153846 0.72727273 0.83333333] mean value: 0.6089549339549339 key: train_fscore value: [0.81081081 0.81818182 0.81081081 0.7706422 0.78095238 0.77777778 0.78504673 0.80373832 0.78899083 0.76635514] mean value: 0.7913306812972423 key: test_precision value: [0.5 0.57142857 0.66666667 0.6 0.66666667 0.8 0.75 0.5 0.8 0.83333333] mean value: 0.6688095238095237 key: train_precision value: [0.8490566 0.86538462 0.8490566 0.82352941 0.85416667 0.82352941 0.84 0.86 0.84313725 0.83673469] mean value: 0.8444595261907375 key: test_recall value: [0.5 0.66666667 0.33333333 0.5 0.85714286 0.57142857 0.42857143 0.42857143 0.66666667 0.83333333] mean value: 0.5785714285714285 key: train_recall value: [0.77586207 0.77586207 0.77586207 0.72413793 0.71929825 0.73684211 0.73684211 0.75438596 0.74137931 0.70689655] mean value: 0.7447368421052631 key: test_accuracy value: [0.53846154 0.61538462 0.61538462 0.61538462 0.69230769 0.69230769 0.61538462 0.46153846 0.75 0.83333333] mean value: 0.642948717948718 key: train_accuracy value: [0.8173913 0.82608696 0.8173913 0.7826087 0.8 0.79130435 0.8 0.8173913 0.80172414 0.78448276] mean value: 0.8038380809595204 key: test_roc_auc value: [0.53571429 0.61904762 0.5952381 0.60714286 0.67857143 0.70238095 0.63095238 0.46428571 0.75 0.83333333] mean value: 0.6416666666666666 key: train_roc_auc value: [0.8177556 0.82652753 0.8177556 0.7831216 0.7993043 0.79083485 0.79945554 0.81684815 0.80172414 0.78448276] mean value: 0.8037810042347248 key: test_jcc value: [0.33333333 0.44444444 0.28571429 0.375 0.6 0.5 0.375 0.3 0.57142857 0.71428571] mean value: 0.44992063492063494 key: train_jcc value: [0.68181818 0.69230769 0.68181818 0.62686567 0.640625 0.63636364 0.64615385 0.671875 0.65151515 0.62121212] mean value: 0.6550554482830602 key: TN value: 45 mean value: 45.0 key: FP value: 27 mean value: 27.0 key: FN value: 19 mean value: 19.0 key: TP value: 37 mean value: 37.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.23 Accuracy on Blind test: 0.65 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.53628516 0.62847281 0.5407598 0.60236335 0.53843927 0.60092044 0.54009676 0.54076219 0.54493165 0.71171832] mean value: 0.5784749746322632/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( key: score_time value: [0.01352882 0.01294923 0.01417613 0.01308346 0.01450157 0.01442575 0.0144608 0.0145483 0.01314163 0.01451683] mean value: 0.013933253288269044 key: test_mcc value: [0.07142857 0.23809524 0.21957752 1. 0.22537447 0.09759001 0.28288947 0.41475753 0.50709255 0.50709255] mean value: 0.3563897912767536 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.61538462 0.54545455 1. 0.70588235 0.5 0.54545455 0.66666667 0.76923077 0.76923077] mean value: 0.6617304264363087 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.57142857 0.6 1. 0.6 0.6 0.75 0.8 0.71428571 0.71428571] mean value: 0.685 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.66666667 0.5 1. 0.85714286 0.42857143 0.42857143 0.57142857 0.83333333 0.83333333] mean value: 0.6619047619047619 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.53846154 0.61538462 0.61538462 1. 0.61538462 0.53846154 0.61538462 0.69230769 0.75 0.75 ] mean value: 0.6730769230769231 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.53571429 0.61904762 0.60714286 1. 0.5952381 0.54761905 0.63095238 0.70238095 0.75 0.75 ] mean value: 0.6738095238095239 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0.44444444 0.375 1. 0.54545455 0.33333333 0.375 0.5 0.625 0.625 ] mean value: 0.5156565656565657 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 44 mean value: 44.0 key: FP value: 22 mean value: 22.0 key: FN value: 20 mean value: 20.0 key: TP value: 42 mean value: 42.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.4 Accuracy on Blind test: 0.71 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01383662 0.01364303 0.01029849 0.01006365 0.00987554 0.00922799 0.00956511 0.00956154 0.0096581 0.00960445] mean value: 0.010533452033996582 key: score_time value: [0.01158452 0.00933385 0.00867271 0.00883508 0.00839686 0.00836468 0.00835061 0.00836229 0.00824571 0.00836349] mean value: 0.008850979804992675 key: test_mcc value: [0.85714286 1. 0.85714286 0.85714286 1. 0.85714286 1. 0.69047619 1. 1. ] mean value: 0.9119047619047619 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.92307692 1. 0.92307692 0.92307692 1. 0.92307692 1. 0.85714286 1. 1. ] mean value: 0.9549450549450549 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 1. 0.85714286 0.85714286 1. 1. 1. 0.85714286 1. 1. ] mean value: 0.9428571428571428 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 0.85714286 1. 0.85714286 1. 1. ] mean value: 0.9714285714285715 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.92307692 1. 0.92307692 0.92307692 1. 0.92307692 1. 0.84615385 1. 1. ] mean value: 0.9538461538461538 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.92857143 1. 0.92857143 0.92857143 1. 0.92857143 1. 0.8452381 1. 1. ] mean value: 0.955952380952381 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.85714286 1. 0.85714286 0.85714286 1. 0.85714286 1. 0.75 1. 1. ] mean value: 0.9178571428571429 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 60 mean value: 60.0 key: FP value: 2 mean value: 2.0 key: FN value: 4 mean value: 4.0 key: TP value: 62 mean value: 62.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.87 Accuracy on Blind test: 0.95 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.08474231 0.08478355 0.08375072 0.08426261 0.08383918 0.08362508 0.08540273 0.08369732 0.08442283 0.08440924] mean value: 0.0842935562133789 key: score_time value: [0.01687574 0.01674438 0.01730251 0.01674294 0.01672387 0.0167408 0.016819 0.01675367 0.01675248 0.01690674] mean value: 0.016836214065551757 key: test_mcc value: [0.07142857 0.6172134 0.59160798 0.54761905 0.22537447 0.41475753 0.14085904 0.41475753 0.84515425 0.66666667] mean value: 0.45354384909897416 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.8 0.66666667 0.76923077 0.70588235 0.66666667 0.4 0.66666667 0.92307692 0.83333333] mean value: 0.6931523378582203 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.66666667 1. 0.71428571 0.6 0.8 0.66666667 0.8 0.85714286 0.83333333] mean value: 0.7438095238095237 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 1. 0.5 0.83333333 0.85714286 0.57142857 0.28571429 0.57142857 1. 0.83333333] mean value: 0.6952380952380952 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.53846154 0.76923077 0.76923077 0.76923077 0.61538462 0.69230769 0.53846154 0.69230769 0.91666667 0.83333333] mean value: 0.7134615384615385 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.53571429 0.78571429 0.75 0.77380952 0.5952381 0.70238095 0.55952381 0.70238095 0.91666667 0.83333333] mean value: 0.7154761904761905 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0.66666667 0.5 0.625 0.54545455 0.5 0.25 0.5 0.85714286 0.71428571] mean value: 0.5491883116883117 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 47 mean value: 47.0 key: FP value: 20 mean value: 20.0 key: FN value: 17 mean value: 17.0 key: TP value: 44 mean value: 44.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.46 Accuracy on Blind test: 0.74 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00844145 0.00862551 0.00836611 0.00838566 0.00852084 0.0095427 0.00830698 0.0083344 0.00922632 0.00840116] mean value: 0.0086151123046875 key: score_time value: [0.00828457 0.00885868 0.00828385 0.0083971 0.00831819 0.00866413 0.00849199 0.00838947 0.00894332 0.00846314] mean value: 0.008509445190429687 key: test_mcc value: [ 0.6172134 1. 0.46056619 0.07142857 0.05143445 -0.28288947 -0.22537447 0.38095238 0.33333333 0. ] mean value: 0.24066643791637493 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 1. 0.5 0.5 0.625 0.5 0.2 0.71428571 0.66666667 0.57142857] mean value: 0.6077380952380953 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 1. 1. 0.5 0.55555556 0.44444444 0.33333333 0.71428571 0.66666667 0.5 ] mean value: 0.638095238095238 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.33333333 0.5 0.71428571 0.57142857 0.14285714 0.71428571 0.66666667 0.66666667] mean value: 0.630952380952381 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.76923077 1. 0.69230769 0.53846154 0.53846154 0.38461538 0.38461538 0.69230769 0.66666667 0.5 ] mean value: 0.6166666666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.78571429 1. 0.66666667 0.53571429 0.52380952 0.36904762 0.4047619 0.69047619 0.66666667 0.5 ] mean value: 0.6142857142857143 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 1. 0.33333333 0.33333333 0.45454545 0.33333333 0.11111111 0.55555556 0.5 0.4 ] mean value: 0.46878787878787875 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 39 mean value: 39.0 key: FP value: 24 mean value: 24.0 key: FN value: 25 mean value: 25.0 key: TP value: 40 mean value: 40.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.23 Accuracy on Blind test: 0.66 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.06711006 1.06243563 1.05919814 1.0622704 1.0625546 1.06830359 1.06534982 1.0697732 1.07362056 1.06692076] mean value: 1.0657536745071412 key: score_time value: [0.08754396 0.08738852 0.08666062 0.08688879 0.08696675 0.08656383 0.08840013 0.08690977 0.08692837 0.08670497] mean value: 0.08709557056427002 key: test_mcc value: [0.54761905 0.73192505 0.53674504 1. 0.7200823 0.73192505 0.69047619 0.6172134 0.84515425 0.84515425] mean value: 0.7266294596768228 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.76923077 0.85714286 0.72727273 1. 0.875 0.83333333 0.85714286 0.72727273 0.92307692 0.92307692] mean value: 0.8492549117549117 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.71428571 0.75 0.8 1. 0.77777778 1. 0.85714286 1. 0.85714286 0.85714286] mean value: 0.8613492063492062 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.83333333 1. 0.66666667 1. 1. 0.71428571 0.85714286 0.57142857 1. 1. ] mean value: 0.8642857142857142 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.76923077 0.84615385 0.76923077 1. 0.84615385 0.84615385 0.84615385 0.76923077 0.91666667 0.91666667] mean value: 0.8525641025641025 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.77380952 0.85714286 0.76190476 1. 0.83333333 0.85714286 0.8452381 0.78571429 0.91666667 0.91666667] mean value: 0.854761904761905 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.625 0.75 0.57142857 1. 0.77777778 0.71428571 0.75 0.57142857 0.85714286 0.85714286] mean value: 0.7474206349206348 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 54 mean value: 54.0 key: FP value: 9 mean value: 9.0 key: FN value: 10 mean value: 10.0 key: TP value: 55 mean value: 55.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.68 Accuracy on Blind test: 0.86 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.78958631 0.82710862 0.84518576 0.87769175 0.84533954 0.9362042 0.90117383 0.90355873 0.81010485 0.82837105] mean value: 0.8564324617385864 key: score_time value: [0.18188357 0.16762471 0.19198155 0.14597726 0.17890453 0.15681887 0.18019104 0.18178201 0.18352628 0.20134711] mean value: 0.17700369358062745 key: test_mcc value: [0.38095238 0.54761905 0.59160798 1. 0.7200823 0.73192505 0.73192505 0.6172134 0.84515425 0.84515425] mean value: 0.7011633725432687 key: train_mcc value: [0.96580942 0.96521476 0.96521476 0.98275345 0.98275862 0.96521476 0.98275862 0.98275862 0.98290472 0.96551724] mean value: 0.974090498263962 key: test_fscore value: [0.66666667 0.76923077 0.66666667 1. 0.875 0.83333333 0.83333333 0.72727273 0.92307692 0.92307692] mean value: 0.8217657342657343 key: train_fscore value: [0.98245614 0.98275862 0.98275862 0.99145299 0.99130435 0.98245614 0.99130435 0.99130435 0.99145299 0.98275862] mean value: 0.9870007169154963 key: test_precision value: [0.66666667 0.71428571 1. 1. 0.77777778 1. 1. 1. 0.85714286 0.85714286] mean value: 0.8873015873015874 key: train_precision value: [1. 0.98275862 0.98275862 0.98305085 0.98275862 0.98245614 0.98275862 0.98275862 0.98305085 0.98275862] mean value: 0.9845109559404062 key: test_recall value: [0.66666667 0.83333333 0.5 1. 1. 0.71428571 0.71428571 0.57142857 1. 1. ] mean value: 0.8 key: train_recall value: [0.96551724 0.98275862 0.98275862 1. 1. 0.98245614 1. 1. 1. 