/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_7030.py:464: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index 1.22.4 1.4.1 aaindex_df contains non-numerical data Total no. of non-numerial columns: 2 Selecting numerical data only PASS: successfully selected numerical columns only for aaindex_df Now checking for NA in the remaining aaindex_cols Counting aaindex_df cols with NA ncols with NA: 4 columns Dropping these... Original ncols: 127 Revised df ncols: 123 Checking NA in revised df... PASS: cols with NA successfully dropped from aaindex_df Proceeding with combining aa_df with other features_df PASS: ncols match Expected ncols: 123 Got: 123 Total no. of columns in clean aa_df: 123 Proceeding to merge, expected nrows in merged_df: 531 PASS: my_features_df and aa_df successfully combined nrows: 531 ncols: 286 count of NULL values before imputation or_mychisq 263 log10_or_mychisq 263 dtype: int64 count of NULL values AFTER imputation mutationinformation 0 or_rawI 0 logorI 0 dtype: int64 PASS: OR values imputed, data ready for ML Total no. of features for aaindex: 123 PASS: x_features has no target variable No. of columns for x_features: 173 PASS: ML data with input features, training and test generated... Total no. of input features: 173 --------No. of numerical features: 167 --------No. of categorical features: 6 Total no. of evolutionary features: 3 Total no. of stability features: 28 --------Common stabilty cols: 5 --------Foldx cols: 23 Total no. of affinity features: 4 --------Common affinity cols: 3 --------Gene specific affinity cols: 1 Total no. of residue level features: 132 --------AA index cols: 123 --------Residue Prop cols: 3 --------AA change Prop cols: 6 Total no. of genomic features: 6 --------MAF+OR cols: 2 --------Lineage cols: 4 --------Other cols: 0 ------------------------------------------------------------- Successfully split data: ALL features actual values: training set imputed values: blind test set Total data size: 119 Train data size: (79, 173) y_train numbers: Counter({0: 50, 1: 29}) Test data size: (40, 173) y_test_numbers: Counter({0: 26, 1: 14}) y_train ratio: 1.7241379310344827 y_test ratio: 1.8571428571428572 ------------------------------------------------------------- index: 0 ind: 1 Mask count check: True index: 1 ind: 2 Mask count check: True Original Data Counter({0: 50, 1: 29}) Data dim: (79, 173) Simple Random OverSampling Counter({1: 50, 0: 50}) (100, 173) Simple Random UnderSampling Counter({0: 29, 1: 29}) (58, 173) Simple Combined Over and UnderSampling Counter({0: 50, 1: 50}) (100, 173) SMOTE_NC OverSampling Counter({1: 50, 0: 50}) (100, 173) ##################################################################### Running ML analysis: feature groups Gene name: gid Drug name: streptomycin Output directory: /home/tanu/git/Data/streptomycin/output/ml/tts_7030/ ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( key: fit_time value: [0.0242238 0.03297615 0.02694082 0.02446318 0.02704215 0.04720712 0.03565717 0.02282596 0.04037237 0.05070901] mean value: 0.033241772651672365 key: score_time value: [0.0121789 0.01169276 0.01170754 0.011518 0.01153922 0.0120914 0.01173162 0.01155663 0.01343775 0.0119257 ] mean value: 0.011937952041625977 key: test_mcc value: [ 0.74535599 0.46666667 0.74535599 -0.06666667 0.46666667 0.25819889 0.48795004 -0.29277002 0.48795004 0.3 ] mean value: 0.35987075924776607 key: train_mcc value: [0.94010481 0.91067388 0.94010481 0.94010481 0.90865445 0.96986363 0.91067388 0.90865445 0.96986363 0.97058178] mean value: 0.9369280134082192 key: test_fscore value: [0.8 0.66666667 0.8 0.33333333 0.66666667 0.57142857 0.5 0. 0.5 0.5 ] mean value: 0.5338095238095238 key: train_fscore value: [0.96 0.93877551 0.96 0.96 0.94117647 0.98039216 0.93877551 0.94117647 0.98039216 0.98113208] mean value: 0.9581820350781823 key: test_precision value: [1. 0.66666667 1. 0.33333333 0.66666667 0.5 1. 0. 1. 0.5 ] mean value: 0.6666666666666666 key: train_precision value: [1. 1. 1. 1. 0.96 1. 1. 0.96 1. 1. ] mean value: 0.992 key: test_recall value: [0.66666667 0.66666667 0.66666667 0.33333333 0.66666667 0.66666667 0.33333333 0. 0.33333333 0.5 ] mean value: 0.4833333333333333 key: train_recall value: [0.92307692 0.88461538 0.92307692 0.92307692 0.92307692 0.96153846 0.88461538 0.92307692 0.96153846 0.96296296] mean value: 0.9270655270655273 key: test_accuracy value: [0.875 0.75 0.875 0.5 0.75 0.625 0.75 0.5 0.75 0.71428571] mean value: 0.7089285714285715 key: train_accuracy value: [0.97183099 0.95774648 0.97183099 0.97183099 0.95774648 0.98591549 0.95774648 0.95774648 0.98591549 0.98611111] mean value: 0.9704420970266041 key: test_roc_auc value: [0.83333333 0.73333333 0.83333333 0.46666667 0.73333333 0.63333333 0.66666667 0.4 0.66666667 0.65 ] mean value: 0.6616666666666667 key: train_roc_auc value: [0.96153846 0.94230769 0.96153846 0.96153846 0.95042735 0.98076923 0.94230769 0.95042735 0.98076923 0.98148148] mean value: 0.9613105413105412 key: test_jcc value: [0.66666667 0.5 0.66666667 0.2 0.5 0.4 0.33333333 0. 0.33333333 0.33333333] mean value: 0.39333333333333337 key: train_jcc value: [0.92307692 0.88461538 0.92307692 0.92307692 0.88888889 0.96153846 0.88461538 0.88888889 0.96153846 0.96296296] mean value: 0.9202279202279204 key: TN value: 42 mean value: 42.0 key: FP value: 15 mean value: 15.0 key: FN value: 8 mean value: 8.0 key: TP value: 14 mean value: 14.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.36 Accuracy on Blind test: 0.72 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.92556214 0.62680626 0.74799728 0.69733357 0.53420568 0.79944897 0.80795002 0.70474362 0.61180806 0.56007266] mean value: 0.7015928268432617 key: score_time value: [0.01263237 0.01342344 0.01227355 0.01217771 0.01460266 0.01221347 0.01453018 0.01216531 0.01223826 0.01225662] mean value: 0.012851357460021973 key: test_mcc value: [ 0. 0.46666667 0.48795004 -0.06666667 0.46666667 0.6 1. 0. 0.1490712 0. ] mean value: 0.3103687901640919 key: train_mcc value: [0. 1. 0.79523358 0.72919664 1. 1. 1. 0.72919664 0.96986363 0. ] mean value: 0.7223490489333553 key: test_fscore value: [0. 0.66666667 0.5 0.33333333 0.66666667 0.75 1. 0. 0.4 0. ] mean value: 0.43166666666666664 key: train_fscore value: [0. 1. 0.84444444 0.8 1. 1. 1. 0.8 0.98039216 0. ] mean value: 0.742483660130719 key: test_precision value: [0. 0.66666667 1. 0.33333333 0.66666667 0.6 1. 0. 0.5 0. ] mean value: 0.4766666666666667 key: train_precision value: [0. 1. 1. 0.94736842 1. 1. 1. 0.94736842 1. 0. ] mean value: 0.7894736842105263 key: test_recall value: [0. 0.66666667 0.33333333 0.33333333 0.66666667 1. 1. 0. 0.33333333 0. ] mean value: 0.4333333333333333 key: train_recall value: [0. 1. 0.73076923 0.69230769 1. 1. 1. 0.69230769 0.96153846 0. ] mean value: 0.7076923076923076 key: test_accuracy value: [0.625 0.75 0.75 0.5 0.75 0.75 1. 0.625 0.625 0.71428571] mean value: 0.7089285714285715 key: train_accuracy value: [0.63380282 1. 0.90140845 0.87323944 1. 1. 1. 0.87323944 0.98591549 0.625 ] mean value: 0.8892605633802816 key: test_roc_auc value: [0.5 0.73333333 0.66666667 0.46666667 0.73333333 0.8 1. 0.5 0.56666667 0.5 ] mean value: 0.6466666666666667 key: train_roc_auc value: [0.5 1. 0.86538462 0.83504274 1. 1. 1. 0.83504274 0.98076923 0.5 ] mean value: 0.8516239316239316 key: test_jcc value: [0. 0.5 0.33333333 0.2 0.5 0.6 1. 0. 0.25 0. ] mean value: 0.3383333333333333 key: train_jcc value: [0. 1. 0.73076923 0.66666667 1. 1. 1. 0.66666667 0.96153846 0. ] mean value: 0.7025641025641025 key: TN value: 43 mean value: 43.0 key: FP value: 16 mean value: 16.0 key: FN value: 7 mean value: 7.0 key: TP value: 13 mean value: 13.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.39 Accuracy on Blind test: 0.72 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01347423 0.01884365 0.00855184 0.00828695 0.00806451 0.00827241 0.00818229 0.00826859 0.00821042 0.00816202] mean value: 0.009831690788269043 key: score_time value: [0.01358652 0.00921679 0.00873518 0.00868773 0.00849938 0.00838518 0.00836921 0.00838017 0.00841665 0.00839853] mean value: 0.009067535400390625 key: test_mcc value: [-0.1490712 0. -0.1490712 -0.46666667 0. 0.6 0.06666667 -0.06666667 -0.46666667 0.25819889] mean value: -0.03732768405861442 key: train_mcc value: [0.53350008 0.46369578 0.44297451 0.61021596 0.39343135 0.5954372 0.40122444 0.55355298 0.55355298 0.54074074] mean value: 0.5088326024907828 key: test_fscore value: [0.44444444 0.54545455 0.44444444 0.25 0.54545455 0.75 0.5 0.33333333 0.25 0.5 ] mean value: 0.45631313131313134 key: train_fscore value: [0.71428571 0.67567568 0.65822785 0.75757576 0.64102564 0.74285714 0.64864865 0.72463768 0.72463768 0.72222222] mean value: 0.7009794012710908 key: test_precision value: [0.33333333 0.375 0.33333333 0.2 0.375 0.6 0.4 0.33333333 0.2 0.33333333] mean value: 0.3483333333333334 key: train_precision value: [0.66666667 0.52083333 0.49056604 0.625 0.48076923 0.59090909 0.5 0.58139535 0.58139535 0.57777778] mean value: 0.5615312834866366 key: test_recall value: [0.66666667 1. 0.66666667 0.33333333 1. 1. 0.66666667 0.33333333 0.33333333 1. ] mean value: 0.7 key: train_recall value: [0.76923077 0.96153846 1. 0.96153846 0.96153846 1. 0.92307692 0.96153846 0.96153846 0.96296296] mean value: 0.9462962962962964 key: test_accuracy value: [0.375 0.375 0.375 0.25 0.375 0.75 0.5 0.5 0.25 0.42857143] mean value: 0.41785714285714287 key: train_accuracy value: [0.77464789 0.66197183 0.61971831 0.77464789 0.6056338 0.74647887 0.63380282 0.73239437 0.73239437 0.72222222] mean value: 0.7003912363067293 key: test_roc_auc value: [0.43333333 0.5 0.43333333 0.26666667 0.5 0.8 0.53333333 0.46666667 0.26666667 0.6 ] mean value: 0.47999999999999987 key: train_roc_auc value: [0.77350427 0.72521368 0.7 0.81410256 0.68076923 0.8 0.69487179 0.78076923 0.78076923 0.77037037] mean value: 0.7520370370370371 key: test_jcc value: [0.28571429 0.375 0.28571429 0.14285714 0.375 0.6 0.33333333 0.2 0.14285714 0.33333333] mean value: 0.30738095238095237 key: train_jcc value: [0.55555556 0.51020408 0.49056604 0.6097561 0.47169811 0.59090909 0.48 0.56818182 0.56818182 0.56521739] mean value: 0.5410270004269655 key: TN value: 13 mean value: 13.0 key: FP value: 9 mean value: 9.0 key: FN value: 37 mean value: 37.0 key: TP value: 20 mean value: 20.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.27 Accuracy on Blind test: 0.52 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.0091784 0.00914526 0.00893593 0.00925088 0.00869727 0.00930667 0.00948238 0.00864649 0.00869966 0.00828242] mean value: 0.008962535858154297 key: score_time value: [0.00917268 0.00923753 0.00889874 0.00905013 0.00859499 0.00902128 0.00948811 0.00878263 0.00849462 0.00845385] mean value: 0.008919453620910645 key: test_mcc value: [-0.29277002 -0.06666667 0.48795004 0. 