0.98275862] mean value: 0.9896249243799152 key: test_accuracy value: [0.69230769 0.76923077 0.76923077 1. 0.84615385 0.84615385 0.84615385 0.76923077 0.91666667 0.91666667] mean value: 0.8371794871794872 key: train_accuracy value: [0.9826087 0.9826087 0.9826087 0.99130435 0.99130435 0.9826087 0.99130435 0.99130435 0.99137931 0.98275862] mean value: 0.9869790104947527 key: test_roc_auc value: [0.69047619 0.77380952 0.75 1. 0.83333333 0.85714286 0.85714286 0.78571429 0.91666667 0.91666667] mean value: 0.8380952380952381 key: train_roc_auc value: [0.98275862 0.98260738 0.98260738 0.99122807 0.99137931 0.98260738 0.99137931 0.99137931 0.99137931 0.98275862] mean value: 0.9870084694494856 key: test_jcc value: [0.5 0.625 0.5 1. 0.77777778 0.71428571 0.71428571 0.57142857 0.85714286 0.85714286] mean value: 0.7117063492063491 key: train_jcc value: [0.96551724 0.96610169 0.96610169 0.98305085 0.98275862 0.96551724 0.98275862 0.98275862 0.98305085 0.96610169] mean value: 0.9743717124488602 key: TN value: 56 mean value: 56.0 key: FP value: 13 mean value: 13.0 key: FN value: 8 mean value: 8.0 key: TP value: 51 mean value: 51.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.73 Accuracy on Blind test: 0.89 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.0548141 0.03600359 0.03932428 0.03700233 0.03631186 0.03644156 0.03789878 0.03571916 0.03629661 0.03441095] mean value: 0.03842232227325439 key: score_time value: [0.01012206 0.00998211 0.01084924 0.01008987 0.01075172 0.01114345 0.01033568 0.01090884 0.01054478 0.01018047] mean value: 0.010490822792053222 key: test_mcc value: [1. 1. 0.85714286 1. 0.85714286 0.85714286 1. 0.85714286 1. 1. ] mean value: 0.9428571428571428 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 0.92307692 1. 0.92307692 0.92307692 1. 0.92307692 1. 1. ] mean value: 0.9692307692307691 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.85714286 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9857142857142858 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 0.85714286 0.85714286 1. 0.85714286 1. 1. ] mean value: 0.9571428571428571 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 0.92307692 1. 0.92307692 0.92307692 1. 0.92307692 1. 1. ] mean value: 0.9692307692307693 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.92857143 1. 0.92857143 0.92857143 1. 0.92857143 1. 1. ] mean value: 0.9714285714285715 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 0.85714286 1. 0.85714286 0.85714286 1. 0.85714286 1. 1. ] mean value: 0.9428571428571428 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 63 mean value: 63.0 key: FP value: 3 mean value: 3.0 key: FN value: 1 mean value: 1.0 key: TP value: 61 mean value: 61.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.84 Accuracy on Blind test: 0.94 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02421451 0.04412055 0.0320704 0.04514098 0.041291 0.04488754 0.03924036 0.05337286 0.05183053 0.04660392] mean value: 0.04227726459503174 key: score_time value: [0.0211246 0.02603841 0.02130747 0.01807022 0.02199841 0.02134991 0.02378583 0.02136731 0.02127481 0.02119875] mean value: 0.02175157070159912 key: test_mcc value: [-0.54761905 -0.09759001 0.59160798 0.69047619 0.38095238 0.50709255 0.14085904 0.41475753 0.35355339 -0.19245009] mean value: 0.22416399219830202 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.16666667 0.36363636 0.66666667 0.83333333 0.71428571 0.6 0.4 0.66666667 0.6 0.22222222] mean value: 0.5233477633477632 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.16666667 0.4 1. 0.83333333 0.71428571 1. 0.66666667 0.8 0.75 0.33333333] mean value: 0.6664285714285715 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.16666667 0.33333333 0.5 0.83333333 0.71428571 0.42857143 0.28571429 0.57142857 0.5 0.16666667] mean value: 0.45000000000000007 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.23076923 0.46153846 0.76923077 0.84615385 0.69230769 0.69230769 0.53846154 0.69230769 0.66666667 0.41666667] mean value: 0.6006410256410257 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.22619048 0.45238095 0.75 0.8452381 0.69047619 0.71428571 0.55952381 0.70238095 0.66666667 0.41666667] mean value: 0.6023809523809525 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.09090909 0.22222222 0.5 0.71428571 0.55555556 0.42857143 0.25 0.5 0.42857143 0.125 ] mean value: 0.381511544011544 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 48 mean value: 48.0 key: FP value: 35 mean value: 35.0 key: FN value: 16 mean value: 16.0 key: TP value: 29 mean value: 29.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.43 Accuracy on Blind test: 0.76 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01183629 0.01080275 0.00891304 0.00854683 0.00870848 0.00964117 0.0084188 0.00841784 0.00847912 0.00880694] mean value: 0.009257125854492187 key: score_time value: [0.01165819 0.00944376 0.0087254 0.00845647 0.00867558 0.00870895 0.00839353 0.00828433 0.00842071 0.00850129] mean value: 0.008926820755004884 key: test_mcc value: [0.23809524 0.28288947 0.05143445 0.54761905 0.53674504 0.73192505 0.50709255 0.09759001 0.66666667 0.35355339] mean value: 0.4013610922857148 key: train_mcc value: [0.52166881 0.48131767 0.48354418 0.39140956 0.5236969 0.50112101 0.4971185 0.48037511 0.49343516 0.41778637] mean value: 0.4791473276191696 key: test_fscore value: [0.61538462 0.66666667 0.4 0.76923077 0.8 0.83333333 0.6 0.5 0.83333333 0.71428571] mean value: 0.6732234432234432 key: train_fscore value: [0.73584906 0.72727273 0.72222222 0.69565217 0.7254902 0.72380952 0.73394495 0.72222222 0.71153846 0.68518519] mean value: 0.7183186722974032 key: test_precision value: [0.57142857 0.55555556 0.5 0.71428571 0.75 1. 1. 0.6 0.83333333 0.625 ] mean value: 0.7149603174603174 key: train_precision value: [0.8125 0.76923077 0.78 0.70175439 0.82222222 0.79166667 0.76923077 0.76470588 0.80434783 0.74 ] mean value: 0.7755658521755237 key: test_recall value: [0.66666667 0.83333333 0.33333333 0.83333333 0.85714286 0.71428571 0.42857143 0.42857143 0.83333333 0.83333333] mean value: 0.6761904761904761 key: train_recall value: [0.67241379 0.68965517 0.67241379 0.68965517 0.64912281 0.66666667 0.70175439 0.68421053 0.63793103 0.63793103] mean value: 0.6701754385964913 key: test_accuracy value: [0.61538462 0.61538462 0.53846154 0.76923077 0.76923077 0.84615385 0.69230769 0.53846154 0.83333333 0.66666667] mean value: 0.6884615384615385 key: train_accuracy value: [0.75652174 0.73913043 0.73913043 0.69565217 0.75652174 0.74782609 0.74782609 0.73913043 0.74137931 0.70689655] mean value: 0.7370014992503747 key: test_roc_auc value: [0.61904762 0.63095238 0.52380952 0.77380952 0.76190476 0.85714286 0.71428571 0.54761905 0.83333333 0.66666667] mean value: 0.6928571428571428 key: train_roc_auc value: [0.75725953 0.73956443 0.73971567 0.69570478 0.75559589 0.74712644 0.74742892 0.73865699 0.74137931 0.70689655] mean value: 0.7369328493647912 key: test_jcc value: [0.44444444 0.5 0.25 0.625 0.66666667 0.71428571 0.42857143 0.33333333 0.71428571 0.55555556] mean value: 0.5232142857142856 key: train_jcc value: [0.58208955 0.57142857 0.56521739 0.53333333 0.56923077 0.56716418 0.57971014 0.56521739 0.55223881 0.52112676] mean value: 0.5606756899405718 key: TN value: 45 mean value: 45.0 key: FP value: 21 mean value: 21.0 key: FN value: 19 mean value: 19.0 key: TP value: 43 mean value: 43.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.25 Accuracy on Blind test: 0.66 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01212406 0.01453185 0.01396585 0.01321959 0.01503253 0.01537943 0.01324081 0.01318336 0.01479745 0.01383924] mean value: 0.01393141746520996 key: score_time value: [0.00918651 0.01131415 0.01131535 0.01138783 0.01142979 0.01165056 0.01144242 0.01141739 0.01159096 0.01164055] mean value: 0.011237549781799316 key: test_mcc value: [0.41475753 0.6172134 0.38095238 0.7200823 0.7200823 0.41475753 0.6172134 0.54761905 0.70710678 1. ] mean value: 0.6139784671107156 key: train_mcc value: [0.98275345 1. 0.98275862 0.80942721 1. 1. 0.82362769 0.93272881 1. 0.93325653] mean value: 0.9464552313523782 key: test_fscore value: [0.71428571 0.8 0.66666667 0.8 0.875 0.66666667 0.72727273 0.76923077 0.85714286 1. ] mean value: 0.7876265401265401 key: train_fscore value: [0.99145299 1. 0.99130435 0.88461538 1. 1. 0.89320388 0.96610169 1. 0.96428571] mean value: 0.9690964016590577 key: test_precision value: [0.625 0.66666667 0.66666667 1. 0.77777778 0.8 1. 0.83333333 0.75 1. ] mean value: 0.8119444444444444 key: train_precision value: [0.98305085 1. 1. 1. 1. 1. 1. 0.93442623 1. 1. ] mean value: 0.9917477076965824 key: test_recall value: [0.83333333 1. 0.66666667 0.66666667 1. 0.57142857 0.57142857 0.71428571 1. 1. ] mean value: 0.8023809523809524 key: train_recall value: [1. 1. 0.98275862 0.79310345 1. 1. 0.80701754 1. 1. 0.93103448] mean value: 0.9513914095583788 key: test_accuracy value: [0.69230769 0.76923077 0.69230769 0.84615385 0.84615385 0.69230769 0.76923077 0.76923077 0.83333333 1. ] mean value: 0.791025641025641 key: train_accuracy value: [0.99130435 1. 0.99130435 0.89565217 1. 1. 0.90434783 0.96521739 1. 0.96551724] mean value: 0.9713343328335832 key: test_roc_auc value: [0.70238095 0.78571429 0.69047619 0.83333333 0.83333333 0.70238095 0.78571429 0.77380952 0.83333333 1. ] mean value: 0.7940476190476191 key: train_roc_auc value: [0.99122807 1. 0.99137931 0.89655172 1. 1. 0.90350877 0.96551724 1. 0.96551724] mean value: 0.9713702359346641 key: test_jcc value: [0.55555556 0.66666667 0.5 0.66666667 0.77777778 0.5 0.57142857 0.625 0.75 1. ] mean value: 0.6613095238095237 key: train_jcc value: [0.98305085 1. 0.98275862 0.79310345 1. 1. 0.80701754 0.93442623 1. 0.93103448] mean value: 0.9431391172549611 key: TN value: 50 mean value: 50.0 key: FP value: 13 mean value: 13.0 key: FN value: 14 mean value: 14.0 key: TP value: 51 mean value: 51.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.69 Accuracy on Blind test: 0.86 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01287651 0.01258087 0.0124259 0.01267362 0.01336336 0.01277065 0.01268458 0.01311874 0.0131762 0.01308203] mean value: 0.012875247001647949 key: score_time value: [0.01142859 0.01157522 0.01139855 0.01146388 0.01148963 0.01156664 0.01148224 0.01146364 0.01155543 0.0116024 ] mean value: 0.011502623558044434 key: test_mcc value: [0.22537447 0.73192505 0.23809524 0.73192505 0.53674504 0.73192505 0.69047619 0.54761905 0.84515425 0.70710678] mean value: 0.5986346184289039 key: train_mcc value: [0.69641781 0.96521476 0.87836615 0.83863125 1. 0.93264992 0.64227406 1. 0.93325653 0.98290472] mean value: 0.8869715198554682 key: test_fscore value: [0.44444444 0.85714286 0.61538462 0.85714286 0.8 0.83333333 0.85714286 0.76923077 0.90909091 0.85714286] mean value: 0.7800055500055499 key: train_fscore value: [0.79166667 0.98275862 0.94017094 0.92063492 1. 0.96363636 0.82608696 1. 0.96428571 0.99145299] mean value: 0.938069317405899 key: test_precision value: [0.66666667 0.75 0.57142857 0.75 0.75 1. 0.85714286 0.83333333 1. 0.75 ] mean value: 0.7928571428571429 key: train_precision value: [1. 0.98275862 0.93220339 0.85294118 1. 1. 0.7037037 1. 1. 0.98305085] mean value: 0.9454657738152082 key: test_recall value: [0.33333333 1. 0.66666667 1. 0.85714286 0.71428571 0.85714286 0.71428571 0.83333333 1. ] mean value: 0.7976190476190476 key: train_recall value: [0.65517241 0.98275862 0.94827586 1. 1. 0.92982456 1. 1. 0.93103448 1. ] mean value: 0.9447065940713854 key: test_accuracy value: [0.61538462 0.84615385 0.61538462 0.84615385 0.76923077 0.84615385 0.84615385 0.76923077 0.91666667 0.83333333] mean value: 0.7903846153846155 key: train_accuracy value: [0.82608696 0.9826087 0.93913043 0.91304348 1. 0.96521739 0.79130435 1. 0.96551724 0.99137931] mean value: 0.9374287856071962 key: test_roc_auc value: [0.5952381 0.85714286 0.61904762 0.85714286 0.76190476 0.85714286 0.8452381 0.77380952 0.91666667 0.83333333] mean value: 0.7916666666666667 key: train_roc_auc value: [0.82758621 0.98260738 0.93905021 0.9122807 1. 0.96491228 0.79310345 1. 0.96551724 0.99137931] mean value: 0.9376436781609195 key: test_jcc value: [0.28571429 0.75 0.44444444 0.75 0.66666667 0.71428571 0.75 0.625 0.83333333 0.75 ] mean value: 0.6569444444444443 key: train_jcc value: [0.65517241 0.96610169 0.88709677 0.85294118 1. 0.92982456 0.7037037 1. 0.93103448 0.98305085] mean value: 0.8908925654695956 key: TN value: 50 mean value: 50.0 key: FP value: 13 mean value: 13.0 key: FN value: 14 mean value: 14.0 key: TP value: 51 mean value: 51.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.64 Accuracy on Blind test: 0.84 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.1059854 0.08800244 0.08774233 0.08785915 0.08800817 0.08856511 0.08835125 0.09002113 0.09012151 0.08924937] mean value: 0.09039058685302734 key: score_time value: [0.01466513 0.01457238 0.01447558 0.01458764 0.01465821 0.01449251 0.01508141 0.01487041 0.01488805 0.01517725] mean value: 0.014746856689453126 key: test_mcc value: [1. 1. 0.85714286 1. 1. 0.85714286 1. 0.85714286 1. 1. ] mean value: 0.9571428571428571 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 0.92307692 1. 1. 0.92307692 1. 0.92307692 1. 1. ] mean value: 0.9769230769230768 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.85714286 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9857142857142858 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 0.85714286 1. 0.85714286 1. 1. ] mean value: 0.9714285714285715 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 0.92307692 1. 1. 0.92307692 1. 0.92307692 1. 1. ] mean value: 0.976923076923077 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.92857143 1. 1. 0.92857143 1. 0.92857143 1. 1. ] mean value: 0.9785714285714286 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 0.85714286 1. 1. 0.85714286 1. 0.85714286 1. 1. ] mean value: 0.9571428571428571 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 63 mean value: 63.0 key: FP value: 2 mean value: 2.0 key: FN value: 1 mean value: 1.0 key: TP value: 62 mean value: 62.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.87 Accuracy on Blind test: 0.95 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.03008389 0.04762316 0.02873278 0.0335021 0.02972507 0.02821159 0.03297353 0.0320456 0.03593206 0.05288649] mean value: 0.03517162799835205 key: score_time value: [0.01709604 0.02524185 0.02258182 0.0180335 0.01741457 0.01950884 0.02742362 0.02516723 0.02462244 0.03974295] mean value: 0.0236832857131958 key: test_mcc value: [1. 1. 0.85714286 1. 0.85714286 0.85714286 1. 0.85714286 1. 1. ] mean value: 0.9428571428571428 key: train_mcc value: [1. 1. 0.98275862 0.98275862 0.98275345 0.98275345 0.98275345 1. 1. 1. ] mean value: 0.9913777588135837 key: test_fscore value: [1. 1. 0.92307692 1. 0.92307692 0.92307692 1. 0.92307692 1. 1. ] mean value: 0.9692307692307691 key: train_fscore value: [1. 1. 0.99130435 0.99130435 0.99115044 0.99115044 0.99115044 1. 1. 1. ] mean value: 0.9956060023085802 key: test_precision value: [1. 1. 0.85714286 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9857142857142858 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 0.85714286 0.85714286 1. 0.85714286 1. 1. ] mean value: 0.9571428571428571 key: train_recall value: [1. 1. 0.98275862 0.98275862 0.98245614 0.98245614 0.98245614 1. 1. 1. ] mean value: 0.9912885662431942 key: test_accuracy value: [1. 1. 0.92307692 1. 0.92307692 0.92307692 1. 0.92307692 1. 1. ] mean value: 0.9692307692307693 key: train_accuracy value: [1. 1. 0.99130435 0.99130435 0.99130435 0.99130435 0.99130435 1. 1. 1. ] mean value: 0.9956521739130435 key: test_roc_auc value: [1. 1. 0.92857143 1. 0.92857143 0.92857143 1. 0.92857143 1. 1. ] mean value: 0.9714285714285715 key: train_roc_auc value: [1. 1. 0.99137931 0.99137931 0.99122807 0.99122807 0.99122807 1. 1. 1. ] mean value: 0.9956442831215971 key: test_jcc value: [1. 1. 0.85714286 1. 0.85714286 0.85714286 1. 0.85714286 1. 1. ] mean value: 0.9428571428571428 key: train_jcc value: [1. 1. 0.98275862 0.98275862 0.98245614 0.98245614 0.98245614 1. 1. 1. ] mean value: 0.9912885662431942 key: TN value: 63 mean value: 63.0 key: FP value: 3 mean value: 3.0 key: FN value: 1 mean value: 1.0 key: TP value: 61 mean value: 61.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.87 Accuracy on Blind test: 0.95 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.03084588 0.03903985 0.04139996 0.03601909 0.04611945 0.0508759 0.03565288 0.0392096 0.03545499 0.01767039] mean value: 0.03722879886627197 key: score_time value: [0.02145314 0.02262902 0.02137327 0.02144718 0.02134752 0.02161336 0.02137446 0.01924396 0.0216949 0.01203442] mean value: 0.02042112350463867 key: test_mcc value: [-0.23809524 0.23809524 -0.14085904 0.21957752 0.05143445 0.28288947 -0.03289758 0.39477102 0.35355339 0.50709255] mean value: 0.16355617745347897 key: train_mcc value: [0.96580942 0.98275862 0.98275862 0.98275862 0.98275345 0.96578908 0.98275345 0.98275345 0.98290472 0.98290472] mean value: 0.9793944154512012 key: test_fscore value: [0.33333333 0.61538462 0.22222222 0.54545455 0.625 0.54545455 0.22222222 0.44444444 0.71428571 0.72727273] mean value: 0.499507437007437 key: train_fscore value: [0.98245614 0.99130435 0.99130435 0.99130435 0.99115044 0.98214286 0.99115044 0.99115044 0.99130435 0.99130435] mean value: 0.9894572064057797 key: test_precision value: [0.33333333 0.57142857 0.33333333 0.6 0.55555556 0.75 0.5 1. 0.625 0.8 ] mean value: 0.6068650793650793 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.33333333 0.66666667 0.16666667 0.5 0.71428571 0.42857143 0.14285714 0.28571429 0.83333333 0.66666667] mean value: 0.4738095238095238 key: train_recall value: [0.96551724 0.98275862 0.98275862 0.98275862 0.98245614 0.96491228 0.98245614 0.98245614 0.98275862 0.98275862] mean value: 0.9791591046581971 key: test_accuracy value: [0.38461538 0.61538462 0.46153846 0.61538462 0.53846154 0.61538462 0.46153846 0.61538462 0.66666667 0.75 ] mean value: 0.5724358974358975 key: train_accuracy value: [0.9826087 0.99130435 0.99130435 0.99130435 0.99130435 0.9826087 0.99130435 0.99130435 0.99137931 0.99137931] mean value: 0.9895802098950526 key: test_roc_auc value: [0.38095238 0.61904762 0.44047619 0.60714286 0.52380952 0.63095238 0.48809524 0.64285714 0.66666667 0.75 ] mean value: 0.575 key: train_roc_auc value: [0.98275862 0.99137931 0.99137931 0.99137931 0.99122807 0.98245614 0.99122807 0.99122807 0.99137931 0.99137931] mean value: 0.9895795523290986 key: test_jcc value: [0.2 0.44444444 0.125 0.375 0.45454545 0.375 0.125 0.28571429 0.55555556 0.57142857] mean value: 0.35116883116883113 key: train_jcc value: [0.96551724 0.98275862 0.98275862 0.98275862 0.98245614 0.96491228 0.98245614 0.98245614 0.98275862 0.98275862] mean value: 0.9791591046581971 key: TN value: 43 mean value: 43.0 key: FP value: 34 mean value: 34.0 key: FN value: 21 mean value: 21.0 key: TP value: 30 mean value: 30.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.12 Accuracy on Blind test: 0.62 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.18425417 0.16746235 0.16713881 0.16807127 0.16799498 0.13374901 0.16947222 0.16639709 0.17026401 0.16803885] mean value: 0.16628427505493165 key: score_time value: [0.00870323 0.00875211 0.00923729 0.00900006 0.0088172 0.00897241 0.00904417 0.00871325 0.00923657 0.00879717] mean value: 0.008927345275878906 key: test_mcc value: [0.85714286 1. 0.85714286 0.85714286 1. 0.85714286 1. 0.85714286 1. 1. ] mean value: 0.9285714285714285 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.92307692 1. 0.92307692 0.92307692 1. 0.92307692 1. 0.92307692 1. 1. ] mean value: 0.9615384615384615 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 1. 0.85714286 0.85714286 1. 1. 1. 1. 1. 1. ] mean value: 0.9571428571428571 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 0.85714286 1. 0.85714286 1. 1. ] mean value: 0.9714285714285715 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.92307692 1. 0.92307692 0.92307692 1. 0.92307692 1. 0.92307692 1. 1. ] mean value: 0.9615384615384615 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.92857143 1. 0.92857143 0.92857143 1. 0.92857143 1. 0.92857143 1. 1. ] mean value: 0.9642857142857142 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.85714286 1. 0.85714286 0.85714286 1. 0.85714286 1. 0.85714286 1. 1. ] mean value: 0.9285714285714285 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 61 mean value: 61.0 key: FP value: 2 mean value: 2.0 key: FN value: 3 mean value: 3.0 key: TP value: 62 mean value: 62.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.87 Accuracy on Blind test: 0.95 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01616454 0.01594663 0.01507449 0.01565361 0.01556253 0.01537538 0.01601553 0.01538396 0.01568604 0.01597762] mean value: 0.015684032440185548 key: score_time value: [0.01205802 0.01172352 0.01197767 0.01179051 0.01297235 0.01291943 0.01175404 0.01311636 0.01326632 0.01292157] mean value: 0.012449979782104492 key: test_mcc value: [-0.69047619 0.38095238 0.05143445 -0.05143445 0.22537447 0.38575837 -0.38095238 -0.05143445 -0.16903085 0.19245009] mean value: -0.010735855884654828 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.15384615 0.66666667 0.4 0.53333333 0.70588235 0.75 0.30769231 0.36363636 0.46153846 0.66666667] mean value: 0.500926230632113 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.14285714 0.66666667 0.5 0.44444444 0.6 0.66666667 0.33333333 0.5 0.42857143 0.55555556] mean value: 0.4838095238095238 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.16666667 0.66666667 0.33333333 0.66666667 0.85714286 0.85714286 0.28571429 0.28571429 0.5 0.83333333] mean value: 0.5452380952380952 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.15384615 0.69230769 0.53846154 0.46153846 0.61538462 0.69230769 0.30769231 0.46153846 0.41666667 0.58333333] mean value: 0.49230769230769234 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.1547619 0.69047619 0.52380952 0.47619048 0.5952381 0.67857143 0.30952381 0.47619048 0.41666667 0.58333333] mean value: 0.4904761904761905 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.08333333 0.5 0.25 0.36363636 0.54545455 0.6 0.18181818 0.22222222 0.3 0.5 ] mean value: 0.35464646464646465 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 28 mean value: 28.0 key: FP value: 28 mean value: 28.0 key: FN value: 36 mean value: 36.0 key: TP value: 36 mean value: 36.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: -0.11 Accuracy on Blind test: 0.5 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.04481435 0.05445457 0.0491457 0.05211663 0.01283717 0.01266241 0.01273108 0.01271272 0.04638553 0.05051947] mean value: 0.034837961196899414 key: score_time value: [0.02102113 0.02327156 0.02284217 0.01170707 0.01157761 0.01172662 0.01173377 0.01661181 0.02016473 0.02096701] mean value: 0.017162346839904787 key: test_mcc value: [0.41475753 0.38095238 0.38095238 1. 0.7200823 0.6172134 0.6172134 0.41475753 0.84515425 0.84515425] mean value: 0.6236237432887879 key: train_mcc value: [1. 1. 0.96521476 1. 1. 1. 0.98275345 1. 1. 1. ] mean value: 0.9947968209959374 key: test_fscore value: [0.71428571 0.66666667 0.66666667 1. 0.875 0.72727273 0.72727273 0.66666667 0.90909091 0.92307692] mean value: 0.7875999000999001 key: train_fscore value: [1. 1. 0.98275862 1. 1. 1. 0.99115044 1. 1. 1. ] mean value: 0.9973909063167531 key: test_precision value: [0.625 0.66666667 0.66666667 1. 0.77777778 1. 1. 0.8 1. 0.85714286] mean value: 0.8393253968253968 key: train_precision value: [1. 1. 0.98275862 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9982758620689655 key: test_recall value: [0.83333333 0.66666667 0.66666667 1. 1. 0.57142857 0.57142857 0.57142857 0.83333333 1. ] mean value: 0.7714285714285715 key: train_recall value: [1. 1. 0.98275862 1. 1. 1. 0.98245614 1. 1. 1. ] mean value: 0.9965214761040532 key: test_accuracy value: [0.69230769 0.69230769 0.69230769 1. 0.84615385 0.76923077 0.76923077 0.69230769 0.91666667 0.91666667] mean value: 0.7987179487179488 key: train_accuracy value: [1. 1. 0.9826087 1. 1. 1. 0.99130435 1. 1. 1. ] mean value: 0.9973913043478261 key: test_roc_auc value: [0.70238095 0.69047619 0.69047619 1. 0.83333333 0.78571429 0.78571429 0.70238095 0.91666667 0.91666667] mean value: 0.8023809523809524 key: train_roc_auc value: [1. 1. 0.98260738 1. 1. 1. 0.99122807 1. 1. 1. ] mean value: 0.9973835450695704 key: test_jcc value: [0.55555556 0.5 0.5 1. 0.77777778 0.57142857 0.57142857 0.5 0.83333333 0.85714286] mean value: 0.6666666666666666 key: train_jcc value: [1. 1. 0.96610169 1. 1. 1. 0.98245614 1. 1. 