0.46666667 -0.06666667 -0.29277002 -0.29277002 -0.29277002 -0.25819889] mean value: -0.060799560747780104 key: train_mcc value: [0.40170738 0.51530373 0.43729399 0.35928426 0.40170738 0.50503962 0.48250404 0.50503962 0.39606306 0.4233902 ] mean value: 0.44273332643919316 key: test_fscore value: [0. 0.33333333 0.5 0. 0.66666667 0.33333333 0. 0. 0. 0. ] mean value: 0.18333333333333332 key: train_fscore value: [0.48648649 0.57894737 0.52631579 0.47368421 0.48648649 0.6 0.54054054 0.6 0.51282051 0.51282051] mean value: 0.5318101907575592 key: test_precision value: [0. 0.33333333 1. 0. 0.66666667 0.33333333 0. 0. 0. 0. ] mean value: 0.2333333333333333 key: train_precision value: [0.81818182 0.91666667 0.83333333 0.75 0.81818182 0.85714286 0.90909091 0.85714286 0.76923077 0.83333333] mean value: 0.8362304362304362 key: test_recall value: [0. 0.33333333 0.33333333 0. 0.66666667 0.33333333 0. 0. 0. 0. ] mean value: 0.16666666666666666 key: train_recall value: [0.34615385 0.42307692 0.38461538 0.34615385 0.34615385 0.46153846 0.38461538 0.46153846 0.38461538 0.37037037] mean value: 0.3908831908831909 key: test_accuracy value: [0.5 0.5 0.75 0.625 0.75 0.5 0.5 0.5 0.5 0.57142857] mean value: 0.5696428571428571 key: train_accuracy value: [0.73239437 0.77464789 0.74647887 0.71830986 0.73239437 0.77464789 0.76056338 0.77464789 0.73239437 0.73611111] mean value: 0.7482589984350548 key: test_roc_auc value: [0.4 0.46666667 0.66666667 0.5 0.73333333 0.46666667 0.4 0.4 0.4 0.4 ] mean value: 0.4833333333333334 key: train_roc_auc value: [0.6508547 0.70042735 0.67008547 0.63974359 0.6508547 0.70854701 0.68119658 0.70854701 0.65897436 0.66296296] mean value: 0.6732193732193733 key: test_jcc value: [0. 0.2 0.33333333 0. 0.5 0.2 0. 0. 0. 0. ] mean value: 0.12333333333333334 key: train_jcc value: [0.32142857 0.40740741 0.35714286 0.31034483 0.32142857 0.42857143 0.37037037 0.42857143 0.34482759 0.34482759] mean value: 0.36349206349206353 key: TN value: 40 mean value: 40.0 key: FP value: 24 mean value: 24.0 key: FN value: 10 mean value: 10.0 key: TP value: 5 mean value: 5.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.16 Accuracy on Blind test: 0.55 Running classifier: 5 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.00837946 0.01102352 0.00812745 0.00867653 0.00784159 0.00877905 0.00860858 0.00786567 0.0082624 0.00899577] mean value: 0.008656001091003418 key: score_time value: [0.04542685 0.03425503 0.00936675 0.00973725 0.00988531 0.00930572 0.01529074 0.01369047 0.01387525 0.01388526] mean value: 0.017471861839294434 key: test_mcc value: [ 0.48795004 -0.06666667 0.25819889 0.1490712 0.46666667 0.25819889 0.48795004 0. -0.06666667 1. ] mean value: 0.2974702384276175 key: train_mcc value: [0.532629 0.49965897 0.332975 0.49787306 0.46412056 0.4660252 0.46504888 0.43897987 0.53764379 0.48034053] mean value: 0.4715294855036312 key: test_fscore value: [0.5 0.33333333 0.57142857 0.4 0.66666667 0.57142857 0.5 0. 0.33333333 1. ] mean value: 0.4876190476190477 key: train_fscore value: [0.65116279 0.61904762 0.53333333 0.63636364 0.60465116 0.58536585 0.62222222 0.625 0.63414634 0.62222222] mean value: 0.6133515181799357 key: test_precision value: [1. 0.33333333 0.5 0.5 0.66666667 0.5 1. 0. 0.33333333 1. ] mean value: 0.5833333333333333 key: train_precision value: [0.82352941 0.8125 0.63157895 0.77777778 0.76470588 0.8 0.73684211 0.68181818 0.86666667 0.77777778] mean value: 0.7673196750789629 key: test_recall value: [0.33333333 0.33333333 0.66666667 0.33333333 0.66666667 0.66666667 0.33333333 0. 0.33333333 1. ] mean value: 0.4666666666666666 key: train_recall value: [0.53846154 0.5 0.46153846 0.53846154 0.5 0.46153846 0.53846154 0.57692308 0.5 0.51851852] mean value: 0.5133903133903133 key: test_accuracy value: [0.75 0.5 0.625 0.625 0.75 0.625 0.75 0.625 0.5 1. ] mean value: 0.675 key: train_accuracy value: [0.78873239 0.77464789 0.70422535 0.77464789 0.76056338 0.76056338 0.76056338 0.74647887 0.78873239 0.76388889] mean value: 0.7623043818466353 key: test_roc_auc value: [0.66666667 0.46666667 0.63333333 0.56666667 0.73333333 0.63333333 0.66666667 0.5 0.46666667 1. ] mean value: 0.6333333333333333 key: train_roc_auc value: [0.73589744 0.71666667 0.65299145 0.72478632 0.70555556 0.6974359 0.71367521 0.71068376 0.72777778 0.71481481] mean value: 0.7100284900284901 key: test_jcc value: [0.33333333 0.2 0.4 0.25 0.5 0.4 0.33333333 0. 0.2 1. ] mean value: 0.3616666666666667 key: train_jcc value: [0.48275862 0.44827586 0.36363636 0.46666667 0.43333333 0.4137931 0.4516129 0.45454545 0.46428571 0.4516129 ] mean value: 0.4430520925126042 key: TN value: 40 mean value: 40.0 key: FP value: 16 mean value: 16.0 key: FN value: 10 mean value: 10.0 key: TP value: 13 mean value: 13.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.16 Accuracy on Blind test: 0.55 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.00934434 0.00849128 0.00862622 0.00854397 0.00879931 0.00910807 0.00843096 0.00899863 0.00908327 0.00870323] mean value: 0.008812928199768066 key: score_time value: [0.00875854 0.00884771 0.00850749 0.00841236 0.00861263 0.00835824 0.0086937 0.00896144 0.00847411 0.00863171] mean value: 0.00862579345703125 key: test_mcc value: [ 0. -0.4472136 0. 0. 0. 0.48795004 0. 0. 0. 0. ] mean value: 0.004073644097430868 key: train_mcc value: [0.46880723 0.53266562 0.46880723 0.53266562 0.43508951 0.50123916 0.50123916 0.56330071 0.46880723 0.51847585] mean value: 0.49910973205557047 key: test_fscore value: [0. 0. 0. 0. 0. 0.5 0. 0. 0. 0. ] mean value: 0.05 key: train_fscore value: [0.47058824 0.55555556 0.47058824 0.55555556 0.42424242 0.51428571 0.51428571 0.59459459 0.47058824 0.54054054] mean value: 0.5110824804942451 key: test_precision value: [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.] mean value: 0.1 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0. 0. 0. 0.33333333 0. 0. 0. 0. ] mean value: 0.03333333333333333 key: train_recall value: [0.30769231 0.38461538 0.30769231 0.38461538 0.26923077 0.34615385 0.34615385 0.42307692 0.30769231 0.37037037] mean value: 0.3447293447293447 key: test_accuracy value: [0.625 0.375 0.625 0.625 0.625 0.75 0.625 0.625 0.625 0.71428571] mean value: 0.6214285714285714 key: train_accuracy value: [0.74647887 0.77464789 0.74647887 0.77464789 0.73239437 0.76056338 0.76056338 0.78873239 0.74647887 0.76388889] mean value: 0.7594874804381847 key: test_roc_auc value: [0.5 0.3 0.5 0.5 0.5 0.66666667 0.5 0.5 0.5 0.5 ] mean value: 0.4966666666666667 key: train_roc_auc value: [0.65384615 0.69230769 0.65384615 0.69230769 0.63461538 0.67307692 0.67307692 0.71153846 0.65384615 0.68518519] mean value: 0.6723646723646725 key: test_jcc value: [0. 0. 0. 0. 0. 0.33333333 0. 0. 0. 0. ] mean value: 0.03333333333333333 key: train_jcc value: [0.30769231 0.38461538 0.30769231 0.38461538 0.26923077 0.34615385 0.34615385 0.42307692 0.30769231 0.37037037] mean value: 0.3447293447293447 key: TN value: 48 mean value: 48.0 key: FP value: 28 mean value: 28.0 key: FN value: 2 mean value: 2.0 key: TP value: 1 mean value: 1.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.22 Accuracy on Blind test: 0.68 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.4739027 0.3656857 0.50512409 0.53201818 0.40765285 0.37933254 0.71146369 1.25982904 0.67421913 0.39366579] mean value: 0.5702893733978271 key: score_time value: [0.01205778 0.01223612 0.01199913 0.01212883 0.01202846 0.01200676 0.012532 0.01227117 0.01231146 0.01202655] mean value: 0.01215982437133789 key: test_mcc value: [ 0.74535599 0.46666667 0.74535599 0.06666667 0.46666667 0.6 0.1490712 -0.29277002 -0.06666667 0.3 ] mean value: 0.3180346494948619 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.66666667 0.8 0.5 0.66666667 0.75 0.4 0. 0.33333333 0.5 ] mean value: 0.5416666666666666 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.66666667 1. 0.4 0.66666667 0.6 0.5 0. 0.33333333 0.5 ] mean value: 0.5666666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.66666667 0.66666667 0.66666667 0.66666667 1. 0.33333333 0. 0.33333333 0.5 ] mean value: 0.5499999999999999 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.875 0.75 0.875 0.5 0.75 0.75 0.625 0.5 0.5 0.71428571] mean value: 0.6839285714285714 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.83333333 0.73333333 0.83333333 0.53333333 0.73333333 0.8 0.56666667 0.4 0.46666667 0.65 ] mean value: 0.655 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.5 0.66666667 0.33333333 0.5 0.6 0.25 0. 0.2 0.33333333] mean value: 0.40499999999999997 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 38 mean value: 38.0 key: FP value: 13 mean value: 13.0 key: FN value: 12 mean value: 12.0 key: TP value: 16 mean value: 16.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.16 Accuracy on Blind test: 0.62 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01303053 0.01276302 0.00972843 0.00934529 0.00922871 0.00918818 0.00910521 0.0092113 0.00886583 0.00934553] mean value: 0.009981203079223632 key: score_time value: [0.01174879 0.01063251 0.00858688 0.00828457 0.00867963 0.00826287 0.00824618 0.00817752 0.00813031 0.00817704] mean value: 0.008892631530761719 key: test_mcc value: [0.74535599 0.74535599 0.77459667 0.46666667 1. 0.6 0.46666667 1. 0.74535599 0.54772256] mean value: 0.7091720537579772 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.8 0.85714286 0.66666667 1. 0.75 0.66666667 1. 0.8 0.66666667] mean value: 0.8007142857142856 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.75 0.66666667 1. 0.6 0.66666667 1. 1. 0.5 ] mean value: 0.8183333333333334 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.66666667 1. 0.66666667 1. 1. 0.66666667 1. 0.66666667 1. ] mean value: 0.8333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.875 0.875 0.875 0.75 1. 0.75 0.75 1. 0.875 0.71428571] mean value: 0.8464285714285713 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.83333333 0.83333333 0.9 0.73333333 1. 0.8 0.73333333 1. 0.83333333 0.8 ] mean value: 0.8466666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.66666667 0.75 0.5 1. 0.6 0.5 1. 0.66666667 0.5 ] mean value: 0.685 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 43 mean value: 43.0 key: FP value: 5 mean value: 5.0 key: FN value: 7 mean value: 7.0 key: TP value: 24 mean value: 24.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.89 Accuracy on Blind test: 0.95 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.0797112 0.08011031 0.0798316 0.07908225 0.07965994 0.07991409 0.08132887 0.07901454 0.07934308 0.07978773] mean value: 0.07977836132049561 key: score_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0.01661205 0.01663494 0.01658773 0.01665282 0.01672363 0.01666379 0.01657057 0.01656389 0.01663399 0.0166142 ] mean value: 0.01662576198577881 key: test_mcc value: [ 0.48795004 -0.06666667 0.74535599 -0.06666667 0.1490712 0.25819889 0.1490712 -0.29277002 0.1490712 0.73029674] mean value: 0.22429119023436436 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.33333333 0.8 0.33333333 0.4 0.57142857 0.4 0. 