1. ] mean value: 0.9948557835266131 key: TN value: 53 mean value: 53.0 key: FP value: 15 mean value: 15.0 key: FN value: 11 mean value: 11.0 key: TP value: 49 mean value: 49.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.64 Accuracy on Blind test: 0.84 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:356: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:357: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_CV['Resampling'] = rs_rus /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:362: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:363: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_BT['Resampling'] = rs_rus /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.10236979 0.19759798 0.20445657 0.26835752 0.19444537 0.18951321 0.18658042 0.20327044 0.2164557 0.1654892 ] mean value: 0.19285361766815184 key: score_time value: [0.0115881 0.02972293 0.02169919 0.02145386 0.02297568 0.02107 0.02039909 0.02235222 0.02386165 0.01167202] mean value: 0.020679473876953125 key: test_mcc value: [0.41475753 0.38095238 0.38095238 1. 0.7200823 0.41475753 0.6172134 0.41475753 0.33333333 0.70710678] mean value: 0.5383913169105485 key: train_mcc value: [1. 1. 0.96521476 1. 1. 1. 0.98275345 1. 1. 1. ] mean value: 0.9947968209959374 key: test_fscore value: [0.71428571 0.66666667 0.66666667 1. 0.875 0.66666667 0.72727273 0.66666667 0.66666667 0.85714286] mean value: 0.7507034632034632 key: train_fscore value: [1. 1. 0.98275862 1. 1. 1. 0.99115044 1. 1. 1. ] mean value: 0.9973909063167531 key: test_precision value: [0.625 0.66666667 0.66666667 1. 0.77777778 0.8 1. 0.8 0.66666667 0.75 ] mean value: 0.7752777777777778 key: train_precision value: [1. 1. 0.98275862 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9982758620689655 key: test_recall value: [0.83333333 0.66666667 0.66666667 1. 1. 0.57142857 0.57142857 0.57142857 0.66666667 1. ] mean value: 0.7547619047619049 key: train_recall value: [1. 1. 0.98275862 1. 1. 1. 0.98245614 1. 1. 1. ] mean value: 0.9965214761040532 key: test_accuracy value: [0.69230769 0.69230769 0.69230769 1. 0.84615385 0.69230769 0.76923077 0.69230769 0.66666667 0.83333333] mean value: 0.7576923076923077 key: train_accuracy value: [1. 1. 0.9826087 1. 1. 1. 0.99130435 1. 1. 1. ] mean value: 0.9973913043478261 key: test_roc_auc value: [0.70238095 0.69047619 0.69047619 1. 0.83333333 0.70238095 0.78571429 0.70238095 0.66666667 0.83333333] mean value: 0.7607142857142857 key: train_roc_auc value: [1. 1. 0.98260738 1. 1. 1. 0.99122807 1. 1. 1. ] mean value: 0.9973835450695704 key: test_jcc value: [0.55555556 0.5 0.5 1. 0.77777778 0.5 0.57142857 0.5 0.5 0.75 ] mean value: 0.6154761904761904 key: train_jcc value: [1. 1. 0.96610169 1. 1. 1. 0.98245614 1. 1. 1. ] mean value: 0.9948557835266131 key: TN value: 49 mean value: 49.0 key: FP value: 16 mean value: 16.0 key: FN value: 15 mean value: 15.0 key: TP value: 48 mean value: 48.0 key: trainingY_neg value: 64 mean value: 64.0 key: trainingY_pos value: 64 mean value: 64.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.64 Accuracy on Blind test: 0.84 PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.03761697 0.03439021 0.03464413 0.03514004 0.03435802 0.0339179 0.03460574 0.03575659 0.03499985 0.03665447] mean value: 0.035208392143249514 key: score_time value: [0.01418996 0.01417136 0.01432729 0.0129149 0.01295686 0.01291966 0.01281643 0.01288414 0.01295233 0.01192045] mean value: 0.013205337524414062 key: test_mcc value: [0.83624201 1. 0.91485507 0.75474102 0.91485507 0.95833333 0.7085716 0.91485507 0.87318841 0.71722586] mean value: 0.8592867442031116 key: train_mcc value: [0.95283019 0.96725018 0.97180697 0.95298209 0.93883291 0.9576579 0.9435291 0.95294092 0.93883426 0.93883426] mean value: 0.9515498776477427 key: test_fscore value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.92 1. 0.95652174 0.85714286 0.95652174 0.9787234 0.84444444 0.95833333 0.93617021 0.86792453] mean value: 0.9275782258504666 key: train_fscore value: [0.97641509 0.98368298 0.98598131 0.97663551 0.96955504 0.97882353 0.97169811 0.97641509 0.96941176 0.96941176] mean value: 0.9758030201952016 key: test_precision value: [0.88461538 1. 0.95652174 0.94736842 0.95652174 0.95833333 0.9047619 0.95833333 0.95652174 0.79310345] mean value: 0.9316081042763755 key: train_precision value: [0.97641509 0.97235023 0.98139535 0.97209302 0.96728972 0.98113208 0.97169811 0.97641509 0.96713615 0.96713615] mean value: 0.9733060999961912 key: test_recall value: [0.95833333 1. 0.95652174 0.7826087 0.95652174 1. 0.79166667 0.95833333 0.91666667 0.95833333] mean value: 0.9278985507246377 key: train_recall value: [0.97641509 0.99528302 0.99061033 0.98122066 0.97183099 0.97652582 0.97169811 0.97641509 0.97169811 0.97169811] mean value: 0.9783395340597041 key: test_accuracy value: [0.91666667 1. 0.95744681 0.87234043 0.95744681 0.9787234 0.85106383 0.95744681 0.93617021 0.85106383] mean value: 0.9278368794326243 key: train_accuracy value: [0.97641509 0.98349057 0.98588235 0.97647059 0.96941176 0.97882353 0.97176471 0.97647059 0.96941176 0.96941176] mean value: 0.9757552719200888 key: test_roc_auc value: [0.91666667 1. 0.95742754 0.87047101 0.95742754 0.97916667 0.85235507 0.95742754 0.9365942 0.84873188] mean value: 0.9276268115942029 key: train_roc_auc value: [0.97641509 0.98349057 0.9858712 0.97645939 0.96940606 0.97882895 0.97176455 0.97647046 0.96941713 0.96941713] mean value: 0.9757540526175923 key: test_jcc value: [0.85185185 1. 0.91666667 0.75 0.91666667 0.95833333 0.73076923 0.92 0.88 0.76666667] mean value: 0.8690954415954415 key: train_jcc value: [0.95391705 0.96788991 0.97235023 0.9543379 0.94090909 0.95852535 0.94495413 0.95391705 0.94063927 0.94063927] mean value: 0.9528079243381858 key: TN value: 219 mean value: 219.0 key: FP value: 17 mean value: 17.0 key: FN value: 17 mean value: 17.0 key: TP value: 219 mean value: 219.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.7 Accuracy on Blind test: 0.89 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.78390193 0.94591141 0.78064728 0.92265105 0.77569675 0.75856495 0.89818215 0.78333569 0.78202963 1.12071753] mean value: 0.855163836479187 key: score_time value: [0.01343822 0.01443529 0.01470709 0.01484108 0.01459599 0.01428747 0.0131669 0.01431489 0.02354884 0.01364064] mean value: 0.015097641944885254 key: test_mcc value: [0.91986621 0.9591663 0.95833333 0.91485507 0.95833333 0.95833333 0.95825929 0.91804649 1. 0.84147165] mean value: 0.9386665021079992 key: train_mcc value: [0.98594778 1. 1. 0.98598008 1. 0.99063185 1. 0.985981 1. 1. ] mean value: 0.9948540708331428 key: test_fscore value: [0.96 0.97959184 0.9787234 0.95652174 0.9787234 0.9787234 0.97959184 0.96 1. 0.92307692] mean value: 0.9694952548442703 key: train_fscore value: [0.99297424 1. 1. 0.99300699 1. 0.9953271 1. 0.99297424 1. 1. ] mean value: 0.9974282573562487 key: test_precision value: [0.92307692 0.96 0.95833333 0.95652174 0.95833333 0.95833333 0.96 0.92307692 1. 0.85714286] mean value: 0.9454818442427138 key: train_precision value: [0.98604651 1. 1. 0.98611111 1. 0.99069767 1. 0.98604651 1. 1. ] mean value: 0.9948901808785531 key: test_recall value: [1. 1. 1. 0.95652174 1. 1. 1. 1. 1. 1. ] mean value: 0.9956521739130434 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95833333 0.97916667 0.9787234 0.95744681 0.9787234 0.9787234 0.9787234 0.95744681 1. 0.91489362] mean value: 0.9682180851063829 key: train_accuracy value: [0.99292453 1. 1. 0.99294118 1. 0.99529412 1. 0.99294118 1. 1. ] mean value: 0.9974100998890123 key: test_roc_auc value: [0.95833333 0.97916667 0.97916667 0.95742754 0.97916667 0.97916667 0.97826087 0.95652174 1. 0.91304348] mean value: 0.9680253623188406 key: train_roc_auc value: [0.99292453 1. 1. 0.99292453 1. 0.99528302 1. 0.99295775 1. 1. ] mean value: 0.9974089821950571 key: test_jcc value: [0.92307692 0.96 0.95833333 0.91666667 0.95833333 0.95833333 0.96 0.92307692 1. 0.85714286] mean value: 0.9414963369963371 key: train_jcc value: [0.98604651 1. 1. 0.98611111 1. 0.99069767 1. 0.98604651 1. 1. ] mean value: 0.9948901808785531 key: TN value: 221 mean value: 221.0 key: FP value: 2 mean value: 2.0 key: FN value: 15 mean value: 15.0 key: TP value: 234 mean value: 234.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.81 Accuracy on Blind test: 0.93 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01341796 0.01314044 0.0100596 0.00966549 0.0095849 0.01072311 0.01043272 0.00973892 0.00969958 0.01060581] mean value: 0.010706853866577149 key: score_time value: [0.01171517 0.00988674 0.00951385 0.00905657 0.00912094 0.00920796 0.00893307 0.00947523 0.00947046 0.00933242] mean value: 0.009571242332458495 key: test_mcc value: [0.54213748 0.35355339 0.74773263 0.27943865 0.45948781 0.74773263 0.62091661 0.58127976 0.49819858 0.42102089] mean value: 0.5251498421702169 key: train_mcc value: [0.66993058 0.63309049 0.62030596 0.66608339 0.66593578 0.68966441 0.69320894 0.65652271 0.68112737 0.60553441] mean value: 0.658140403139998 key: test_fscore value: [0.7755102 0.6 0.875 0.58536585 0.74509804 0.875 0.82352941 0.80769231 0.72727273 0.66666667] mean value: 0.7481135210352263 key: train_fscore value: [0.8364486 0.82110092 0.81632653 0.83135392 0.83449883 0.84722222 0.85201794 0.82660333 0.8440367 0.76216216] mean value: 0.8271771144180518 key: test_precision value: [0.76 0.75 0.84 0.66666667 0.67857143 0.84 0.77777778 0.75 0.8 0.77777778] mean value: 0.764079365079365 key: train_precision value: [0.8287037 0.79910714 0.78947368 0.84134615 0.8287037 0.83561644 0.81196581 0.83253589 0.82142857 0.89240506] mean value: 0.8281286158530381 key: test_recall value: [0.79166667 0.5 0.91304348 0.52173913 0.82608696 0.91304348 0.875 0.875 0.66666667 0.58333333] mean value: 0.7465579710144927 key: train_recall value: [0.84433962 0.84433962 0.84507042 0.82159624 0.84037559 0.85915493 0.89622642 0.82075472 0.86792453 0.66509434] mean value: 0.8304876428381611 key: test_accuracy value: [0.77083333 0.66666667 0.87234043 0.63829787 0.72340426 0.87234043 0.80851064 0.78723404 0.74468085 0.70212766] mean value: 0.7586436170212766 key: train_accuracy value: [0.83490566 0.81603774 0.80941176 0.83294118 0.83294118 0.84470588 0.84470588 0.82823529 0.84 0.79294118] mean value: 0.8276825749167592 key: test_roc_auc value: [0.77083333 0.66666667 0.87318841 0.63586957 0.72554348 0.87318841 0.80706522 0.78532609 0.74637681 0.70471014] mean value: 0.7588768115942028 key: train_roc_auc value: [0.83490566 0.81603774 0.80932766 0.83296793 0.83292364 0.8446718 0.84482682 0.82821773 0.84006555 0.79264107] mean value: 0.827658561431482 key: test_jcc value: [0.63333333 0.42857143 0.77777778 0.4137931 0.59375 0.77777778 0.7 0.67741935 0.57142857 0.5 ] mean value: 0.6073851347175874 key: train_jcc value: [0.7188755 0.69649805 0.68965517 0.71138211 0.716 0.73493976 0.7421875 0.70445344 0.73015873 0.61572052] mean value: 0.7059870797225559 key: TN value: 182 mean value: 182.0 key: FP value: 60 mean value: 60.0 key: FN value: 54 mean value: 54.0 key: TP value: 176 mean value: 176.