0.4 0.8 ] mean value: 0.4538095238095238 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.33333333 1. 0.33333333 0.5 0.5 0.5 0. 0.5 0.66666667] mean value: 0.5333333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.33333333 0.33333333 0.66666667 0.33333333 0.33333333 0.66666667 0.33333333 0. 0.33333333 1. ] mean value: 0.4333333333333334 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.75 0.5 0.875 0.5 0.625 0.625 0.625 0.5 0.625 0.85714286] mean value: 0.6482142857142856 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66666667 0.46666667 0.83333333 0.46666667 0.56666667 0.63333333 0.56666667 0.4 0.56666667 0.9 ] mean value: 0.6066666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0.2 0.66666667 0.2 0.25 0.4 0.25 0. 0.25 0.66666667] mean value: 0.32166666666666666 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 39 mean value: 39.0 key: FP value: 17 mean value: 17.0 key: FN value: 11 mean value: 11.0 key: TP value: 12 mean value: 12.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.28 Accuracy on Blind test: 0.7 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00881672 0.00906944 0.00841808 0.00802994 0.00884914 0.00797772 0.0080471 0.00805855 0.00790024 0.00817227] mean value: 0.008333921432495117 key: score_time value: [0.00838375 0.0091083 0.0088253 0.00814772 0.00830793 0.008255 0.00831819 0.00809455 0.00816226 0.00925803] mean value: 0.008486104011535645 key: test_mcc value: [ 0. -0.06666667 0.74535599 -0.6 0.48795004 0.4472136 0.1490712 -0.06666667 0.25819889 -0.09128709] mean value: 0.12631692864704402 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.33333333 0.8 0. 0.5 0.66666667 0.4 0.33333333 0.57142857 0.33333333] mean value: 0.39380952380952383 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0.33333333 1. 0. 1. 0.5 0.5 0.33333333 0.5 0.25 ] mean value: 0.4416666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.33333333 0.66666667 0. 0.33333333 1. 0.33333333 0.33333333 0.66666667 0.5 ] mean value: 0.41666666666666663 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.625 0.5 0.875 0.25 0.75 0.625 0.625 0.5 0.625 0.42857143] mean value: 0.5803571428571429 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.46666667 0.83333333 0.2 0.66666667 0.7 0.56666667 0.46666667 0.63333333 0.45 ] mean value: 0.5483333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0.2 0.66666667 0. 0.33333333 0.5 0.25 0.2 0.4 0.2 ] mean value: 0.275 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 34 mean value: 34.0 key: FP value: 17 mean value: 17.0 key: FN value: 16 mean value: 16.0 key: TP value: 12 mean value: 12.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.08 Accuracy on Blind test: 0.57 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [0.98950601 0.99312663 0.9990921 1.0047307 0.99263144 0.98464394 0.99300337 0.98910856 0.98675776 0.99708605] mean value: 0.9929686546325683 key: score_time value: [0.08685112 0.08616352 0.0863297 0.09210467 0.08822775 0.08625507 0.08599234 0.08730912 0.0862937 0.08622384] mean value: 0.08717508316040039 key: test_mcc value: [0.74535599 0.46666667 0.74535599 0.25819889 0.48795004 0.74535599 0.74535599 0. 0.1490712 0.73029674] mean value: 0.5073607504728022 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.66666667 0.8 0.57142857 0.5 0.8 0.8 0. 0.4 0.8 ] mean value: 0.6138095238095238 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.66666667 1. 0.5 1. 1. 1. 0. 0.5 0.66666667] mean value: 0.7333333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.66666667 0.66666667 0.66666667 0.33333333 0.66666667 0.66666667 0. 0.33333333 1. ] mean value: 0.5666666666666667 key: train_recall value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.875 0.75 0.875 0.625 0.75 0.875 0.875 0.625 0.625 0.85714286] mean value: 0.7732142857142856 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.83333333 0.73333333 0.83333333 0.63333333 0.66666667 0.83333333 0.83333333 0.5 0.56666667 0.9 ] mean value: 0.7333333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.5 0.66666667 0.4 0.33333333 0.66666667 0.66666667 0. 0.25 0.66666667] mean value: 0.48166666666666674 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 45 mean value: 45.0 key: FP value: 13 mean value: 13.0 key: FN value: 5 mean value: 5.0 key: TP value: 16 mean value: 16.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.57 Accuracy on Blind test: 0.8 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.82440186 0.80704331 0.8464756 0.91650128 0.80716181 0.7972815 0.81476235 0.90086365 0.79608941 0.80462122] mean value: 0.8315201997756958 key: score_time value: [0.19199228 0.18694234 0.19604993 0.18045735 0.1642139 0.16165352 0.17684007 0.18403292 0.14751863 0.19382191] mean value: 0.1783522844314575 key: test_mcc value: [0.74535599 0.48795004 0.46666667 0.48795004 0.48795004 0.74535599 0.74535599 0. 0. 0.73029674] mean value: 0.48968814969294777 key: train_mcc value: [0.88152145 0.91067388 0.94010481 0.94010481 0.94010481 0.96986363 0.91067388 0.90865445 0.94010481 0.94155447] mean value: 0.9283361007134785 key: test_fscore value: [0.8 0.5 0.66666667 0.5 0.5 0.8 0.8 0. 0. 0.8 ] mean value: 0.5366666666666666 key: train_fscore value: [0.91666667 0.93877551 0.96 0.96 0.96 0.98039216 0.93877551 0.94117647 0.96 0.96153846] mean value: 0.9517324776064273 key: test_precision value: [1. 1. 0.66666667 1. 1. 1. 1. 0. 0. 0.66666667] mean value: 0.7333333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 0.96 1. 1. ] mean value: 0.9960000000000001 key: test_recall value: [0.66666667 0.33333333 0.66666667 0.33333333 0.33333333 0.66666667 0.66666667 0. 0. 1. ] mean value: 0.4666666666666666 key: train_recall value: [0.84615385 0.88461538 0.92307692 0.92307692 0.92307692 0.96153846 0.88461538 0.92307692 0.92307692 0.92592593] mean value: 0.9118233618233619 key: test_accuracy value: [0.875 0.75 0.75 0.75 0.75 0.875 0.875 0.625 0.625 0.85714286] mean value: 0.7732142857142856 key: train_accuracy value: [0.94366197 0.95774648 0.97183099 0.97183099 0.97183099 0.98591549 0.95774648 0.95774648 0.97183099 0.97222222] mean value: 0.9662363067292643 key: test_roc_auc value: [0.83333333 0.66666667 0.73333333 0.66666667 0.66666667 0.83333333 0.83333333 0.5 0.5 0.9 ] mean value: 0.7133333333333333 key: train_roc_auc value: [0.92307692 0.94230769 0.96153846 0.96153846 0.96153846 0.98076923 0.94230769 0.95042735 0.96153846 0.96296296] mean value: 0.9548005698005699 key: test_jcc value: [0.66666667 0.33333333 0.5 0.33333333 0.33333333 0.66666667 0.66666667 0. 0. 0.66666667] mean value: 0.4166666666666667 key: train_jcc value: [0.84615385 0.88461538 0.92307692 0.92307692 0.92307692 0.96153846 0.88461538 0.88888889 0.92307692 0.92592593] mean value: 0.9084045584045584 key: TN value: 48 mean value: 48.0 key: FP value: 16 mean value: 16.0 key: FN value: 2 mean value: 2.0 key: TP value: 13 mean value: 13.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.63 Accuracy on Blind test: 0.82 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.25267649 0.03277755 0.03268933 0.0326879 0.03371954 0.0320909 0.03316617 0.03263474 0.03360939 0.03158092] mean value: 0.054763293266296385 key: score_time value: [0.01154733 0.01026154 0.01063204 0.01008058 0.010144 0.00997472 0.01005268 0.01010799 0.01003933 0.01033354] mean value: 0.010317373275756835 key: test_mcc value: [1. 0.74535599 0.46666667 1. 1. 0.6 1. 1. 0.74535599 0.73029674] mean value: 0.8287675395006747 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) mean value: 1.0 key: test_fscore value: [1. 0.8 0.66666667 1. 1. 0.75 1. 1. 0.8 0.8 ] mean value: 0.8816666666666668 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.66666667 1. 1. 0.6 1. 1. 1. 0.66666667] mean value: 0.8933333333333332 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.66666667 0.66666667 1. 1. 1. 1. 1. 0.66666667 1. ] mean value: 0.9 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.875 0.75 1. 1. 0.75 1. 1. 0.875 0.85714286] mean value: 0.9107142857142858 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.83333333 0.73333333 1. 1. 0.8 1. 1. 0.83333333 0.9 ] mean value: 0.9099999999999999 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.66666667 0.5 1. 1. 0.6 1. 1. 0.66666667 0.66666667] mean value: 0.8099999999999999 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 46 mean value: 46.0 key: FP value: 3 mean value: 3.0 key: FN value: 4 mean value: 4.0 key: TP value: 26 mean value: 26.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.95 Accuracy on Blind test: 0.98 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02152681 0.03794527 0.03800631 0.03840089 0.03812551 0.03813028 0.038131 0.03790116 0.04037714 0.04405189] mean value: 0.03725962638854981 key: score_time value: [0.02359128 0.01147771 0.02012229 0.02056122 0.01671767 0.02125883 0.02173471 0.02235389 0.02048898 0.01903772] mean value: 0.01973443031311035 key: test_mcc value: [ 0.46666667 0.46666667 1. -0.46666667 0.46666667 0.46666667 0.74535599 0.25819889 0.06666667 -0.09128709] mean value: 0.33789344559962303 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.66666667 1. 0.25 0.66666667 0.66666667 0.8 0.57142857 0.5 0.33333333] mean value: 0.6121428571428571 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.66666667 1. 0.2 0.66666667 0.66666667 1. 0.5 0.4 0.25 ] mean value: 0.6016666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.66666667 1. 0.33333333 0.66666667 0.66666667 0.66666667 0.66666667 0.66666667 0.5 ] mean value: 0.65 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.75 0.75 1. 0.25 0.75 0.75 0.875 0.625 0.5 0.42857143] mean value: 0.6678571428571429 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.73333333 0.73333333 1. 0.26666667 0.73333333 0.73333333 0.83333333 0.63333333 0.53333333 0.45 ] mean value: 0.6649999999999999 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.5 1. 0.14285714 0.5 0.5 0.66666667 0.4 0.33333333 0.2 ] mean value: 0.4742857142857142 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 34 mean value: 34.0 key: FP value: 10 mean value: 10.0 key: FN value: 16 mean value: 16.0 key: TP value: 19 mean value: 19.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.04 Accuracy on Blind test: 0.52 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01962996 0.00853753 0.00832033 0.00821114 0.00803685 0.00808215 0.00803947 0.00806713 0.00811481 0.00799322] mean value: 0.00930325984954834 key: score_time value: [0.00881076 0.00864339 0.00856471 0.00838065 0.00816441 0.00830078 0.00829577 0.00821209 0.00824976 0.00826263] mean value: 0.008388495445251465 key: test_mcc value: [-0.06666667 -0.06666667 0.1490712 -0.06666667 0.48795004 0.48795004 0. -0.29277002 0.1490712 -0.4 ] mean value: 0.038127244806394525 key: train_mcc value: [0.35808137 0.39561212 0.39440661 0.39440661 0.43729399 0.36890287 0.39561212 0.39901194 0.39901194 0.41403934] mean value: 0.3956378889330924 key: test_fscore value: [0.33333333 0.33333333 0.4 0.33333333 0.5 0.5 0. 