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.24 Accuracy on Blind test: 0.68 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01091361 0.01103592 0.01082873 0.01045895 0.00958872 0.00965691 0.00966382 0.00966477 0.009624 0.00963116] mean value: 0.010106658935546875 key: score_time value: [0.00961423 0.00953054 0.00960398 0.00916052 0.00865173 0.00873494 0.00867224 0.00870585 0.00867987 0.00867605] mean value: 0.009002995491027833 key: test_mcc value: [0.25354628 0.51639778 0.48913043 0.42545532 0.31876614 0.7085716 0.44646172 0.40653424 0.31884058 0.32605546] mean value: 0.4209759551550246 key: train_mcc value: [0.54318451 0.53172012 0.50722652 0.52031192 0.49399615 0.47474887 0.48764745 0.47693583 0.51529177 0.51618878] mean value: 0.5067251933766291 key: test_fscore value: [0.59090909 0.71428571 0.73913043 0.63157895 0.63636364 0.85714286 0.73469388 0.69565217 0.66666667 0.7037037 ] mean value: 0.6970127102686764 key: train_fscore value: [0.76513317 0.75 0.74452555 0.74055416 0.72122762 0.7254902 0.73607748 0.71717172 0.75650118 0.74939173] mean value: 0.7406072801630106 key: test_precision value: [0.65 0.83333333 0.73913043 0.8 0.66666667 0.80769231 0.72 0.72727273 0.66666667 0.63333333] mean value: 0.7244095469747645 key: train_precision value: [0.78606965 0.79787234 0.77272727 0.79891304 0.79213483 0.75897436 0.75621891 0.77173913 0.75829384 0.77386935] mean value: 0.7766812720311039 key: test_recall value: [0.54166667 0.625 0.73913043 0.52173913 0.60869565 0.91304348 0.75 0.66666667 0.66666667 0.79166667] mean value: 0.6824275362318841 key: train_recall value: [0.74528302 0.70754717 0.71830986 0.69014085 0.66197183 0.69483568 0.71698113 0.66981132 0.75471698 0.72641509] mean value: 0.7086012932943573 key: test_accuracy value: [0.625 0.75 0.74468085 0.70212766 0.65957447 0.85106383 0.72340426 0.70212766 0.65957447 0.65957447] mean value: 0.7077127659574467 key: train_accuracy value: [0.77122642 0.76415094 0.75294118 0.75764706 0.74352941 0.73647059 0.74352941 0.73647059 0.75764706 0.75764706] mean value: 0.7521259711431743 key: test_roc_auc value: [0.625 0.75 0.74456522 0.69836957 0.65851449 0.85235507 0.72282609 0.70289855 0.65942029 0.6567029 ] mean value: 0.7070652173913043 key: train_roc_auc value: [0.77122642 0.76415094 0.75302285 0.75780627 0.74372176 0.73656878 0.74346709 0.73631411 0.75764018 0.75757374] mean value: 0.7521492160510231 key: test_jcc value: [0.41935484 0.55555556 0.5862069 0.46153846 0.46666667 0.75 0.58064516 0.53333333 0.5 0.54285714] mean value: 0.5396158056502884 key: train_jcc value: [0.61960784 0.6 0.59302326 0.588 0.564 0.56923077 0.58237548 0.55905512 0.60836502 0.59922179] mean value: 0.5882879274114092 key: TN value: 173 mean value: 173.0 key: FP value: 75 mean value: 75.0 key: FN value: 63 mean value: 63.0 key: TP value: 161 mean value: 161.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.03 Accuracy on Blind test: 0.57 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00887275 0.009969 0.00995159 0.01022196 0.00914335 0.01016235 0.00951147 0.01033115 0.01050925 0.01074362] mean value: 0.009941649436950684 key: score_time value: [0.01565647 0.01562095 0.01235104 0.01209283 0.01130271 0.01212144 0.01227593 0.01490164 0.01249647 0.0126102 ] mean value: 0.013142967224121093 key: test_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( [0.38341289 0.54594868 0.79308818 0.31884058 0.67023783 0.65942029 0.54211097 0.46440394 0.4899891 0.40290954] mean value: 0.5270362005800171 key: train_mcc value: [0.72426952 0.68335602 0.66440957 0.71015175 0.68941367 0.64633879 0.69126867 0.70274704 0.65723256 0.69851923] mean value: 0.6867706820106939 key: test_fscore value: [0.71698113 0.78431373 0.88372093 0.65217391 0.83636364 0.82608696 0.79245283 0.76363636 0.76 0.74576271] mean value: 0.7761492199416529 key: train_fscore value: [0.86784141 0.84978541 0.84095861 0.86206897 0.85287846 0.8329718 0.85209713 0.85776805 0.8373102 0.8558952 ] mean value: 0.8509575228344838 key: test_precision value: [0.65517241 0.74074074 0.95 0.65217391 0.71875 0.82608696 0.72413793 0.67741935 0.73076923 0.62857143] mean value: 0.7303821969312914 key: train_precision value: [0.81404959 0.77952756 0.78455285 0.79681275 0.78125 0.77419355 0.80082988 0.8 0.7751004 0.79674797] mean value: 0.7903064533356285 key: test_recall value: [0.79166667 0.83333333 0.82608696 0.65217391 1. 0.82608696 0.875 0.875 0.79166667 0.91666667] mean value: 0.838768115942029 key: train_recall value: [0.92924528 0.93396226 0.90610329 0.93896714 0.93896714 0.90140845 0.91037736 0.9245283 0.91037736 0.9245283 ] mean value: 0.9218464877314201 key: test_accuracy value: [0.6875 0.77083333 0.89361702 0.65957447 0.80851064 0.82978723 0.76595745 0.72340426 0.74468085 0.68085106] mean value: 0.7564716312056737 key: train_accuracy value: [0.85849057 0.83490566 0.82823529 0.84941176 0.83764706 0.81882353 0.84235294 0.84705882 0.82352941 0.84470588] mean value: 0.8385160932297447 key: test_roc_auc value: [0.6875 0.77083333 0.89221014 0.65942029 0.8125 0.82971014 0.76358696 0.7201087 0.74365942 0.67572464] mean value: 0.7555253623188405 key: train_roc_auc value: [0.85849057 0.83490566 0.82805164 0.84920055 0.8374081 0.81862875 0.84251262 0.84724068 0.82373328 0.84489326] mean value: 0.8385065107626894 key: test_jcc value: [0.55882353 0.64516129 0.79166667 0.48387097 0.71875 0.7037037 0.65625 0.61764706 0.61290323 0.59459459] mean value: 0.6383371037071227 key: train_jcc value: [0.76653696 0.73880597 0.72556391 0.75757576 0.74349442 0.71375465 0.74230769 0.75095785 0.72014925 0.7480916 ] mean value: 0.7407238076610564 key: TN value: 159 mean value: 159.0 key: FP value: 38 mean value: 38.0 key: FN value: 77 mean value: 77.0 key: TP value: 198 mean value: 198.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: -0.04 Accuracy on Blind test: 0.57 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.02149534 0.01937175 0.02007341 0.02000952 0.01925445 0.02118301 0.01910329 0.01937222 0.02085876 0.01932025] mean value: 0.02000420093536377 key: score_time value: [0.01150203 0.01241422 0.01137638 0.01128054 0.01172709 0.01148748 0.01127481 0.01146984 0.01192999 0.01131344] mean value: 0.011577582359313965 key: test_mcc value: [0.62554324 0.83624201 0.87318841 0.57560058 0.7023605 0.87318841 0.53734864 0.83303222 0.75645593 0.53176131] mean value: 0.7144721256854312 key: train_mcc value: [0.79372539 0.77482717 0.85412791 0.82151485 0.75250476 0.82132891 0.83090737 0.82588861 0.79806344 0.8074811 ] mean value: 0.8080369516528417 key: test_fscore value: [0.80851064 0.91304348 0.93617021 0.77272727 0.84444444 0.93617021 0.75555556 0.91304348 0.86363636 0.7755102 ] mean value: 0.8518811860796796 key: train_fscore value: [0.89320388 0.88349515 0.92705882 0.90952381 0.87104623 0.90995261 0.9138756 0.91252955 0.89688249 0.90167866] mean value: 0.9019246797517653 key: test_precision value: [0.82608696 0.95454545 0.91666667 0.80952381 0.86363636 0.91666667 0.80952381 0.95454545 0.95 0.76 ] mean value: 0.8761195181629964 key: train_precision value: [0.92 0.91 0.92924528 0.92270531 0.9040404 0.91866029 0.92718447 0.91469194 0.91219512 0.91707317] mean value: 0.917579598998058 key: test_recall value: [0.79166667 0.875 0.95652174 0.73913043 0.82608696 0.95652174 0.70833333 0.875 0.79166667 0.79166667] mean value: 0.8311594202898551 key: train_recall value: [0.86792453 0.85849057 0.92488263 0.89671362 0.84037559 0.90140845 0.9009434 0.91037736 0.88207547 0.88679245] mean value: 0.8869984055275045 key: test_accuracy value: [0.8125 0.91666667 0.93617021 0.78723404 0.85106383 0.93617021 0.76595745 0.91489362 0.87234043 0.76595745] mean value: 0.855895390070922 key: train_accuracy value: [0.89622642 0.88679245 0.92705882 0.91058824 0.87529412 0.91058824 0.91529412 0.91294118 0.89882353 0.90352941] mean value: 0.9037136514983353 key: test_roc_auc value: [0.8125 0.91666667 0.9365942 0.78623188 0.85054348 0.9365942 0.76721014 0.91576087 0.8740942 0.76539855] mean value: 0.856159420289855 key: train_roc_auc value: [0.89622642 0.88679245 0.92706396 0.91062096 0.87537647 0.91060989 0.91526043 0.91293516 0.89878421 0.90349012] mean value: 0.9037160067322171 key: test_jcc value: [0.67857143 0.84 0.88 0.62962963 0.73076923 0.88 0.60714286 0.84 0.76 0.63333333] mean value: 0.7479446479446479 key: train_jcc value: [0.80701754 0.79130435 0.86403509 0.83406114 0.77155172 0.83478261 0.84140969 0.83913043 0.81304348 0.8209607 ] mean value: 0.8217296750973186 key: TN value: 208 mean value: 208.0 key: FP value: 40 mean value: 40.0 key: FN value: 28 mean value: 28.0 key: TP value: 196 mean value: 196.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.38 Accuracy on Blind test: 0.79 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.27547193 1.79986334 2.08108068 1.87422729 1.80815768 1.70918036 1.83464098 1.91837955 1.86248994 1.87331986] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( 1.8036811590194701 key: score_time value: [0.01231146 0.01532292 0.01324606 0.01445222 0.01483798 0.01499081 0.01235771 0.0146966 0.01462078 0.01356864] mean value: 0.014040517807006835 key: test_mcc value: [0.9591663 0.91986621 0.91833182 0.87318841 0.95833333 0.95833333 0.91804649 0.95825929 1. 0.8047833 ] mean value: 0.9268308495362693 key: train_mcc value: [0.99061012 1. 0.99063185 0.99530506 0.99530506 1. 0.99063227 0.99063227 0.99063227 0.99530516] mean value: 0.993905406072443 key: test_fscore value: [0.97959184 0.95652174 0.95833333 0.93617021 0.9787234 0.9787234 0.96 0.97959184 1. 0.90566038] mean value: 0.963331614456824 key: train_fscore value: [0.99530516 1. 0.9953271 0.99765808 0.99765808 1. 0.99530516 0.99530516 0.99530516 0.99764706] mean value: 0.996951097815485 key: test_precision value: [0.96 1. 0.92 0.91666667 0.95833333 0.95833333 0.92307692 0.96 1. 0.82758621] mean value: 0.9423996463306809 key: train_precision value: [0.99065421 1. 0.99069767 0.9953271 0.9953271 1. 0.99065421 0.99065421 0.99065421 0.99530516] mean value: 0.9939273866775237 key: test_recall value: [1. 0.91666667 1. 0.95652174 1. 1. 1. 1. 1. 1. ] mean value: 0.9873188405797102 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97916667 0.95833333 0.95744681 0.93617021 0.9787234 0.9787234 0.95744681 0.9787234 1. 0.89361702] mean value: 0.9618351063829786 key: train_accuracy value: [0.99528302 1. 0.99529412 0.99764706 0.99764706 1. 0.99529412 0.99529412 0.99529412 0.99764706] mean value: 0.9969400665926749 key: test_roc_auc value: [0.97916667 0.95833333 0.95833333 0.9365942 0.97916667 0.97916667 0.95652174 0.97826087 1. 0.89130435] mean value: 0.9616847826086957 key: train_roc_auc value: [0.99528302 1. 0.99528302 0.99764151 0.99764151 1. 0.99530516 0.99530516 0.99530516 0.99765258] mean value: 0.9969417131721144 key: test_jcc value: [0.96 0.91666667 0.92 0.88 0.95833333 0.95833333 0.92307692 0.96 1. 0.82758621] mean value: 0.9303996463306807 key: train_jcc value: [0.99065421 1. 0.99069767 0.9953271 0.9953271 1. 0.99065421 0.99065421 0.99065421 0.99530516] mean value: 0.9939273866775237 key: TN value: 221 mean value: 221.0 key: FP value: 3 mean value: 3.0 key: FN value: 15 mean value: 15.0 key: TP value: 233 mean value: 233.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.64 Accuracy on Blind test: 0.88 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02307987 0.02041054 0.01674342 0.0163703 0.01677394 0.01718926 0.0178535 0.0168376 0.01861668 0.01827574] mean value: 0.018215084075927736 key: score_time value: [0.01175237 0.00904274 0.00862765 0.00864434 0.00859547 0.00861335 0.00882912 0.00861955 0.00866389 0.00884366] mean value: 0.009023213386535644 key: test_mcc value: [0.9591663 1. 0.95833333 0.91485507 0.95833333 0.91833182 1. 1. 1. 0.91804649] mean value: 0.962706635839812 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97959184 1. 0.9787234 0.95652174 0.9787234 0.95833333 1. 1. 1. 0.96 ] mean value: 0.98118937177091 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.96 1. 0.95833333 0.95652174 0.95833333 0.92 1. 1. 1. 0.92307692] mean value: 0.9676265328874024 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.95652174 1. 1. 1. 1. 1. 1. ] mean value: 0.9956521739130434 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97916667 1. 0.9787234 0.95744681 0.9787234 0.95744681 1. 1. 1. 0.95744681] mean value: 0.980895390070922 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97916667 1. 0.97916667 0.95742754 0.97916667 0.95833333 1. 1. 1. 0.95652174] mean value: 0.9809782608695654 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.96 1. 0.95833333 0.91666667 0.95833333 0.92 1. 1. 1. 0.92307692] mean value: 0.9636410256410256 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 228 mean value: 228.0 key: FP value: 1 mean value: 1.0 key: FN value: 8 mean value: 8.0 key: TP value: 235 mean value: 235.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.76 Accuracy on Blind test: 0.92 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.10994768 0.1118784 0.11284971 0.11134195 0.11294222 0.11214495 0.11530566 0.11096668 0.11075163 0.11043859] mean value: 0.11185674667358399 key: score_time value: [0.01739383 0.01757956 0.01746917 0.01737118 0.01771498 0.01772046 0.0186255 0.01787496 0.01741862 0.01758409] mean value: 0.017675232887268067 key: test_mcc value: [0.8819171 0.8819171 1. 0.78804348 0.82971014 0.95833333 0.95825929 0.95833333 1. 0.95825929] mean value: 0.9214773080042724 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.94117647 0.93333333 1. 0.89361702 0.91304348 0.9787234 0.97959184 0.9787234 1. 