0. 0.4 0. ] mean value: 0.27999999999999997 key: train_fscore value: [0.5 0.55813953 0.53658537 0.53658537 0.52631579 0.56521739 0.55813953 0.57777778 0.57777778 0.55813953] mean value: 0.5494678072692067 key: test_precision value: [0.33333333 0.33333333 0.5 0.33333333 1. 1. 0. 0. 0.5 0. ] mean value: 0.4 key: train_precision value: [0.71428571 0.70588235 0.73333333 0.73333333 0.83333333 0.65 0.70588235 0.68421053 0.68421053 0.75 ] mean value: 0.7194471472799646 key: test_recall value: [0.33333333 0.33333333 0.33333333 0.33333333 0.33333333 0.33333333 0. 0. 0.33333333 0. ] mean value: 0.23333333333333334 key: train_recall value: [0.38461538 0.46153846 0.42307692 0.42307692 0.38461538 0.5 0.46153846 0.5 0.5 0.44444444] mean value: 0.4482905982905983 key: test_accuracy value: [0.5 0.5 0.625 0.5 0.75 0.75 0.625 0.5 0.625 0.42857143] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) mean value: 0.5803571428571429 key: train_accuracy value: [0.71830986 0.73239437 0.73239437 0.73239437 0.74647887 0.71830986 0.73239437 0.73239437 0.73239437 0.73611111] mean value: 0.7313575899843506 key: test_roc_auc value: [0.46666667 0.46666667 0.56666667 0.46666667 0.66666667 0.66666667 0.5 0.4 0.56666667 0.3 ] mean value: 0.5066666666666666 key: train_roc_auc value: [0.64786325 0.67521368 0.66709402 0.66709402 0.67008547 0.67222222 0.67521368 0.68333333 0.68333333 0.67777778] mean value: 0.6719230769230771 key: test_jcc value: [0.2 0.2 0.25 0.2 0.33333333 0.33333333 0. 0. 0.25 0. ] mean value: 0.17666666666666667 key: train_jcc value: [0.33333333 0.38709677 0.36666667 0.36666667 0.35714286 0.39393939 0.38709677 0.40625 0.40625 0.38709677] mean value: 0.3791539240329563 key: TN value: 39 mean value: 39.0 key: FP value: 22 mean value: 22.0 key: FN value: 11 mean value: 11.0 key: TP value: 7 mean value: 7.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.06 Accuracy on Blind test: 0.57 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01078463 0.01264167 0.01373768 0.01230025 0.01327467 0.01310778 0.01337218 0.01272774 0.01252699 0.01253462] mean value: 0.012700819969177246 key: score_time value: [0.00824046 0.01123905 0.01123929 0.01127291 0.01127601 0.0113132 0.01129079 0.01124072 0.01128697 0.01134467] mean value: 0.010974407196044922 key: test_mcc value: [ 0.74535599 0.74535599 0.48795004 -0.06666667 0.46666667 0.6 1. 0.1490712 -0.29277002 0.73029674] mean value: 0.45652599414297745 key: train_mcc value: [0.88152145 0.88152145 0.91067388 0.79523358 1. 0.9703421 1. 0.94196687 0.88152145 0.89081333] mean value: 0.9153594107114607 key: test_fscore value: [0.8 0.8 0.5 0.33333333 0.66666667 0.75 1. 0.4 0. 0.8 ] mean value: 0.605 key: train_fscore value: [0.91666667 0.91666667 0.93877551 0.84444444 1. 0.98113208 1. 0.96296296 0.91666667 0.93103448] mean value: 0.9408349475841808 key: test_precision value: [1. 1. 1. 0.33333333 0.66666667 0.6 1. 0.5 0. 0.66666667] mean value: 0.6766666666666666 key: train_precision value: [1. 1. 1. 1. 1. 0.96296296 1. 0.92857143 1. 0.87096774] mean value: 0.9762502133469877 key: test_recall value: [0.66666667 0.66666667 0.33333333 0.33333333 0.66666667 1. 1. 0.33333333 0. 1. ] mean value: 0.6 key: train_recall value: [0.84615385 0.84615385 0.88461538 0.73076923 1. 1. 1. 1. 0.84615385 1. ] mean value: 0.9153846153846154 key: test_accuracy value: [0.875 0.875 0.75 0.5 0.75 0.75 1. 0.625 0.5 0.85714286] mean value: 0.7482142857142857 key: train_accuracy value: [0.94366197 0.94366197 0.95774648 0.90140845 1. 0.98591549 1. 0.97183099 0.94366197 0.94444444] mean value: 0.9592331768388107 key: test_roc_auc value: [0.83333333 0.83333333 0.66666667 0.46666667 0.73333333 0.8 1. 0.56666667 0.4 0.9 ] mean value: 0.72 key: train_roc_auc value: [0.92307692 0.92307692 0.94230769 0.86538462 1. 0.98888889 1. 0.97777778 0.92307692 0.95555556] mean value: 0.94991452991453 key: test_jcc value: [0.66666667 0.66666667 0.33333333 0.2 0.5 0.6 1. 0.25 0. 0.66666667] mean value: 0.4883333333333334 key: train_jcc value: [0.84615385 0.84615385 0.88461538 0.73076923 1. 0.96296296 1. 0.92857143 0.84615385 0.87096774] mean value: 0.8916348287316029 key: TN value: 42 mean value: 42.0 key: FP value: 12 mean value: 12.0 key: FN value: 8 mean value: 8.0 key: TP value: 17 mean value: 17.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.36 Accuracy on Blind test: 0.72 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.00896978 0.01184177 0.0120883 0.01203012 0.01236367 0.01237869 0.01220155 0.01421261 0.01220298 0.0120194 ] mean value: 0.012030887603759765 key: score_time value: [0.00857139 0.01130891 0.01136184 0.01203728 0.011446 0.01147342 0.01199031 0.01191354 0.01148462 0.01145744] mean value: 0.011304473876953125 key: test_mcc value: [0.46666667 0.48795004 0.48795004 0.4472136 0.46666667 0.25819889 0. 0. 0.1490712 0.73029674] mean value: 0.3494013833369193 key: train_mcc value: [0.81567142 0.36210341 0.93931624 0.9703421 1. 1. 0.68088097 0.65199786 0.88152145 0.97058178] mean value: 0.8272415231599487 key: test_fscore value: [0.66666667 0.5 0.5 0.66666667 0.66666667 0.57142857 0. 0. 0.4 0.8 ] mean value: 0.47714285714285715 key: train_fscore value: [0.88135593 0.32258065 0.96153846 0.98113208 1. 1. 0.73170732 0.7 0.91666667 0.98113208] mean value: 0.8476113173586375 key: test_precision value: [0.66666667 1. 1. 0.5 0.66666667 0.5 0. 0. 0.5 0.66666667] mean value: 0.55 key: train_precision value: [0.78787879 1. 0.96153846 0.96296296 1. 1. 1. 1. 1. 1. ] mean value: 0.9712380212380213 key: test_recall value: [0.66666667 0.33333333 0.33333333 1. 0.66666667 0.66666667 0. 0. 0.33333333 1. ] mean value: 0.5 key: train_recall value: [1. 0.19230769 0.96153846 1. 1. 1. 0.57692308 0.53846154 0.84615385 0.96296296] mean value: 0.8078347578347579 key: test_accuracy value: [0.75 0.75 0.75 0.625 0.75 0.625 0.625 0.625 0.625 0.85714286] mean value: 0.6982142857142857 key: train_accuracy value: [0.90140845 0.70422535 0.97183099 0.98591549 1. 1. 0.84507042 0.83098592 0.94366197 0.98611111] mean value: 0.9169209702660407 key: test_roc_auc value: [0.73333333 0.66666667 0.66666667 0.7 0.73333333 0.63333333 0.5 0.5 0.56666667 0.9 ] mean value: 0.6599999999999999 key: train_roc_auc value: [0.92222222 0.59615385 0.96965812 0.98888889 1. 1. 0.78846154 0.76923077 0.92307692 0.98148148] mean value: 0.893917378917379 key: test_jcc value: [0.5 0.33333333 0.33333333 0.5 0.5 0.4 0. 0. 0.25 0.66666667] mean value: 0.3483333333333333 key: train_jcc value: [0.78787879 0.19230769 0.92592593 0.96296296 1. 1. 0.57692308 0.53846154 0.84615385 0.96296296] mean value: 0.7793576793576793 key: TN value: 41 mean value: 41.0 key: FP value: 15 mean value: 15.0 key: FN value: 9 mean value: 9.0 key: TP value: 14 mean value: 14.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.06 Accuracy on Blind test: 0.5 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.07852364 0.07593727 0.07648396 0.07617307 0.07601118 0.07563257 0.07694602 0.07653785 0.07624555 0.07758784] mean value: 0.07660789489746093 key: score_time value: [0.01418972 0.0142076 0.014153 0.01459336 0.01418948 0.01423931 0.01444244 0.01423192 0.01443076 0.0149498 ] mean value: 0.014362740516662597 key: test_mcc value: [0.74535599 0.74535599 0.25819889 1. 1. 0.6 1. 1. 0.74535599 0.54772256] mean value: 0.7641989424752118 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.8 0.57142857 1. 1. 0.75 1. 1. 0.8 0.66666667] mean value: 0.8388095238095238 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.5 1. 1. 0.6 1. 1. 1. 0.5] mean value: 0.86 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.66666667 0.66666667 1. 1. 1. 1. 1. 0.66666667 1. ] mean value: 0.8666666666666668 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.875 0.875 0.625 1. 1. 0.75 1. 1. 0.875 0.71428571] mean value: 0.8714285714285713 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.83333333 0.83333333 0.63333333 1. 1. 0.8 1. 1. 0.83333333 0.8 ] mean value: 0.8733333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.66666667 0.4 1. 1. 0.6 1. 1. 0.66666667 0.5 ] mean value: 0.7500000000000001 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 44 mean value: 44.0 key: FP value: 4 mean value: 4.0 key: FN value: 6 mean value: 6.0 key: TP value: 25 mean value: 25.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.72 Accuracy on Blind test: 0.88 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.02951646 0.03389382 0.02654004 0.02991986 0.03635168 0.03002954 0.03156233 0.04396296 0.03435445 0.02904296] mean value: 0.032517409324645995 key: score_time value: [0.02055669 0.02374935 0.01841545 0.01818538 0.01966786 0.01749253 0.02351284 0.02446222 0.01901722 0.01872277] mean value: 0.0203782320022583 key: test_mcc value: [0.74535599 0.74535599 0.74535599 1. 1. 0.6 0.74535599 0.74535599 0.74535599 0.73029674] mean value: 0.7802432698339802 key: train_mcc value: [0.96986363 0.96986363 1. 1. 0.94010481 1. 1. 1. 0.96986363 1. ] mean value: 0.9849695685748777 key: test_fscore value: [0.8 0.8 0.8 1. 1. 0.75 0.8 0.8 0.8 0.8 ] mean value: 0.835 key: train_fscore value: [0.98039216 0.98039216 1. 1. 0.96 1. 1. 1. 0.98039216 1. ] mean value: 0.9901176470588234 key: test_precision value: [1. 1. 1. 1. 1. 0.6 1. 1. 1. 0.66666667] mean value: 0.9266666666666665 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.66666667 0.66666667 1. 1. 1. 0.66666667 0.66666667 0.66666667 1. ] mean value: 0.8 key: train_recall value: [0.96153846 0.96153846 1. 1. 0.92307692 1. 1. 1. 0.96153846 1. ] mean value: 0.9807692307692308 key: test_accuracy value: [0.875 0.875 0.875 1. 1. 0.75 0.875 0.875 0.875 0.85714286] mean value: 0.8857142857142858 key: train_accuracy value: [0.98591549 0.98591549 1. 1. 0.97183099 1. 1. 1. 0.98591549 1. ] mean value: 0.9929577464788732 key: test_roc_auc value: [0.83333333 0.83333333 0.83333333 1. 1. 0.8 0.83333333 0.83333333 0.83333333 0.9 ] mean value: 0.8699999999999999 key: train_roc_auc value: [0.98076923 0.98076923 1. 1. 0.96153846 1. 1. 1. 0.98076923 1. ] mean value: 0.9903846153846153 key: test_jcc value: [0.66666667 0.66666667 0.66666667 1. 1. 0.6 0.66666667 0.66666667 0.66666667 0.66666667] mean value: 0.7266666666666668 key: train_jcc value: [0.96153846 0.96153846 1. 1. 0.92307692 1. 1. 1. 0.96153846 1. ] mean value: 0.9807692307692308 key: TN value: 47 mean value: 47.0 key: FP value: 6 mean value: 6.0 key: FN value: 3 mean value: 3.0 key: TP value: 23 mean value: 23.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.95 Accuracy on Blind test: 0.98 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.01245141 0.01464605 0.01453471 0.01492357 0.01498866 0.01470661 0.01472354 0.01476574 0.01528478 0.01498938] mean value: 0.014601445198059082 key: score_time value: [0.01121616 0.01147175 0.01149368 0.01152205 0.01179218 0.01165199 0.01153016 0.01156569 0.01180983 0.01152635] mean value: 0.011557984352111816 key: test_mcc value: [-0.29277002 -0.06666667 0.74535599 -0.4472136 0.1490712 0.77459667 0.1490712 -0.29277002 0.48795004 0.73029674] mean value: 0.1936921532620129 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.33333333 0.8 0. 