0.97959184] mean value: 0.9597800785439059 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.88888889 1. 1. 0.875 0.91304348 0.95833333 0.96 1. 1. 0.96 ] mean value: 0.9555265700483091 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.875 1. 0.91304348 0.91304348 1. 1. 0.95833333 1. 1. ] mean value: 0.9659420289855072 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9375 0.9375 1. 0.89361702 0.91489362 0.9787234 0.9787234 0.9787234 1. 0.9787234 ] mean value: 0.959840425531915 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9375 0.9375 1. 0.89402174 0.91485507 0.97916667 0.97826087 0.97916667 1. 0.97826087] mean value: 0.959873188405797 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.88888889 0.875 1. 0.80769231 0.84 0.95833333 0.96 0.95833333 1. 0.96 ] mean value: 0.9248247863247865 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 225 mean value: 225.0 key: FP value: 8 mean value: 8.0 key: FN value: 11 mean value: 11.0 key: TP value: 228 mean value: 228.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.4 Accuracy on Blind test: 0.83 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01066399 0.00968838 0.01067972 0.0100224 0.00957918 0.0111053 0.01065373 0.01077867 0.01065278 0.00983286] mean value: 0.010365700721740723 key: score_time value: [0.00920367 0.00877094 0.00928211 0.00929713 0.0091722 0.0094471 0.00929952 0.00941586 0.00855613 0.0093224 ] mean value: 0.00917670726776123 key: test_mcc value: [0.77459667 0.79235477 0.87979456 0.79418308 0.87979456 0.87979456 0.8047833 0.95833333 0.95825929 0.84147165] mean value: 0.8563365763657028 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.88888889 0.89361702 0.93877551 0.89795918 0.93877551 0.93877551 0.90566038 0.9787234 0.97959184 0.92307692] mean value: 0.9283844165876625 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.91304348 0.88461538 0.84615385 0.88461538 0.88461538 0.82758621 1. 0.96 0.85714286] mean value: 0.8857772542300278 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.875 1. 0.95652174 1. 1. 1. 0.95833333 1. 1. ] mean value: 0.9789855072463768 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.875 0.89583333 0.93617021 0.89361702 0.93617021 0.93617021 0.89361702 0.9787234 0.9787234 0.91489362] mean value: 0.9238918439716312 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.875 0.89583333 0.9375 0.89492754 0.9375 0.9375 0.89130435 0.97916667 0.97826087 0.91304348] mean value: 0.9240036231884059 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.8 0.80769231 0.88461538 0.81481481 0.88461538 0.88461538 0.82758621 0.95833333 0.96 0.85714286] mean value: 0.8679415673726018 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 205 mean value: 205.0 key: FP value: 5 mean value: 5.0 key: FN value: 31 mean value: 31.0 key: TP value: 231 mean value: 231.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.19 Accuracy on Blind test: 0.74 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.573318 1.54127455 1.53914356 1.51387715 1.52104235 1.51077247 1.55802274 1.57117796 1.55043197 1.55481386] mean value: 1.5433874607086182 key: score_time value: [0.09027529 0.09085727 0.09054208 0.09096718 0.09015322 0.08981037 0.09099841 0.09189463 0.0910449 0.09459853] mean value: 0.09111418724060058 key: test_mcc value: [1. 0.91986621 0.95833333 0.91485507 0.95833333 0.95833333 1. 0.95825929 1. 1. ] mean value: 0.9667980574877196 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.95652174 0.9787234 0.95652174 0.9787234 0.9787234 1. 0.97959184 1. 1. ] mean value: 0.982880552776152 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.95833333 0.95652174 0.95833333 0.95833333 1. 0.96 1. 1. ] mean value: 0.9791521739130434 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.91666667 1. 0.95652174 1. 1. 1. 1. 1. 1. ] mean value: 0.9873188405797102 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.95833333 0.9787234 0.95744681 0.9787234 0.9787234 1. 0.9787234 1. 1. ] mean value: 0.9830673758865249 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.95833333 0.97916667 0.95742754 0.97916667 0.97916667 1. 0.97826087 1. 1. ] mean value: 0.9831521739130433 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( key: test_jcc value: [1. 0.91666667 0.95833333 0.91666667 0.95833333 0.95833333 1. 0.96 1. 1. ] mean value: 0.9668333333333333 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 231 mean value: 231.0 key: FP value: 3 mean value: 3.0 key: FN value: 5 mean value: 5.0 key: TP value: 233 mean value: 233.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.7 Accuracy on Blind test: 0.91 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.91157484 0.93154073 0.91679835 0.99111915 0.93584204 0.93280792 0.90386271 0.93082833 0.93126726 0.91790628] mean value: 0.9303547620773316 key: score_time value: [0.21734333 0.20787477 0.17531276 0.21779132 0.21399546 0.2189126 0.19838953 0.20722294 0.19999313 0.203017 ] mean value: 0.20598528385162354 key: test_mcc value: [0.9591663 0.87576054 0.95833333 0.91485507 0.8729597 0.95833333 1. 0.95825929 1. 0.91804649] mean value: 0.9415714063303422 key: train_mcc value: [0.98594778 0.98594778 0.99063185 0.99063185 0.99063185 0.99530506 0.99063227 0.98589335 0.98589335 0.985981 ] mean value: 0.9887496142619037 key: test_fscore value: [0.97959184 0.93617021 0.9787234 0.95652174 0.93333333 0.9787234 1. 0.97959184 1. 0.96 ] mean value: 0.9702655767209751 key: train_fscore value: [0.99297424 0.99297424 0.9953271 0.9953271 0.9953271 0.99765808 0.99530516 0.99294118 0.99294118 0.99297424] mean value: 0.9943749621924566 key: test_precision value: [0.96 0.95652174 0.95833333 0.95652174 0.95454545 0.95833333 1. 0.96 1. 0.92307692] mean value: 0.9627332522549914 key: train_precision value: [0.98604651 0.98604651 0.99069767 0.99069767 0.99069767 0.9953271 0.99065421 0.99061033 0.99061033 0.98604651] mean value: 0.9897434523827744 key: test_recall value: [1. 0.91666667 1. 0.95652174 0.91304348 1. 1. 1. 1. 1. ] mean value: 0.9786231884057971 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 0.99528302 0.99528302 1. ] mean value: 0.999056603773585 key: test_accuracy value: [0.97916667 0.9375 0.9787234 0.95744681 0.93617021 0.9787234 1. 0.9787234 1. 0.95744681] mean value: 0.9703900709219859 key: train_accuracy value: [0.99292453 0.99292453 0.99529412 0.99529412 0.99529412 0.99764706 0.99529412 0.99294118 0.99294118 0.99294118] mean value: 0.9943496115427303 key: test_roc_auc value: [0.97916667 0.9375 0.97916667 0.95742754 0.93568841 0.97916667 1. 0.97826087 1. 0.95652174] mean value: 0.9702898550724639 key: train_roc_auc value: [0.99292453 0.99292453 0.99528302 0.99528302 0.99528302 0.99764151 0.99530516 0.99294667 0.99294667 0.99295775] mean value: 0.9943495880946054 key: test_jcc value: [0.96 0.88 0.95833333 0.91666667 0.875 0.95833333 1. 0.96 1. 0.92307692] mean value: 0.9431410256410256 key: train_jcc value: [0.98604651 0.98604651 0.99069767 0.99069767 0.99069767 0.9953271 0.99065421 0.98598131 0.98598131 0.98604651] mean value: 0.9888176483373179 key: TN value: 227 mean value: 227.0 key: FP value: 5 mean value: 5.0 key: FN value: 9 mean value: 9.0 key: TP value: 231 mean value: 231.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.75 Accuracy on Blind test: 0.92 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.08122516 0.0644002 0.06695461 0.06452489 0.06639028 0.06484818 0.066329 0.0656693 0.06637669 0.0666132 ] mean value: 0.06733314990997315 key: score_time value: [0.01088309 0.010566 0.01057339 0.01108551 0.01054788 0.01051974 0.01045942 0.01042914 0.01063132 0.01047158] mean value: 0.010616707801818847 key: test_mcc value: [0.9591663 1. 0.95833333 0.95825929 0.95833333 0.95833333 1. 0.95825929 1. 1. ] mean value: 0.9750684887473801 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97959184 1. 0.9787234 0.97777778 0.9787234 0.9787234 1. 0.97959184 1. 1. ] mean value: 0.9873131664013123 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.96 1. 0.95833333 1. 0.95833333 0.95833333 1. 0.96 1. 1. ] mean value: 0.9795 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.95652174 1. 1. 1. 1. 1. 1. ] mean value: 0.9956521739130434 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97916667 1. 0.9787234 0.9787234 0.9787234 0.9787234 1. 0.9787234 1. 1. ] mean value: 0.9872783687943262 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97916667 1. 0.97916667 0.97826087 0.97916667 0.97916667 1. 0.97826087 1. 1. ] mean value: 0.9873188405797102 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.96 1. 0.95833333 0.95652174 0.95833333 0.95833333 1. 0.96 1. 1. ] mean value: 0.9751521739130435 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 231 mean value: 231.0 key: FP value: 1 mean value: 1.0 key: FN value: 5 mean value: 5.0 key: TP value: 235 mean value: 235.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.85 Accuracy on Blind test: 0.95 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.0380466 0.0955472 0.06435776 0.05732799 0.03821635 0.03762507 0.09199166 0.03993559 0.03922415 0.08888984] mean value: 0.05911622047424316 key: score_time value: [0.02059531 0.0233674 0.02036071 0.01197243 0.01196742 0.0157702 0.01203489 0.0119803 0.02232051 0.01203489] mean value: 0.016240406036376952 key: test_mcc value: [0.91986621 0.9591663 0.87979456 0.8729597 0.95833333 0.84254172 0.91804649 0.84147165 1. 0.84147165] mean value: 0.9033651606243817 key: train_mcc value: [0.97208751 0.97668677 0.98134942 0.98134942 0.98598008 0.98598008 0.97674215 0.98135106 0.97674215 0.97674215] mean value: 0.9795010789882053 key: test_fscore value: [0.96 0.97959184 0.93877551 0.93333333 0.9787234 0.92 0.96 0.92307692 1. 0.92307692] mean value: 0.9516577930681274 key: train_fscore value: [0.98604651 0.98834499 0.99069767 0.99069767 0.99300699 0.99300699 0.98834499 0.99065421 0.98834499 0.98834499] mean value: 0.9897490005466532 key: test_precision value: [0.92307692 0.96 0.88461538 0.95454545 0.95833333 0.85185185 0.92307692 0.85714286 1. 0.85714286] mean value: 0.9169785584785586 key: train_precision value: [0.97247706 0.97695853 0.98156682 0.98156682 0.98611111 0.98611111 0.97695853 0.98148148 0.97695853 0.97695853] mean value: 0.9797148509859372 key: test_recall value: [1. 1. 1. 0.91304348 1. 1. 1. 1. 1. 1. ] mean value: 0.9913043478260869 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95833333 0.97916667 0.93617021 0.93617021 0.9787234 0.91489362 0.95744681 0.91489362 1. 0.91489362] mean value: 0.9490691489361701 key: train_accuracy value: [0.98584906 0.98820755 0.99058824 0.99058824 0.99294118 0.99294118 0.98823529 0.99058824 0.98823529 0.98823529] mean value: 0.9896409544950057 key: test_roc_auc value: [0.95833333 0.97916667 0.9375 0.93568841 0.97916667 0.91666667 0.95652174 0.91304348 1. 0.91304348] mean value: 0.9489130434782608 key: train_roc_auc value: [0.98584906 0.98820755 0.99056604 0.99056604 0.99292453 0.99292453 0.98826291 0.99061033 0.98826291 0.98826291] mean value: 0.989643679688192 key: test_jcc value: [0.92307692 0.96 0.88461538 0.875 0.95833333 0.85185185 0.92307692 0.85714286 1. 0.85714286] mean value: 0.9090240130240129 key: train_jcc value: [0.97247706 0.97695853 0.98156682 0.98156682 0.98611111 0.98611111 0.97695853 0.98148148 0.97695853 0.97695853] mean value: 0.9797148509859372 key: TN value: 214 mean value: 214.0 key: FP value: 2 mean value: 2.0 key: FN value: 22 mean value: 22.0 key: TP value: 234 mean value: 234.