0.4 0.85714286 0.4 0. 0.5 0.8 ] mean value: 0.409047619047619 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0.33333333 1. 0. 0.5 0.75 0.5 0. 1. 0.66666667] mean value: 0.475 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.33333333 0.66666667 0. 0.33333333 1. 0.33333333 0. 0.33333333 1. ] mean value: 0.4 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.5 0.5 0.875 0.375 0.625 0.875 0.625 0.5 0.75 0.85714286] mean value: 0.6482142857142856 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.4 0.46666667 0.83333333 0.3 0.56666667 0.9 0.56666667 0.4 0.66666667 0.9 ] mean value: 0.6000000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0.2 0.66666667 0. 0.25 0.75 0.25 0. 0.33333333 0.66666667] mean value: 0.31166666666666665 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 40 mean value: 40.0 key: FP value: 18 mean value: 18.0 key: FN value: 10 mean value: 10.0 key: TP value: 11 mean value: 11.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.18 Accuracy on Blind test: 0.65 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.17009902 0.14949942 0.16850829 0.15098715 0.15430665 0.15416098 0.1536386 0.15502644 0.12563252 0.17038202] mean value: 0.15522410869598388 key: score_time value: [0.00882673 0.00872111 0.00868773 0.00874734 0.00878859 0.0092032 0.00890565 0.00874686 0.00879812 0.00924945] mean value: 0.008867478370666504 key: test_mcc value: [0.74535599 0.74535599 0.46666667 0.77459667 1. 0.6 0.74535599 1. 0.74535599 0.54772256] mean value: 0.7370409863413036 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.8 0.66666667 0.85714286 1. 0.75 0.8 1. 0.8 0.66666667] mean value: 0.8140476190476191 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.66666667 0.75 1. 0.6 1. 1. 1. 0.5 ] mean value: 0.8516666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.66666667 0.66666667 1. 1. 1. 0.66666667 1. 0.66666667 1. ] mean value: 0.8333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.875 0.875 0.75 0.875 1. 0.75 0.875 1. 0.875 0.71428571] mean value: 0.8589285714285714 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.83333333 0.83333333 0.73333333 0.9 1. 0.8 0.83333333 1. 0.83333333 0.8 ] mean value: 0.8566666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.66666667 0.5 0.75 1. 0.6 0.66666667 1. 0.66666667 0.5 ] mean value: 0.7016666666666667 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 44 mean value: 44.0 key: FP value: 5 mean value: 5.0 key: FN value: 6 mean value: 6.0 key: TP value: 24 mean value: 24.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.95 Accuracy on Blind test: 0.98 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01000357 0.01294374 0.01351094 0.01364636 0.01392603 0.01322412 0.0132308 0.01357937 0.01318026 0.01335979] mean value: 0.013060498237609863 key: score_time value: [0.01163244 0.0116744 0.01197124 0.01201701 0.01205063 0.01258826 0.01453471 0.01262641 0.01148725 0.01319456] mean value: 0.012377691268920899 key: test_mcc value: [-0.6 -0.06666667 0.74535599 -0.29277002 0.25819889 0.46666667 -0.46666667 -0.29277002 -0.06666667 0. ] mean value: -0.03153184948553623 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.33333333 0.8 0. 0.57142857 0.66666667 0.25 0. 0.33333333 0. ] mean value: 0.2954761904761905 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0.33333333 1. 0. 0.5 0.66666667 0.2 0. 0.33333333 0. ] mean value: 0.30333333333333334 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.33333333 0.66666667 0. 0.66666667 0.66666667 0.33333333 0. 0.33333333 0. ] mean value: 0.3 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.25 0.5 0.875 0.5 0.625 0.75 0.25 0.5 0.5 0.71428571] mean value: 0.5464285714285715 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.2 0.46666667 0.83333333 0.4 0.63333333 0.73333333 0.26666667 0.4 0.46666667 0.5 ] mean value: 0.48999999999999994 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0.2 0.66666667 0. 0.4 0.5 0.14285714 0. 0.2 0. ] mean value: 0.21095238095238095 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 34 mean value: 34.0 key: FP value: 20 mean value: 20.0 key: FN value: 16 mean value: 16.0 key: TP value: 9 mean value: 9.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.17 Accuracy on Blind test: 0.48 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.04468751 0.05357623 0.05370712 0.03210664 0.01244235 0.01242089 0.01257348 0.01239419 0.01240015 0.03188992] mean value: 0.02781984806060791 key: score_time value: [0.02110004 0.02102828 0.02099276 0.02104115 0.01156378 0.01158667 0.0115962 0.01159286 0.01153088 0.02093101] mean value: 0.01629636287689209 key: test_mcc value: [ 0.74535599 0.46666667 0.74535599 0.6 0.46666667 0.6 0.77459667 0.1490712 -0.06666667 0.73029674] mean value: 0.5211343262748217 key: train_mcc value: [0.96986363 0.96986363 1. 1. 1. 1. 1. 0.96986363 1. 1. ] mean value: 0.9909590875629279 key: test_fscore value: [0.8 0.66666667 0.8 0.75 0.66666667 0.75 0.85714286 0.4 0.33333333 0.8 ] mean value: 0.6823809523809523 key: train_fscore value: [0.98039216 0.98039216 1. 1. 1. 1. 1. 0.98039216 1. 1. ] mean value: 0.9941176470588236 key: test_precision value: [1. 0.66666667 1. 0.6 0.66666667 0.6 0.75 0.5 0.33333333 0.66666667] mean value: 0.6783333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.66666667 0.66666667 1. 0.66666667 1. 1. 0.33333333 0.33333333 1. ] mean value: 0.7333333333333333 key: train_recall value: [0.96153846 0.96153846 1. 1. 1. 1. 1. 0.96153846 1. 1. ] mean value: 0.9884615384615385 key: test_accuracy value: [0.875 0.75 0.875 0.75 0.75 0.75 0.875 0.625 0.5 0.85714286] mean value: 0.7607142857142857 key: train_accuracy value: [0.98591549 0.98591549 1. 1. 1. 1. 1. 0.98591549 1. 1. ] mean value: 0.995774647887324 key: test_roc_auc value: [0.83333333 0.73333333 0.83333333 0.8 0.73333333 0.8 0.9 0.56666667 0.46666667 0.9 ] mean value: 0.7566666666666666 key: train_roc_auc value: [0.98076923 0.98076923 1. 1. 1. 1. 1. 0.98076923 1. 1. ] mean value: 0.9942307692307694 key: test_jcc value: [0.66666667 0.5 0.66666667 0.6 0.5 0.6 0.75 0.25 0.2 0.66666667] mean value: 0.54 key: train_jcc value: [0.96153846 0.96153846 1. 1. 1. 1. 1. 0.96153846 1. 1. ] mean value: 0.9884615384615385 key: TN value: 39 mean value: 39.0 key: FP value: 8 mean value: 8.0 key: FN value: 11 mean value: 11.0 key: TP value: 21 mean value: 21.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.25 Accuracy on Blind test: 0.68 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:130: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:131: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_CV['Resampling'] = rs_none /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:136: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:137: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy baseline_BT['Resampling'] = rs_none Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.12132359 0.10357308 0.14754748 0.10202861 0.10264635 0.11086059 0.16924834 0.22648358 0.17872405 0.17529178] mean value: 0.14377274513244628 key: score_time value: [0.02111197 0.01191688 0.02108765 0.01181531 0.02225852 0.01208162 0.02574563 0.02083993 0.02090573 0.01990175] mean value: 0.018766498565673827 key: test_mcc value: [ 0.74535599 0.46666667 0.74535599 -0.06666667 0.46666667 0.6 0.77459667 0.1490712 0.06666667 0.3 ] mean value: 0.4247713186074663 key: train_mcc value: [1. 0.96986363 1. 0.81830122 1. 1. 1. 0.96986363 1. 0.77297107] mean value: 0.953099954418505 key: test_fscore value: [0.8 0.66666667 0.8 0.33333333 0.66666667 0.75 0.85714286 0.4 0.5 0.5 ] mean value: 0.6273809523809524 key: train_fscore value: [1. 0.98039216 1. 0.875 1. 1. 1. 0.98039216 1. 0.82608696] mean value: 0.9661871270247229 key: test_precision value: [1. 0.66666667 1. 0.33333333 0.66666667 0.6 0.75 0.5 0.4 0.5 ] mean value: 0.6416666666666667 key: train_precision value: [1. 1. 1. 0.95454545 1. 1. 1. 1. 1. 1. ] mean value: 0.9954545454545455 key: test_recall value: [0.66666667 0.66666667 0.66666667 0.33333333 0.66666667 1. 1. 0.33333333 0.66666667 0.5 ] mean value: 0.65 key: train_recall value: [1. 0.96153846 1. 0.80769231 1. 1. 1. 0.96153846 1. 0.7037037 ] mean value: 0.9434472934472934 key: test_accuracy value: [0.875 0.75 0.875 0.5 0.75 0.75 0.875 0.625 0.5 0.71428571] mean value: 0.7214285714285714 key: train_accuracy value: [1. 0.98591549 1. 0.91549296 1. 1. 1. 0.98591549 1. 0.88888889] mean value: 0.9776212832550861 key: test_roc_auc value: [0.83333333 0.73333333 0.83333333 0.46666667 0.73333333 0.8 0.9 0.56666667 0.53333333 0.65 ] mean value: 0.705 key: train_roc_auc value: [1. 0.98076923 1. 0.89273504 1. 1. 1. 0.98076923 1. 0.85185185] mean value: 0.9706125356125355 key: test_jcc value: [0.66666667 0.5 0.66666667 0.2 0.5 0.6 0.75 0.25 0.33333333 0.33333333] mean value: 0.47999999999999987 key: train_jcc value: [1. 0.96153846 1. 0.77777778 1. 1. 1. 0.96153846 1. 0.7037037 ] mean value: 0.9404558404558406 key: TN value: 38 mean value: 38.0 key: FP value: 10 mean value: 10.0 key: FN value: 12 mean value: 12.0 key: TP value: 19 mean value: 19.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.09 Accuracy on Blind test: 0.6 PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02095628 0.02766466 0.02307987 0.02544475 0.02728295 0.0233891 0.02444839 0.02446628 0.02351332 0.02731419] mean value: 0.024755978584289552 key: score_time value: [0.01138926 0.01142073 0.01142931 0.01147127 0.01143646 0.01138425 0.01142192 0.0114305 0.0113945 0.01140404] mean value: 0.01141822338104248 key: test_mcc value: [0.81649658 0.81649658 1. 0.21821789 0.40824829 0.81649658 0.6 0.65465367 0.21821789 0.6 ] mean value: 0.6148827484427002 key: train_mcc value: [0.97801929 0.95555556 0.95555556 0.95555556 0.95555556 0.97801929 0.95555556 0.95555556 1. 0.93356387] mean value: 0.962293579241685 key: test_fscore value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.90909091 0.90909091 1. 0.66666667 0.72727273 0.88888889 0.8 0.83333333 0.66666667 0.8 ] mean value: 0.8201010101010102 key: train_fscore value: [0.98876404 0.97777778 0.97777778 0.97777778 0.97777778 0.98876404 0.97777778 0.97777778 1. 0.96703297] mean value: 0.9811227723587275 key: test_precision value: [0.83333333 0.83333333 1. 0.57142857 0.66666667 1. 0.8 0.71428571 0.57142857 0.8 ] mean value: 0.779047619047619 key: train_precision value: [1. 0.97777778 0.97777778 0.97777778 0.97777778 1. 0.97777778 0.97777778 1. 0.95652174] mean value: 0.9823188405797103 key: test_recall value: [1. 1. 1. 0.8 0.8 0.8 0.8 1. 0.8 0.8] mean value: 0.8800000000000001 key: train_recall value: [0.97777778 0.97777778 0.97777778 0.97777778 0.97777778 0.97777778 0.97777778 0.97777778 1. 0.97777778] mean value: 0.9800000000000001 key: test_accuracy value: [0.9 0.9 1. 0.6 0.7 0.9 0.8 0.8 0.6 0.8] mean value: 0.8 key: train_accuracy value: [0.98888889 0.97777778 0.97777778 0.97777778 0.97777778 0.98888889 0.97777778 0.97777778 1. 0.96666667] mean value: 0.9811111111111112 key: test_roc_auc value: [0.9 0.9 1. 0.6 0.7 0.9 0.8 0.8 0.6 0.8] mean value: 0.8000000000000002 key: train_roc_auc value: [0.98888889 0.97777778 0.97777778 0.97777778 0.97777778 0.98888889 0.97777778 0.97777778 1. 0.96666667] mean value: 0.9811111111111112 key: test_jcc value: [0.83333333 0.83333333 1. 0.5 0.57142857 0.8 0.66666667 0.71428571 0.5 0.66666667] mean value: 0.7085714285714286 key: train_jcc value: [0.97777778 0.95652174 0.95652174 0.95652174 0.95652174 0.97777778 0.95652174 0.95652174 1. 0.93617021] mean value: 0.9630856203104123 key: TN value: 36 mean value: 36.0 key: FP value: 6 mean value: 6.0 key: FN value: 14 mean value: 14.0 key: TP value: 44 mean value: 44.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.23 Accuracy on Blind test: 0.65 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.63891172 0.51611137 0.76950169 0.56394839 0.5907445 0.58179784 0.55293965 0.53497767 0.65838146 0.52853274] mean value: 0.5935847043991089 key: score_time value: [0.01189089 0.01190567 0.01321769 0.01461577 0.01429796 0.0120244 0.01439905 0.01183438 0.01506233 0.01375699] mean value: 0.013300514221191407 key: test_mcc value: [1. 0.81649658 1. 0.21821789 0.6 0.81649658 0.40824829 0.81649658 0.40824829 0.81649658] mean value: 0.6900700794874622 key: train_mcc value: [0.97801929 1. 1. 1. 1. 0.97801929 1. 1. 1. 1. ] mean value: 0.9956038587687303 key: test_fscore value: [1. 0.90909091 1. 0.66666667 0.8 0.88888889 0.66666667 0.90909091 0.72727273 0.88888889] mean value: 0.8456565656565657 key: train_fscore value: [0.98876404 1. 1. 1. 1. 0.98876404 1. 1. 1. 1. ] mean value: 0.997752808988764 key: test_precision value: [1. 0.83333333 1. 0.57142857 0.8 1. 0.75 0.83333333 0.66666667 1. ] mean value: 0.8454761904761906 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.8 0.8 0.8 0.6 1. 0.8 0.8] mean value: 0.86 key: train_recall value: [0.97777778 1. 1. 1. 1. 0.97777778 1. 1. 1. 1. ] mean value: 0.9955555555555555 key: test_accuracy value: [1. 0.9 1. 0.6 0.8 0.9 0.7 0.9 0.7 0.9] mean value: 0.8400000000000001 key: train_accuracy value: [0.98888889 1. 1. 1. 1. 0.98888889 1. 1. 1. 1. ] mean value: 0.9977777777777778 key: test_roc_auc value: [1. 0.9 1. 0.6 0.8 0.9 0.7 0.9 0.7 0.9] mean value: 0.8400000000000001 key: train_roc_auc value: [0.98888889 1. 1. 1. 1. 0.98888889 1. 1. 1. 1. ] mean value: 0.9977777777777778 key: test_jcc value: [1. 0.83333333 1. 0.5 0.66666667 0.8 0.5 0.83333333 0.57142857 0.8 ] mean value: 0.7504761904761905 key: train_jcc value: [0.97777778 1. 1. 1. 1. 0.97777778 1. 1. 1. 1. ] mean value: 0.9955555555555555 key: TN value: 41 mean value: 41.0 key: FP value: 7 mean value: 7.0 key: FN value: 9 mean value: 9.0 key: TP value: 43 mean value: 43.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.25 Accuracy on Blind test: 0.68 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.011446 0.01138425 0.00847435 0.0083313 0.00806546 0.00832367 0.0079782 0.00797153 0.00808334 0.00805235] mean value: 0.008811044692993163 key: score_time value: [0.01148987 0.01026154 0.00851727 0.00848866 0.00831866 0.00834775 0.00825739 0.00826335 0.00835299 0.00837731] mean value: 0.008867478370666504 key: test_mcc value: [ 0.40824829 0.65465367 0.33333333 0.2 0.5 -0.21821789 0.2 0.33333333 -0.33333333 0. ] mean value: 0.20780174042691812 key: train_mcc value: [0.4412613 0.57055978 0.57055978 0.66097134 0.57055978 0.51708769 0.76486616 0.5527708 0.6681531 0.63737744] mean value: 0.5954167177357577 key: test_fscore value: [0.72727273 0.83333333 0.71428571 0.6 0.76923077 0.5 0.6 0.71428571 0.57142857 0.66666667] mean value: 0.6696503496503496 key: train_fscore value: [0.75229358 0.8 0.8 0.84 0.8 0.77876106 0.88659794 0.79279279 0.83870968 0.82568807] mean value: 0.8114843121679529 key: test_precision value: [0.66666667 0.71428571 0.55555556 0.6 0.625 0.42857143 0.6 0.55555556 0.44444444 0.5 ] mean value: 0.5690079365079365 key: train_precision value: [0.640625 0.67692308 0.67692308 0.76363636 0.67692308 0.64705882 0.82692308 0.66666667 0.8125 0.703125 ] mean value: 0.709130416152475 key: test_recall value: [0.8 1. 1. 0.6 1. 0.6 0.6 1. 0.8 1. ] mean value: 0.8400000000000001 key: train_recall value: [0.91111111 0.97777778 0.97777778 0.93333333 0.97777778 0.97777778 0.95555556 0.97777778 0.86666667 1. ] mean value: 0.9555555555555555 key: test_accuracy value: [0.7 0.8 0.6 0.6 0.7 0.4 0.6 0.6 0.4 0.5] mean value: 0.5900000000000001 key: train_accuracy value: [0.7 0.75555556 0.75555556 0.82222222 0.75555556 0.72222222 0.87777778 0.74444444 0.83333333 0.78888889] mean value: 0.7755555555555554 key: test_roc_auc value: [0.7 0.8 0.6 0.6 0.7 0.4 0.6 0.6 0.4 0.5] mean value: 0.5900000000000001 key: train_roc_auc value: [0.7 0.75555556 0.75555556 0.82222222 0.75555556 0.72222222 0.87777778 0.74444444 0.83333333 0.78888889] mean value: 0.7755555555555554 key: test_jcc value: [0.57142857 0.71428571 0.55555556 0.42857143 0.625 0.33333333 0.42857143 0.55555556 0.4 0.5 ] mean value: 0.5112301587301588 key: train_jcc value: [0.60294118 0.66666667 0.66666667 0.72413793 0.66666667 0.63768116 0.7962963 0.65671642 0.72222222 0.703125 ] mean value: 0.6843120203354327 key: TN value: 17 mean value: 17.0 key: FP value: 8 mean value: 8.0 key: FN value: 33 mean value: 33.0 key: TP value: 42 mean value: 42.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.25 Accuracy on Blind test: 0.55 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00854492 0.00822186 0.00823379 0.00817966 0.00828838 0.00816011 0.00876474 0.00883484 0.00813937 0.00822973] mean value: 0.008359742164611817 key: score_time value: [0.00832868 0.00838923 0.00824618 0.00832224 0.00821042 0.00837898 0.00903296 0.00819707 0.00829124 0.00826311] mean value: 0.008366012573242187 key: test_mcc value: [ 0.65465367 -0.40824829 0.81649658 0. 0.40824829 0.40824829 0.6 0.40824829 -0.40824829 0. ] mean value: 0.2479398542099566 key: train_mcc value: [0.60238451 0.73994007 0.65487619 0.64700558 0.69162666 0.62988978 0.71554175 0.78086881 0.64508188 0.65487619] mean value: 0.6762091428071246 key: test_fscore value: [0.75 0.36363636 0.90909091 0.61538462 0.72727273 0.66666667 0.8 0.72727273 0.36363636 0.54545455] mean value: 0.6468414918414919 key: train_fscore value: [0.80851064 0.875 0.83673469 0.82978723 0.85106383 0.82474227 0.86315789 0.89361702 0.81818182 0.83673469] mean value: 0.8437530092119255 key: test_precision value: [1. 0.33333333 0.83333333 0.5 0.66666667 0.75 0.8 0.66666667 0.33333333 0.5 ] mean value: 0.6383333333333334 key: train_precision value: [0.7755102 0.82352941 0.77358491 0.79591837 0.81632653 0.76923077 0.82 0.85714286 0.8372093 0.77358491] mean value: 0.8042037253825484 key: test_recall value: [0.6 0.4 1. 0.8 0.8 0.6 0.8 0.8 0.4 0.6] mean value: 0.6799999999999999 key: train_recall value: [0.84444444 0.93333333 0.91111111 0.86666667 0.88888889 0.88888889 0.91111111 0.93333333 0.8 0.91111111] mean value: 0.8888888888888887 key: test_accuracy value: [0.8 0.3 0.9 0.5 0.7 0.7 0.8 0.7 0.3 0.5] mean value: 0.62 key: train_accuracy value: [0.8 0.86666667 0.82222222 0.82222222 0.84444444 0.81111111 0.85555556 0.88888889 0.82222222 0.82222222] mean value: 0.8355555555555554 key: test_roc_auc value: [0.8 0.3 0.9 0.5 0.7 0.7 0.8 0.7 0.3 0.5] mean value: 0.62 key: train_roc_auc value: [0.8 0.86666667 0.82222222 0.82222222 0.84444444 0.81111111 0.85555556 0.88888889 0.82222222 0.82222222] mean value: 0.8355555555555556 key: test_jcc value: [0.6 0.22222222 0.83333333 0.44444444 0.57142857 0.5 0.66666667 0.57142857 0.22222222 0.375 ] mean value: 0.5006746031746031 key: train_jcc value: [0.67857143 0.77777778 0.71929825 0.70909091 0.74074074 0.70175439 0.75925926 0.80769231 0.69230769 0.71929825] mean value: 0.7305790992633099 key: TN value: 28 mean value: 28.0 key: FP value: 16 mean value: 16.0 key: FN value: 22 mean value: 22.0 key: TP value: 34 mean value: 34.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.1 Accuracy on Blind test: 0.52 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01051331 0.00788808 0.0079484 0.00784755 0.00783825 0.00789666 0.00844765 0.00793743 0.00793314 0.00828576] mean value: 0.00825362205505371 key: score_time value: [0.01452923 0.00902128 0.00907946 0.00902486 0.00899076 0.00922275 0.00933123 0.00924468 0.00922418 0.00955987] mean value: 0.00972282886505127 key: test_mcc value: [ 0.2 0.6 0.81649658 0.5 0.65465367 -0.40824829 0.5 0.65465367 0.21821789 0.40824829] mean value: 0.41440218125796735 key: train_mcc value: [0.60540551 0.65487619 0.60540551 0.62237591 0.58969198 0.67082039 0.62360956 0.58137767 0.73624773 0.60540551] mean value: 0.6295215990305435 key: test_fscore value: [0.6 0.8 0.90909091 0.76923077 0.83333333 0.36363636 0.76923077 0.83333333 0.66666667 0.72727273] mean value: 0.7271794871794872 key: train_fscore value: [0.8125 0.83673469 0.8125 0.81318681 0.80808081 0.84210526 0.8172043 0.8 0.87234043 0.8125 ] mean value: 0.822715230491025 key: test_precision value: [0.6 0.8 0.83333333 0.625 0.71428571 0.33333333 0.625 0.71428571 0.57142857 0.66666667] mean value: 0.6483333333333333 key: train_precision value: [0.76470588 0.77358491 0.76470588 0.80434783 0.74074074 0.8 0.79166667 0.76 0.83673469 0.76470588] mean value: 0.7801192480091116 key: test_recall value: [0.6 0.8 1. 1. 1. 0.4 1. 1. 0.8 0.8] mean value: 0.8400000000000001 key: train_recall value: [0.86666667 0.91111111 0.86666667 0.82222222 0.88888889 0.88888889 0.84444444 0.84444444 0.91111111 0.86666667] mean value: 0.8711111111111112 key: test_accuracy value: [0.6 0.8 0.9 0.7 0.8 0.3 0.7 0.8 0.6 0.7] mean value: 0.69 key: train_accuracy value: [0.8 0.82222222 0.8 0.81111111 0.78888889 0.83333333 0.81111111 0.78888889 0.86666667 0.8 ] mean value: 0.8122222222222224 key: test_roc_auc value: [0.6 0.8 0.9 0.7 0.8 0.3 0.7 0.8 0.6 0.7] mean value: 0.69 key: train_roc_auc value: [0.8 0.82222222 0.8 0.81111111 0.78888889 0.83333333 0.81111111 0.78888889 0.86666667 0.8 ] mean value: 0.8122222222222222 key: test_jcc value: [0.42857143 0.66666667 0.83333333 0.625 0.71428571 0.22222222 0.625 0.71428571 0.5 0.57142857] mean value: 0.5900793650793651 key: train_jcc value: [0.68421053 0.71929825 0.68421053 0.68518519 0.6779661 0.72727273 0.69090909 0.66666667 0.77358491 0.68421053] mean value: 0.6993514501950366 key: TN value: 27 mean value: 27.0 key: FP value: 8 mean value: 8.0 key: FN value: 23 mean value: 23.0 key: TP value: 42 mean value: 42.