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.7 Accuracy on Blind test: 0.9 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01346302 0.01294136 0.00970078 0.00945663 0.00930452 0.00928974 0.00934792 0.00945902 0.0093205 0.00939202] mean value: 0.010167551040649415 key: score_time value: [0.01154852 0.01079345 0.00881791 0.00856185 0.00858855 0.00848055 0.00855136 0.00852346 0.00851059 0.00855494] mean value: 0.009093117713928223 key: test_mcc value: [0.37532595 0.41812101 0.4899891 0.53176131 0.31884058 0.65942029 0.4121128 0.67037015 0.40653424 0.19490273] mean value: 0.4477378148478753 key: train_mcc value: [0.49998299 0.53349983 0.55776302 0.5578964 0.49179493 0.50351826 0.47945542 0.55012068 0.5560934 0.52144501] mean value: 0.52515699447063 key: test_fscore value: [0.69387755 0.69565217 0.72727273 0.75555556 0.65217391 0.82608696 0.68181818 0.81818182 0.69565217 0.57777778] mean value: 0.7124048829017772 key: train_fscore value: [0.72911392 0.76144578 0.77725118 0.77619048 0.74528302 0.73891626 0.72592593 0.7635468 0.76309227 0.73657289] mean value: 0.7517338526541064 key: test_precision value: [0.68 0.72727273 0.76190476 0.77272727 0.65217391 0.82608696 0.75 0.9 0.72727273 0.61904762] mean value: 0.7416485977790325 key: train_precision value: [0.78688525 0.77832512 0.784689 0.78743961 0.74881517 0.77720207 0.76165803 0.79896907 0.80952381 0.80446927] mean value: 0.7837976402731726 key: test_recall value: [0.70833333 0.66666667 0.69565217 0.73913043 0.65217391 0.82608696 0.625 0.75 0.66666667 0.54166667] mean value: 0.6871376811594203 key: train_recall value: [0.67924528 0.74528302 0.76995305 0.76525822 0.74178404 0.70422535 0.69339623 0.73113208 0.72169811 0.67924528] mean value: 0.7231220657276995 key: test_accuracy value: [0.6875 0.70833333 0.74468085 0.76595745 0.65957447 0.82978723 0.70212766 0.82978723 0.70212766 0.59574468] mean value: 0.7225620567375886 key: train_accuracy value: [0.74764151 0.76650943 0.77882353 0.77882353 0.74588235 0.75058824 0.73882353 0.77411765 0.77647059 0.75764706] mean value: 0.7615327413984463 key: test_roc_auc value: [0.6875 0.70833333 0.74365942 0.76539855 0.65942029 0.82971014 0.70380435 0.83152174 0.70289855 0.59692029] mean value: 0.7229166666666667 key: train_roc_auc value: [0.74764151 0.76650943 0.77884445 0.77885552 0.74589202 0.75069758 0.73871689 0.77401674 0.77634201 0.75746302] mean value: 0.7614979183275754 key: test_jcc value: [0.53125 0.53333333 0.57142857 0.60714286 0.48387097 0.7037037 0.51724138 0.69230769 0.53333333 0.40625 ] mean value: 0.5579861838301772 key: train_jcc value: [0.57370518 0.61478599 0.63565891 0.63424125 0.59398496 0.5859375 0.56976744 0.61752988 0.61693548 0.58299595] mean value: 0.6025542551398176 key: TN value: 179 mean value: 179.0 key: FP value: 74 mean value: 74.0 key: FN value: 57 mean value: 57.0 key: TP value: 162 mean value: 162.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.29 Accuracy on Blind test: 0.72 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01703286 0.02359843 0.02437735 0.02722859 0.02431583 0.02274323 0.02081561 0.02550769 0.02908754 0.02610683] mean value: 0.02408139705657959 key: score_time value: [0.00865674 0.01090193 0.01216245 0.01233125 0.01168108 0.01177025 0.01205945 0.01162004 0.01164269 0.01160455] mean value: 0.011443042755126953 key: test_mcc value: [0.9591663 1. 0.95833333 0.87318841 0.95833333 0.95833333 0.29857543 0.87917396 1. 0.87917396] mean value: 0.8764278068547181 key: train_mcc value: [0.98594778 0.98130676 0.98598008 0.98598008 0.98598008 0.97180822 0.35766119 0.985981 0.96758624 0.985981 ] mean value: 0.9194212407461956 key: test_fscore value: [0.97959184 1. 0.9787234 0.93617021 0.9787234 0.9787234 0.28571429 0.94117647 1. 0.94117647] mean value: 0.9019999489157364 key: train_fscore value: [0.99297424 0.99065421 0.99300699 0.99300699 0.99300699 0.98584906 0.36923077 0.99297424 0.9837587 0.99297424] mean value: 0.9287436427786687 key: test_precision value: [0.96 1. 0.95833333 0.91666667 0.95833333 0.95833333 1. 0.88888889 1. 0.88888889] mean value: 0.9529444444444444 key: train_precision value: [0.98604651 0.98148148 0.98611111 0.98611111 0.98611111 0.99052133 1. 0.98604651 0.96803653 0.98604651] mean value: 0.9856512206393118 key: test_recall value: [1. 1. 1. 0.95652174 1. 1. 0.16666667 1. 1. 1. ] mean value: 0.9123188405797101 key: train_recall value: [1. 1. 1. 1. 1. 0.98122066 0.22641509 1. 1. 1. ] mean value: 0.9207635751616617 key: test_accuracy value: [0.97916667 1. 0.9787234 0.93617021 0.9787234 0.9787234 0.57446809 0.93617021 1. 0.93617021] mean value: 0.929831560283688 key: train_accuracy value: [0.99292453 0.99056604 0.99294118 0.99294118 0.99294118 0.98588235 0.61411765 0.99294118 0.98352941 0.99294118] mean value: 0.9531725860155383 key: test_roc_auc value: [0.97916667 1. 0.97916667 0.9365942 0.97916667 0.97916667 0.58333333 0.93478261 1. 0.93478261] mean value: 0.9306159420289856 key: train_roc_auc value: [0.99292453 0.99056604 0.99292453 0.99292453 0.99292453 0.98589335 0.61320755 0.99295775 0.98356808 0.99295775] mean value: 0.9530848613694747 key: test_jcc value: [0.96 1. 0.95833333 0.88 0.95833333 0.95833333 0.16666667 0.88888889 1. 0.88888889] mean value: 0.8659444444444444 key: train_jcc value: [0.98604651 0.98148148 0.98611111 0.98611111 0.98611111 0.97209302 0.22641509 0.98604651 0.96803653 0.98604651] mean value: 0.9064498996974336 key: TN value: 224 mean value: 224.0 key: FP value: 21 mean value: 21.0 key: FN value: 12 mean value: 12.0 key: TP value: 215 mean value: 215.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.79 Accuracy on Blind test: 0.92 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01574564 0.01656222 0.01745152 0.01749682 0.01709723 0.01979041 0.01725698 0.01651931 0.01879168 0.01649356] mean value: 0.017320537567138673 key: score_time value: [0.01179242 0.01166368 0.01182699 0.01172543 0.01172519 0.01178741 0.01175237 0.01176429 0.01189065 0.01179552] mean value: 0.011772394180297852 key: test_mcc value: [0.77459667 1. 0.95833333 0.75474102 0.95833333 0.95833333 0.29857543 0.73387289 1. 0.62296012] mean value: 0.8059746130758967 key: train_mcc value: [0.94939206 0.95299984 0.98134942 0.95774367 0.96758129 0.98598008 0.42978041 0.84691467 0.96706971 0.89968417] mean value: 0.8938495321242821 key: test_fscore value: [0.88888889 1. 0.9787234 0.85714286 0.9787234 0.9787234 0.28571429 0.87272727 1. 0.8 ] mean value: 0.8640643517239261 key: train_fscore value: [0.97471264 0.97619048 0.99069767 0.9787234 0.98383372 0.99300699 0.47482014 0.92374728 0.98352941 0.94581281] mean value: 0.9225074550014183 key: test_precision value: [0.8 1. 0.95833333 0.94736842 0.95833333 0.95833333 1. 0.77419355 1. 0.85714286] mean value: 0.9253704826582586 key: train_precision value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] [0.95067265 0.98557692 0.98156682 0.98571429 0.96818182 0.98611111 1. 0.8582996 0.98122066 0.98969072] mean value: 0.9687034578168727 key: test_recall value: [1. 1. 1. 0.7826087 1. 1. 0.16666667 1. 1. 0.75 ] mean value: 0.869927536231884 key: train_recall value: [1. 0.96698113 1. 0.97183099 1. 1. 0.31132075 1. 0.98584906 0.90566038] mean value: 0.914164230667021 key: test_accuracy value: [0.875 1. 0.9787234 0.87234043 0.9787234 0.9787234 0.57446809 0.85106383 1. 0.80851064] mean value: 0.8917553191489361 key: train_accuracy value: [0.9740566 0.97641509 0.99058824 0.97882353 0.98352941 0.99294118 0.65647059 0.91764706 0.98352941 0.94823529] mean value: 0.9402236403995563 key: test_roc_auc value: [0.875 1. 0.97916667 0.87047101 0.97916667 0.97916667 0.58333333 0.84782609 1. 0.80978261] mean value: 0.8923913043478262 key: train_roc_auc value: [0.9740566 0.97641509 0.99056604 0.97884002 0.98349057 0.99292453 0.65566038 0.91784038 0.98353486 0.94813535] mean value: 0.9401463814332536 key: test_jcc value: [0.8 1. 0.95833333 0.75 0.95833333 0.95833333 0.16666667 0.77419355 1. 0.66666667] mean value: 0.8032526881720431 key: train_jcc value: [0.95067265 0.95348837 0.98156682 0.95833333 0.96818182 0.98611111 0.31132075 0.8582996 0.96759259 0.89719626] mean value: 0.8832763304869211 key: TN value: 216 mean value: 216.0 key: FP value: 31 mean value: 31.0 key: FN value: 20 mean value: 20.0 key: TP value: 205 mean value: 205.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.46 Accuracy on Blind test: 0.84 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.1704247 0.15489531 0.15418124 0.15523338 0.1523037 0.15141606 0.1532445 0.15418482 0.15686536 0.15323281] mean value: 0.15559818744659423 key: score_time value: [0.01525044 0.01562023 0.01537919 0.01555705 0.01544309 0.01569819 0.01545882 0.01533937 0.01538706 0.01550388] mean value: 0.015463733673095703 key: test_mcc value: [0.9591663 1. 0.95833333 0.91485507 0.95833333 0.95833333 1. 0.95825929 1. 1. ] mean value: 0.970728066853194 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97959184 1. 0.9787234 0.95652174 0.9787234 0.9787234 1. 0.97959184 1. 1. ] mean value: 0.985187562536578 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.96 1. 0.95833333 0.95652174 0.95833333 0.95833333 1. 0.96 1. 1. ] mean value: 0.9751521739130435 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.95652174 1. 1. 1. 1. 1. 1. ] mean value: 0.9956521739130434 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97916667 1. 0.9787234 0.95744681 0.9787234 0.9787234 1. 0.9787234 1. 1. ] mean value: 0.9851507092198581 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97916667 1. 0.97916667 0.95742754 0.97916667 0.97916667 1. 0.97826087 1. 1. ] mean value: 0.985235507246377 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.96 1. 0.95833333 0.91666667 0.95833333 0.95833333 1. 0.96 1. 1. ] mean value: 0.9711666666666666 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 230 mean value: 230.0 key: FP value: 1 mean value: 1.0 key: FN value: 6 mean value: 6.0 key: TP value: 235 mean value: 235.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.78 Accuracy on Blind test: 0.93 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.0479846 0.05868626 0.06571293 0.05242252 0.05591035 0.05214858 0.06340671 0.05452132 0.05256462 0.05731559] mean value: 0.05606734752655029 key: score_time value: [0.02042913 0.03717065 0.02822971 0.02923608 0.02547407 0.03469443 0.02357244 0.01914525 0.02023339 0.0177238 ] mean value: 0.025590896606445312 key: test_mcc value: [0.9591663 1. 0.95833333 0.91485507 0.95833333 0.8729597 1. 0.95825929 1. 0.95825929] mean value: 0.9580166321994591 key: train_mcc value: [0.99529409 0.99056604 0.99058818 0.99063185 0.99530516 0.98589335 1. 0.99530506 0.99530506 0.99530506] mean value: 0.9934193859946688 key: test_fscore value: [0.97959184 1. 0.9787234 0.95652174 0.9787234 0.93333333 1. 0.97959184 1. 0.97959184] mean value: 0.9786077391178487 key: train_fscore value: [0.99763593 0.99528302 0.99530516 0.9953271 0.99764706 0.99294118 1. 0.99763593 0.99763593 0.99763593] mean value: 0.9967047256509615 key: test_precision value: [0.96 1. 0.95833333 0.95652174 0.95833333 0.95454545 1. 0.96 1. 0.96 ] mean value: 0.9707733860342558 key: train_precision value: [1. 0.99528302 0.99530516 0.99069767 1. 0.99528302 1. 1. 1. 1. ] mean value: 0.9976568876473703 key: test_recall value: [1. 1. 1. 0.95652174 1. 0.91304348 1. 1. 1. 1. ] mean value: 0.9869565217391305 key: train_recall value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] [0.99528302 0.99528302 0.99530516 1. 0.99530516 0.99061033 1. 0.99528302 0.99528302 0.99528302] mean value: 0.9957635751616618 key: test_accuracy value: [0.97916667 1. 0.9787234 0.95744681 0.9787234 0.93617021 1. 0.9787234 1. 0.9787234 ] mean value: 0.9787677304964539 key: train_accuracy value: [0.99764151 0.99528302 0.99529412 0.99529412 0.99764706 0.99294118 1. 0.99764706 0.99764706 0.99764706] mean value: 0.996704217536071 key: test_roc_auc value: [0.97916667 1. 0.97916667 0.95742754 0.97916667 0.93568841 1. 0.97826087 1. 0.97826087] mean value: 0.978713768115942 key: train_roc_auc value: [0.99764151 0.99528302 0.99529409 0.99528302 0.99765258 0.99294667 1. 0.99764151 0.99764151 0.99764151] mean value: 0.996702542297812 key: test_jcc value: [0.96 1. 0.95833333 0.91666667 0.95833333 0.875 1. 0.96 1. 0.96 ] mean value: 0.9588333333333334 key: train_jcc value: [0.99528302 0.99061033 0.99065421 0.99069767 0.99530516 0.98598131 1. 0.99528302 0.99528302 0.99528302] mean value: 0.9934380756866741 key: TN value: 229 mean value: 229.0 key: FP value: 3 mean value: 3.0 key: FN value: 7 mean value: 7.0 key: TP value: 233 mean value: 233.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.83 Accuracy on Blind test: 0.94 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.17006922 0.20242786 0.20251346 0.20794773 0.19582844 0.158283 0.16847277 0.16146183 0.14422131 0.1402185 ] mean value: 0.17514441013336182 key: score_time value: [0.02313662 0.02319384 0.02319884 0.0233531 0.02322769 0.02406597 0.02945781 0.02915096 0.02370977 0.02334666] mean value: 0.024584126472473145 key: test_mcc value: [0.60246408 0.83333333 0.95833333 0.65942029 0.80641033 1. 0.7023605 0.79308818 0.74773263 0.57227835] mean value: 0.7675421024291873 key: train_mcc value: [0.92933834 0.92518727 0.93907982 0.92000886 0.95399286 0.95399286 0.92050525 0.94390914 0.90604652 0.93933764] mean value: 0.9331398559963876 key: test_fscore value: [0.81481481 0.91666667 0.9787234 0.82608696 0.90196078 1. 