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.04 Accuracy on Blind test: 0.48 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.00914907 0.00866842 0.00909472 0.00902867 0.00853419 0.00859928 0.0086832 0.00868559 0.0086875 0.00870299] mean value: 0.008783364295959472 key: score_time value: [0.00892496 0.0084846 0.00915527 0.00837111 0.00835466 0.00840259 0.00836229 0.00844765 0.00840688 0.0084002 ] mean value: 0.008531022071838378 key: test_mcc value: [0.40824829 0.6 1. 0.40824829 0.40824829 0.81649658 0.6 0.65465367 0.21821789 0.40824829] mean value: 0.5522361303727148 key: train_mcc value: [0.8230355 0.86666667 0.84632727 0.84465303 0.82548988 0.87011096 0.88910845 0.86666667 0.91111111 0.84632727] mean value: 0.8589496793973822 key: test_fscore value: [0.66666667 0.8 1. 0.72727273 0.72727273 0.88888889 0.8 0.83333333 0.66666667 0.66666667] mean value: 0.7776767676767677 key: train_fscore value: [0.91304348 0.93333333 0.92473118 0.92134831 0.91489362 0.93617021 0.94505495 0.93333333 0.95555556 0.92473118] mean value: 0.9302195155523411 key: test_precision value: [0.75 0.8 1. 0.66666667 0.66666667 1. 0.8 0.71428571 0.57142857 0.75 ] mean value: 0.7719047619047619 key: train_precision value: [0.89361702 0.93333333 0.89583333 0.93181818 0.87755102 0.89795918 0.93478261 0.93333333 0.95555556 0.89583333] mean value: 0.9149616904760952 key: test_recall value: [0.6 0.8 1. 0.8 0.8 0.8 0.8 1. 0.8 0.6] mean value: 0.8 key: train_recall value: [0.93333333 0.93333333 0.95555556 0.91111111 0.95555556 0.97777778 0.95555556 0.93333333 0.95555556 0.95555556] mean value: 0.9466666666666667 key: test_accuracy value: [0.7 0.8 1. 0.7 0.7 0.9 0.8 0.8 0.6 0.7] mean value: 0.77 key: train_accuracy value: [0.91111111 0.93333333 0.92222222 0.92222222 0.91111111 0.93333333 0.94444444 0.93333333 0.95555556 0.92222222] mean value: 0.928888888888889 key: test_roc_auc value: [0.7 0.8 1. 0.7 0.7 0.9 0.8 0.8 0.6 0.7] mean value: 0.77 key: train_roc_auc value: [0.91111111 0.93333333 0.92222222 0.92222222 0.91111111 0.93333333 0.94444444 0.93333333 0.95555556 0.92222222] mean value: 0.928888888888889 key: test_jcc value: [0.5 0.66666667 1. 0.57142857 0.57142857 0.8 0.66666667 0.71428571 0.5 0.5 ] mean value: 0.6490476190476191 key: train_jcc value: [0.84 0.875 0.86 0.85416667 0.84313725 0.88 0.89583333 0.875 0.91489362 0.86 ] mean value: 0.8698030871923237 key: TN value: 37 mean value: 37.0 key: FP value: 10 mean value: 10.0 key: FN value: 13 mean value: 13.0 key: TP value: 40 mean value: 40.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.1 Accuracy on Blind test: 0.5 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.34990168 0.50119901 0.34612966 0.31338263 0.36281276 0.32218599 0.34134626 0.46834445 0.28037453 0.3393724 ] mean value: 0.3625049352645874 key: score_time value: [0.01199079 0.01198435 0.011935 0.01188803 0.01190996 0.01193237 0.01193595 0.0119586 0.01192045 0.01193857] mean value: 0.011939406394958496 key: test_mcc value: [0.65465367 0.81649658 1. 0. 0.2 0.81649658 0.40824829 0.65465367 0. 0.81649658] mean value: 0.5367045374662995 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.75 0.90909091 1. 0.54545455 0.6 0.88888889 0.66666667 0.83333333 0.61538462 0.88888889] mean value: 0.7697707847707849 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.83333333 1. 0.5 0.6 1. 0.75 0.71428571 0.5 1. ] mean value: 0.7897619047619048 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.6 1. 1. 0.6 0.6 0.8 0.6 1. 0.8 0.8] mean value: 0.78 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.9 1. 0.5 0.6 0.9 0.7 0.8 0.5 0.9] mean value: 0.76 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8 0.9 1. 0.5 0.6 0.9 0.7 0.8 0.5 0.9] mean value: 0.76 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.6 0.83333333 1. 0.375 0.42857143 0.8 0.5 0.71428571 0.44444444 0.8 ] mean value: 0.6495634920634921 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 37 mean value: 37.0 key: FP value: 11 mean value: 11.0 key: FN value: 13 mean value: 13.0 key: TP value: 39 mean value: 39.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.13 Accuracy on Blind test: 0.62 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01446295 0.01370049 0.01050329 0.01081491 0.01074243 0.01266026 0.01105475 0.01161981 0.01020932 0.01007462] mean value: 0.011584281921386719 key: score_time value: [0.01130819 0.01019692 0.00851107 0.00835633 0.00995207 0.00970793 0.00899029 0.00867486 0.00860381 0.00912476] mean value: 0.009342622756958009 key: test_mcc value: [1. 0.81649658 0.65465367 0.65465367 1. 0.6 0.65465367 0.40824829 0.40824829 0.81649658] mean value: 0.701345075490711 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.90909091 0.83333333 0.83333333 1. 0.8 0.75 0.72727273 0.72727273 0.88888889] mean value: 0.846919191919192 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.83333333 0.71428571 0.71428571 1. 0.8 1. 0.66666667 0.66666667 1. ] mean value: 0.8395238095238096 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 0.8 0.6 0.8 0.8 0.8] mean value: 0.8800000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.9 0.8 0.8 1. 0.8 0.8 0.7 0.7 0.9] mean value: 0.8400000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.9 0.8 0.8 1. 0.8 0.8 0.7 0.7 0.9] mean value: 0.8400000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.83333333 0.71428571 0.71428571 1. 0.66666667 0.6 0.57142857 0.57142857 0.8 ] mean value: 0.7471428571428571 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 40 mean value: 40.0 key: FP value: 6 mean value: 6.0 key: FN value: 10 mean value: 10.0 key: TP value: 44 mean value: 44.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.75 Accuracy on Blind test: 0.88 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.08087611 0.08038592 0.08071065 0.08093286 0.08061075 0.0805552 0.08088231 0.08353758 0.08065701 0.08074427] mean value: 0.08098926544189453 key: score_time value: [0.01645565 0.01689053 0.01666975 0.01656699 0.0165782 0.01668191 0.01906228 0.01703238 0.01756358 0.01673245] mean value: 0.017023372650146484 key: test_mcc value: [0.81649658 0.6 1. 0.40824829 0.6 0.5 0.2 0.65465367 0.21821789 0.65465367] mean value: 0.5652270103043536 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.88888889 0.8 1. 0.72727273 0.8 0.57142857 0.6 0.83333333 0.66666667 0.75 ] mean value: 0.7637590187590187 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.8 1. 0.66666667 0.8 1. 0.6 0.71428571 0.57142857 1. ] mean value: 0.8152380952380952 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.8 1. 0.8 0.8 0.4 0.6 1. 0.8 0.6] mean value: 0.76 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9 0.8 1. 0.7 0.8 0.7 0.6 0.8 0.6 0.8] mean value: 0.77 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9 0.8 1. 0.7 0.8 0.7 0.6 0.8 0.6 0.8] mean value: 0.77 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.8 0.66666667 1. 0.57142857 0.66666667 0.4 0.42857143 0.71428571 0.5 0.6 ] mean value: 0.6347619047619047 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 39 mean value: 39.0 key: FP value: 12 mean value: 12.0 key: FN value: 11 mean value: 11.0 key: TP value: 38 mean value: 38.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.3 Accuracy on Blind test: 0.7 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00929761 0.0082314 0.00812483 0.0081141 0.00811553 0.00808263 0.00860763 0.00804114 0.00888228 0.00918865] mean value: 0.00846858024597168 key: score_time value: [0.00910568 0.00822258 0.00867009 0.00826836 0.00844622 0.00843167 0.00880265 0.00870919 0.00882745 0.00881433] mean value: 0.008629822731018066 key: test_mcc value: [ 0.6 0.6 0.40824829 0.5 0. 0.40824829 0.21821789 0.5 -0.40824829 0.40824829] mean value: 0.3234714471163718 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.8 0.72727273 0.76923077 0.44444444 0.66666667 0.5 0.76923077 0.36363636 0.66666667] mean value: 0.6507148407148406 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.8 0.66666667 0.625 0.5 0.75 0.66666667 0.625 0.33333333 0.75 ] mean value: 0.6516666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.8 0.8 1. 0.4 0.6 0.4 1. 0.4 0.6] mean value: 0.68 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.8 0.7 0.7 0.5 0.7 0.6 0.7 0.3 0.7] mean value: 0.65 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8 0.8 0.7 0.7 0.5 0.7 0.6 0.7 0.3 0.7] mean value: 0.65 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.66666667 0.57142857 0.625 0.28571429 0.5 0.33333333 0.625 0.22222222 0.5 ] mean value: 0.4996031746031746 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 31 mean value: 31.0 key: FP value: 16 mean value: 16.0 key: FN value: 19 mean value: 19.0 key: TP value: 34 mean value: 34.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.02 Accuracy on Blind test: 0.55 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.08314943 1.05717731 1.05498576 1.04640746 1.04371905 1.05806494 1.05173302 1.04391503 1.03815794 1.03380728] mean value: 1.0511117219924926 key: score_time value: [0.09315157 0.0880518 0.08978987 0.0893538 0.08839059 0.08848882 0.0899272 0.09106064 0.08935261 0.0950737 ] mean value: 0.09026405811309815 key: test_mcc value: [0.81649658 0.65465367 1. 0.65465367 0.81649658 0.65465367 0.65465367 0.65465367 0.40824829 0.65465367] mean value: 0.6969163476567177 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.88888889 0.83333333 1. 0.75 0.90909091 0.75 0.75 0.83333333 0.72727273 0.75 ] mean value: 0.8191919191919192 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.71428571 1. 1. 0.83333333 1. 1. 0.71428571 0.66666667 1. ] mean value: 0.892857142857143 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 1. 1. 0.6 1. 0.6 0.6 1. 0.8 0.6] mean value: 0.8 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9 0.8 1. 0.8 0.9 0.8 0.8 0.8 0.7 0.8] mean value: 0.8300000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9 0.8 1. 0.8 0.9 0.8 0.8 0.8 0.7 0.8] mean value: 0.8300000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.8 0.71428571 1. 0.6 0.83333333 0.6 0.6 0.71428571 0.57142857 0.6 ] mean value: 0.7033333333333334 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 43 mean value: 43.0 key: FP value: 10 mean value: 10.0 key: FN value: 7 mean value: 7.0 key: TP value: 40 mean value: 40.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.6 Accuracy on Blind test: 0.82 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.8008554 0.86556268 0.83195925 0.85484838 0.86451149 0.86783385 0.90637803 0.84007192 0.82203436 0.86065936] mean value: 0.8514714717864991 key: score_time value: [0.17614007 0.23751974 0.16117907 0.16920114 0.17847824 0.17201066 0.16586041 0.17395544 0.19984627 0.19024968] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( mean value: 0.18244407176971436 key: test_mcc value: [0.81649658 0.65465367 0.81649658 0.65465367 0.81649658 0.81649658 0.81649658 0.40824829 0.40824829 0.65465367] mean value: 0.6862940497690287 key: train_mcc value: [1. 0.97801929 0.97801929 0.95555556 0.97801929 1. 0.97801929 0.97801929 1. 