0.85714286 0.90196078 0.86956522 0.80701754] mean value: 0.8873939029279801 key: train_fscore value: [0.96487119 0.96296296 0.96983759 0.96018735 0.97706422 0.97706422 0.96055684 0.97209302 0.95327103 0.96983759] mean value: 0.9667746021193985 key: test_precision value: [0.73333333 0.91666667 0.95833333 0.82608696 0.82142857 1. 0.84 0.85185185 0.90909091 0.6969697 ] mean value: 0.8553761319196103 key: train_precision value: [0.95813953 0.94545455 0.9587156 0.95794393 0.95515695 0.95515695 0.94520548 0.9587156 0.94444444 0.9543379 ] mean value: 0.9533270923017632 key: test_recall value: [0.91666667 0.91666667 1. 0.82608696 1. 1. 0.875 0.95833333 0.83333333 0.95833333] mean value: 0.9284420289855074 key: train_recall value: [0.97169811 0.98113208 0.98122066 0.96244131 1. 1. 0.97641509 0.98584906 0.96226415 0.98584906] mean value: 0.9806869519000797 key: test_accuracy value: [0.79166667 0.91666667 0.9787234 0.82978723 0.89361702 1. 0.85106383 0.89361702 0.87234043 0.76595745] mean value: 0.8793439716312056 key: train_accuracy value: [0.96462264 0.96226415 0.96941176 0.96 0.97647059 0.97647059 0.96 0.97176471 0.95294118 0.96941176] mean value: 0.9663357380688125 key: test_roc_auc value: [0.79166667 0.91666667 0.97916667 0.82971014 0.89583333 1. 0.85054348 0.89221014 0.87318841 0.76177536] mean value: 0.8790760869565217 key: train_roc_auc value: [0.96462264 0.96226415 0.96938391 0.95999424 0.97641509 0.97641509 0.96003853 0.97179777 0.95296306 0.96945035] mean value: 0.9663344848968022 key: test_jcc value: [0.6875 0.84615385 0.95833333 0.7037037 0.82142857 1. 0.75 0.82142857 0.76923077 0.67647059] mean value: 0.8034249383514089 key: train_jcc value: [0.9321267 0.92857143 0.94144144 0.92342342 0.95515695 0.95515695 0.92410714 0.94570136 0.91071429 0.94144144] mean value: 0.9357841119093099 key: TN value: 196 mean value: 196.0 key: FP value: 17 mean value: 17.0 key: FN value: 40 mean value: 40.0 key: TP value: 219 mean value: 219.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.07 Accuracy on Blind test: 0.68 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.59148049 0.55282736 0.5599308 0.55353999 0.55820084 0.54877162 0.55891895 0.55548716 0.56001878 0.56187654] mean value: 0.5601052522659302 key: score_time value: [0.00914335 0.00937653 0.00928164 0.00906444 0.00925207 0.00907731 0.00926995 0.00922632 0.00914979 0.00905776] mean value: 0.009189915657043458 key: test_mcc value: [0.9591663 1. 0.95833333 0.91485507 0.95833333 0.91833182 1. 0.95825929 1. 0.95825929] mean value: 0.9625538447534631 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97959184 1. 0.9787234 0.95652174 0.9787234 0.95833333 1. 0.97959184 1. 0.97959184] mean value: 0.9811077391178488 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.96 1. 0.95833333 0.95652174 0.95833333 0.92 1. 0.96 1. 0.96 ] mean value: 0.9673188405797102 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.95652174 1. 1. 1. 1. 1. 1. ] mean value: 0.9956521739130434 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") [0.97916667 1. 0.9787234 0.95744681 0.9787234 0.95744681 1. 0.9787234 1. 0.9787234 ] mean value: 0.980895390070922 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97916667 1. 0.97916667 0.95742754 0.97916667 0.95833333 1. 0.97826087 1. 0.97826087] mean value: 0.9809782608695652 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.96 1. 0.95833333 0.91666667 0.95833333 0.92 1. 0.96 1. 0.96 ] mean value: 0.9633333333333333 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 228 mean value: 228.0 key: FP value: 1 mean value: 1.0 key: FN value: 8 mean value: 8.0 key: TP value: 235 mean value: 235.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.85 Accuracy on Blind test: 0.95 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02428985 0.02749515 0.02805972 0.02740002 0.02796483 0.03774118 0.03575802 0.0446558 0.03393126 0.03918934] mean value: 0.03264851570129394 key: score_time value: [0.01221967 0.01220965 0.01315665 0.01267314 0.01473403 0.01557922 0.01255774 0.01477599 0.01570821 0.0162406 ] mean value: 0.013985490798950196 key: test_mcc value: [1. 0.83624201 1. 0.82971014 0.91804649 1. 1. 0.91485507 1. 1. ] mean value: 0.9498853721073142 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.91304348 1. 0.91304348 0.95454545 1. 1. 0.95833333 1. 1. ] mean value: 0.9738965744400527 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.95454545 1. 0.91304348 1. 1. 1. 0.95833333 1. 1. ] mean value: 0.9825922266139658 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.875 1. 0.91304348 0.91304348 1. 1. 0.95833333 1. 1. ] mean value: 0.9659420289855072 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.91666667 1. 0.91489362 0.95744681 1. 1. 0.95744681 1. 1. ] mean value: 0.974645390070922 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.91666667 1. 0.91485507 0.95652174 1. 1. 0.95742754 1. 1. ] mean value: 0.9745471014492754 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.84 1. 0.84 0.91304348 1. 1. 0.92 1. 1. ] mean value: 0.9513043478260869 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 233 mean value: 233.0 key: FP value: 8 mean value: 8.0 key: FN value: 3 mean value: 3.0 key: TP value: 228 mean value: 228.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: -0.04 Accuracy on Blind test: 0.78 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.01509428 0.01487875 0.01487017 0.03336596 0.03517532 0.03639007 0.03261375 0.03647685 0.03703356 0.03658605] mean value: 0.029248476028442383 key: score_time value: [0.01185608 0.0118258 0.0119319 0.02261043 0.0208478 0.02019501 0.02254701 0.02088499 0.02249098 0.02036333] mean value: 0.018555331230163574 key: test_mcc value: [0.9591663 0.9591663 0.95833333 0.83243502 0.91833182 0.95833333 0.87318841 0.91804649 1. 0.76896316] mean value: 0.9145964175646162 key: train_mcc value: [0.97668677 0.97668677 0.98134942 0.97193552 0.98134942 0.9767396 0.96715612 0.98135106 0.97674215 0.97674215] mean value: 0.9766738992560577 key: test_fscore value: [0.97959184 0.97959184 0.9787234 0.90909091 0.95833333 0.9787234 0.93617021 0.96 1. 0.88888889] mean value: 0.9569113826059116 key: train_fscore value: [0.98834499 0.98834499 0.99069767 0.98604651 0.99069767 0.98839907 0.98360656 0.99065421 0.98834499 0.98834499] mean value: 0.9883481648755348 key: test_precision value: [0.96 0.96 0.95833333 0.95238095 0.92 0.95833333 0.95652174 0.92307692 1. 0.8 ] mean value: 0.9388646281254978 key: train_precision value: [0.97695853 0.97695853 0.98156682 0.97695853 0.98156682 0.97706422 0.97674419 0.98148148 0.97695853 0.97695853] mean value: 0.9783216154992586 key: test_recall value: [1. 1. 1. 0.86956522 1. 1. 0.91666667 1. 1. 1. ] mean value: 0.9786231884057971 key: train_recall value: [1. 1. 1. 0.99530516 1. 1. 0.99056604 1. 1. 1. ] mean value: 0.9985871202055098 key: test_accuracy value: [0.97916667 0.97916667 0.9787234 0.91489362 0.95744681 0.9787234 0.93617021 0.95744681 1. 0.87234043] mean value: 0.9554078014184396 key: train_accuracy value: [0.98820755 0.98820755 0.99058824 0.98588235 0.99058824 0.98823529 0.98352941 0.99058824 0.98823529 0.98823529] mean value: 0.9882297447280799 key: test_roc_auc value: [0.97916667 0.97916667 0.97916667 0.91394928 0.95833333 0.97916667 0.9365942 0.95652174 1. 0.86956522] mean value: 0.955163043478261 key: train_roc_auc value: [0.98820755 0.98820755 0.99056604 0.98586013 0.99056604 0.98820755 0.98354593 0.99061033 0.98826291 0.98826291] mean value: 0.9882296926211355 key: test_jcc value: [0.96 0.96 0.95833333 0.83333333 0.92 0.95833333 0.88 0.92307692 1. 0.8 ] mean value: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:432: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:433: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_CV['Resampling'] = rs_rouC /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:438: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:439: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_BT['Resampling'] = rs_rouC 0.9193076923076925 key: train_jcc value: [0.97695853 0.97695853 0.98156682 0.97247706 0.98156682 0.97706422 0.96774194 0.98148148 0.97695853 0.97695853] mean value: 0.9769732443304507 key: TN value: 220 mean value: 220.0 key: FP value: 5 mean value: 5.0 key: FN value: 16 mean value: 16.0 key: TP value: 231 mean value: 231.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.78 Accuracy on Blind test: 0.92 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=168)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.25742292 0.25813818 0.26374173 0.35750985 0.27137733 0.13077354 0.26217246 0.20676613 0.25405955 0.25714278] mean value: 0.2519104480743408 key: score_time value: [0.02147627 0.02145267 0.02258754 0.02365971 0.0148344 0.012043 0.02242708 0.01192284 0.02006245 0.02252841] mean value: 0.01929943561553955 key: test_mcc value: [0.9591663 0.9591663 0.95833333 0.83243502 0.91833182 0.95833333 0.87318841 0.91804649 1. 0.76896316] mean value: 0.9145964175646162 key: train_mcc value: [0.97668677 0.97668677 0.98134942 0.97193552 0.98134942 0.9767396 0.96715612 0.98135106 0.97674215 0.97674215] mean value: 0.9766738992560577 key: test_fscore value: [0.97959184 0.97959184 0.9787234 0.90909091 0.95833333 0.9787234 0.93617021 0.96 1. 0.88888889] mean value: 0.9569113826059116 key: train_fscore value: [0.98834499 0.98834499 0.99069767 0.98604651 0.99069767 0.98839907 0.98360656 0.99065421 0.98834499 0.98834499] mean value: 0.9883481648755348 key: test_precision value: [0.96 0.96 0.95833333 0.95238095 0.92 0.95833333 0.95652174 0.92307692 1. 0.8 ] mean value: 0.9388646281254978 key: train_precision value: [0.97695853 0.97695853 0.98156682 0.97695853 0.98156682 0.97706422 0.97674419 0.98148148 0.97695853 0.97695853] mean value: 0.9783216154992586 key: test_recall value: [1. 1. 1. 0.86956522 1. 1. 0.91666667 1. 1. 1. ] mean value: 0.9786231884057971 key: train_recall value: [1. 1. 1. 0.99530516 1. 1. 0.99056604 1. 1. 1. ] mean value: 0.9985871202055098 key: test_accuracy value: [0.97916667 0.97916667 0.9787234 0.91489362 0.95744681 0.9787234 0.93617021 0.95744681 1. 0.87234043] mean value: 0.9554078014184396 key: train_accuracy value: [0.98820755 0.98820755 0.99058824 0.98588235 0.99058824 0.98823529 0.98352941 0.99058824 0.98823529 0.98823529] mean value: 0.9882297447280799 key: test_roc_auc value: [0.97916667 0.97916667 0.97916667 0.91394928 0.95833333 0.97916667 0.9365942 0.95652174 1. 0.86956522] mean value: 0.955163043478261 key: train_roc_auc value: [0.98820755 0.98820755 0.99056604 0.98586013 0.99056604 0.98820755 0.98354593 0.99061033 0.98826291 0.98826291] mean value: 0.9882296926211355 key: test_jcc value: [0.96 0.96 0.95833333 0.83333333 0.92 0.95833333 0.88 0.92307692 1. 0.8 ] mean value: 0.9193076923076925 key: train_jcc value: [0.97695853 0.97695853 0.98156682 0.97247706 0.98156682 0.97706422 0.96774194 0.98148148 0.97695853 0.97695853] mean value: 0.9769732443304507 key: TN value: 220 mean value: 220.0 key: FP value: 5 mean value: 5.0 key: FN value: 16 mean value: 16.0 key: TP value: 231 mean value: 231.0 key: trainingY_neg value: 236 mean value: 236.0 key: trainingY_pos value: 236 mean value: 236.0 key: blindY_neg value: 117 mean value: 117.0 key: blindY_pos value: 31 mean value: 31.0 MCC on Blind test: 0.78 Accuracy on Blind test: 0.92 PASS: sorting df by score that is mapped onto the order I want Concatenating dfs with different resampling methods [WF]: 70/30 No. of dfs combining: 10 The sampling methods are: Resampling Logistic Regression none Logistic Regression smnc Logistic Regression ros Logistic Regression rus Logistic Regression rouC PASS: 10 dfs successfully combined nrows in combined_df_wf: 240 ncols in combined_df_wf: 9 Concatenating dfs with different resampling methods: 70/30 No. of dfs combining: 5 The sampling methods are: Resampling training_size 0 none 300 24 smnc 472 48 ros 472 72 rus 128 96 rouC 472 PASS: 5 dfs successfully combined nrows in combined_df: 120 ncols in combined_df: 32 File successfully written: /home/tanu/git/Data/ethambutol/output/ml/tts_7030/embb_baselineC_7030.csv File successfully written: /home/tanu/git/Data/ethambutol/output/ml/tts_7030/embb_baselineC_ext_7030.csv