1. ] mean value: 0.9845652024773812 key: test_fscore value: [0.90909091 0.83333333 0.88888889 0.75 0.90909091 0.88888889 0.88888889 0.72727273 0.72727273 0.75 ] mean value: 0.8272727272727272 key: train_fscore value: [1. 0.98901099 0.98901099 0.97777778 0.98901099 1. 0.98901099 0.98901099 1. 1. ] mean value: 0.9922832722832723 key: test_precision value: [0.83333333 0.71428571 1. 1. 0.83333333 1. 1. 0.66666667 0.66666667 1. ] mean value: 0.8714285714285716 key: train_precision value: [1. 0.97826087 0.97826087 0.97777778 0.97826087 1. 0.97826087 0.97826087 1. 1. ] mean value: 0.9869082125603864 key: test_recall value: [1. 1. 0.8 0.6 1. 0.8 0.8 0.8 0.8 0.6] mean value: 0.8200000000000001 key: train_recall value: [1. 1. 1. 0.97777778 1. 1. 1. 1. 1. 1. ] mean value: 0.9977777777777778 key: test_accuracy value: [0.9 0.8 0.9 0.8 0.9 0.9 0.9 0.7 0.7 0.8] mean value: 0.8300000000000001 key: train_accuracy value: [1. 0.98888889 0.98888889 0.97777778 0.98888889 1. 0.98888889 0.98888889 1. 1. ] mean value: 0.9922222222222222 key: test_roc_auc value: [0.9 0.8 0.9 0.8 0.9 0.9 0.9 0.7 0.7 0.8] mean value: 0.8300000000000001 key: train_roc_auc value: [1. 0.98888889 0.98888889 0.97777778 0.98888889 1. 0.98888889 0.98888889 1. 1. ] mean value: 0.9922222222222222 key: test_jcc value: [0.83333333 0.71428571 0.8 0.6 0.83333333 0.8 0.8 0.57142857 0.57142857 0.6 ] mean value: 0.7123809523809523 key: train_jcc value: [1. 0.97826087 0.97826087 0.95652174 0.97826087 1. 0.97826087 0.97826087 1. 1. ] mean value: 0.9847826086956522 key: TN value: 42 mean value: 42.0 key: FP value: 9 mean value: 9.0 key: FN value: 8 mean value: 8.0 key: TP value: 41 mean value: 41.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.6 Accuracy on Blind test: 0.82 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.03647876 0.03303075 0.03647852 0.03536677 0.03443265 0.03521061 0.03624892 0.03567147 0.03307509 0.03529716] mean value: 0.03512907028198242 key: score_time value: [0.0158205 0.01005316 0.06378174 0.00998211 0.01029038 0.01069117 0.00997925 0.00992107 0.01004457 0.0107832 ] mean value: 0.01613471508026123 key: test_mcc value: [1. 0.81649658 1. 1. 1. 0.6 0.81649658 0.6 0.5 0.81649658] mean value: 0.8149489742783178 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.90909091 1. 1. 1. 0.8 0.88888889 0.8 0.76923077 0.88888889] mean value: 0.9056099456099458 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.83333333 1. 1. 1. 0.8 1. 0.8 0.625 1. ] mean value: 0.9058333333333334 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 0.8 0.8 0.8 1. 0.8] mean value: 0.9200000000000002 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.9 1. 1. 1. 0.8 0.9 0.8 0.7 0.9] mean value: 0.9 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.9 1. 1. 1. 0.8 0.9 0.8 0.7 0.9] mean value: 0.9 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.83333333 1. 1. 1. 0.66666667 0.8 0.66666667 0.625 0.8 ] mean value: 0.8391666666666667 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 44 mean value: 44.0 key: FP value: 4 mean value: 4.0 key: FN value: 6 mean value: 6.0 key: TP value: 46 mean value: 46.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.89 Accuracy on Blind test: 0.95 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02231097 0.04178953 0.03549719 0.04157138 0.04162025 0.03488564 0.04136801 0.04148436 0.04149842 0.04407692] mean value: 0.03861026763916016 key: score_time value: [0.02140141 0.02215552 0.01681995 0.02330375 0.01159453 0.02025843 0.01248741 0.01387024 0.02162814 0.02118063] mean value: 0.018470001220703126 key: test_mcc value: [0.81649658 0.81649658 0.6 0.40824829 0.40824829 0.21821789 0.65465367 0.5 0.40824829 0. ] mean value: 0.4830609594191011 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.90909091 0.90909091 0.8 0.72727273 0.72727273 0.66666667 0.83333333 0.76923077 0.72727273 0.61538462] mean value: 0.7684615384615385 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.83333333 0.83333333 0.8 0.66666667 0.66666667 0.57142857 0.71428571 0.625 0.66666667 0.5 ] mean value: 0.6877380952380954 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.8 0.8 0.8 0.8 1. 1. 0.8 0.8] mean value: 0.8800000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9 0.9 0.8 0.7 0.7 0.6 0.8 0.7 0.7 0.5] mean value: 0.73 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9 0.9 0.8 0.7 0.7 0.6 0.8 0.7 0.7 0.5] mean value: 0.73 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.83333333 0.83333333 0.66666667 0.57142857 0.57142857 0.5 0.71428571 0.625 0.57142857 0.44444444] mean value: 0.6331349206349206 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 29 mean value: 29.0 key: FP value: 6 mean value: 6.0 key: FN value: 21 mean value: 21.0 key: TP value: 44 mean value: 44.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.16 Accuracy on Blind test: 0.62 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01321387 0.01240396 0.00874496 0.0092957 0.00834417 0.00813174 0.00837374 0.00808525 0.00824094 0.00825596] mean value: 0.009309029579162598 key: score_time value: [0.01152873 0.01367998 0.00886726 0.00834537 0.00828481 0.00844979 0.00816917 0.00834799 0.00844622 0.00816035] mean value: 0.009227967262268067 key: test_mcc value: [ 0. 0.6 0.2 0.2 0.40824829 0.6 0. 0. -0.40824829 0.40824829] mean value: 0.20082482904638627 key: train_mcc value: [0.4260261 0.53665631 0.51571581 0.64444444 0.68888889 0.60238451 0.62360956 0.51111111 0.51161666 0.62237591] mean value: 0.5682829320802611 key: test_fscore value: [0.54545455 0.8 0.6 0.6 0.72727273 0.8 0.44444444 0.44444444 0.36363636 0.66666667] mean value: 0.599191919191919 key: train_fscore value: [0.72916667 0.75294118 0.73809524 0.82222222 0.84444444 0.80851064 0.8045977 0.75555556 0.76086957 0.80898876] mean value: 0.7825391972164348 key: test_precision value: [0.5 0.8 0.6 0.6 0.66666667 0.8 0.5 0.5 0.33333333 0.75 ] mean value: 0.605 key: train_precision value: [0.68627451 0.8 0.79487179 0.82222222 0.84444444 0.7755102 0.83333333 0.75555556 0.74468085 0.81818182] mean value: 0.7875074733558554 key: test_recall value: [0.6 0.8 0.6 0.6 0.8 0.8 0.4 0.4 0.4 0.6] mean value: 0.6 key: train_recall value: [0.77777778 0.71111111 0.68888889 0.82222222 0.84444444 0.84444444 0.77777778 0.75555556 0.77777778 0.8 ] mean value: 0.78 key: test_accuracy value: [0.5 0.8 0.6 0.6 0.7 0.8 0.5 0.5 0.3 0.7] mean value: 0.6 key: train_accuracy value: [0.71111111 0.76666667 0.75555556 0.82222222 0.84444444 0.8 0.81111111 0.75555556 0.75555556 0.81111111] mean value: 0.7833333333333333 key: test_roc_auc value: [0.5 0.8 0.6 0.6 0.7 0.8 0.5 0.5 0.3 0.7] mean value: 0.6 key: train_roc_auc value: [0.71111111 0.76666667 0.75555556 0.82222222 0.84444444 0.8 0.81111111 0.75555556 0.75555556 0.81111111] mean value: 0.7833333333333332 key: test_jcc value: [0.375 0.66666667 0.42857143 0.42857143 0.57142857 0.66666667 0.28571429 0.28571429 0.22222222 0.5 ] mean value: 0.44305555555555554 key: train_jcc value: [0.57377049 0.60377358 0.58490566 0.69811321 0.73076923 0.67857143 0.67307692 0.60714286 0.61403509 0.67924528] mean value: 0.6443403754932071 key: TN value: 30 mean value: 30.0 key: FP value: 20 mean value: 20.0 key: FN value: 20 mean value: 20.0 key: TP value: 30 mean value: 30.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.26 Accuracy on Blind test: 0.35 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01003146 0.01199222 0.01306844 0.01315403 0.01348281 0.01319456 0.01266003 0.0128665 0.01410055 0.01346374] mean value: 0.012801432609558105 key: score_time value: [0.00822568 0.01112962 0.01109433 0.0114522 0.01152349 0.01129174 0.0113523 0.0113461 0.01147819 0.01160097] mean value: 0.011049461364746094 key: test_mcc value: [1. 0.33333333 1. 0. 0.6 0.81649658 0.40824829 0.81649658 0.40824829 1. ] mean value: 0.6382823076116511 key: train_mcc value: [1. 0.51730613 0.97801929 0.97801929 1. 0.95650071 0.89442719 0.87447463 1. 1. ] mean value: 0.9198747257089167 key: test_fscore value: [1. 0.33333333 1. 0.54545455 0.8 0.90909091 0.72727273 0.88888889 0.72727273 1. ] mean value: 0.7931313131313131 key: train_fscore value: [1. 0.59375 0.98876404 0.98876404 1. 0.97826087 0.94736842 0.92857143 1. 1. ] mean value: 0.9425478809076917 key: test_precision value: [1. 1. 1. 0.5 0.8 0.83333333 0.66666667 1. 0.66666667 1. ] mean value: 0.8466666666666667 key: train_precision value: [1. 1. 1. 1. 1. 0.95744681 0.9 1. 1. 1. ] mean value: 0.9857446808510637 key: test_recall value: [1. 0.2 1. 0.6 0.8 1. 0.8 0.8 0.8 1. ] mean value: 0.8 key: train_recall value: [1. 0.42222222 0.97777778 0.97777778 1. 1. 1. 0.86666667 1. 1. ] mean value: 0.9244444444444444 key: test_accuracy value: [1. 0.6 1. 0.5 0.8 0.9 0.7 0.9 0.7 1. ] mean value: 0.8100000000000002 key: train_accuracy value: [1. 0.71111111 0.98888889 0.98888889 1. 0.97777778 0.94444444 0.93333333 1. 1. ] mean value: 0.9544444444444444 key: test_roc_auc value: [1. 0.6 1. 0.5 0.8 0.9 0.7 0.9 0.7 1. ] mean value: 0.8100000000000002 key: train_roc_auc value: [1. 0.71111111 0.98888889 0.98888889 1. 0.97777778 0.94444444 0.93333333 1. 1. ] mean value: 0.9544444444444444 key: test_jcc value: [1. 0.2 1. 0.375 0.66666667 0.83333333 0.57142857 0.8 0.57142857 1. ] mean value: 0.7017857142857142 key: train_jcc value: [1. 0.42222222 0.97777778 0.97777778 1. 0.95744681 0.9 0.86666667 1. 1. ] mean value: 0.9101891252955083 key: TN value: 41 mean value: 41.0 key: FP value: 10 mean value: 10.0 key: FN value: 9 mean value: 9.0 key: TP value: 40 mean value: 40.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.28 Accuracy on Blind test: 0.7 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.00887799 0.01214647 0.01226735 0.01229191 0.01261067 0.01222086 0.01222086 0.01250696 0.01220679 0.01205516] mean value: 0.011940503120422363 key: score_time value: [0.00837612 0.0112083 0.0112288 0.0112474 0.01141977 0.01135349 0.01139736 0.01148057 0.0116353 0.01141691] mean value: 0.011076402664184571 key: test_mcc value: [0.6 0.81649658 1. 0. 0.6 0.40824829 0.6 0.6 0.40824829 0.6 ] mean value: 0.5632993161855452 key: train_mcc value: [0.95555556 0.95555556 0.93541435 1. 1. 0.95555556 0.97801929 1. 1. 0.93541435] mean value: 0.9715514653897289 key: test_fscore value: [0.8 0.90909091 1. 0.54545455 0.8 0.72727273 0.8 0.8 0.66666667 0.8 ] mean value: 0.7848484848484849 key: train_fscore value: [0.97777778 0.97777778 0.96551724 1. 1. 0.97777778 0.98876404 1. 1. 0.96774194] mean value: 0.9855356555140335 key: test_precision value: [0.8 0.83333333 1. 0.5 0.8 0.66666667 0.8 0.8 0.75 0.8 ] mean value: 0.775 key: train_precision value: [0.97777778 0.97777778 1. 1. 1. 0.97777778 1. 1. 1. 0.9375 ] mean value: 0.9870833333333333 key: test_recall value: [0.8 1. 1. 0.6 0.8 0.8 0.8 0.8 0.6 0.8] mean value: 0.8 key: train_recall value: [0.97777778 0.97777778 0.93333333 1. 1. 0.97777778 0.97777778 1. 1. 1. ] mean value: 0.9844444444444445 key: test_accuracy value: [0.8 0.9 1. 0.5 0.8 0.7 0.8 0.8 0.7 0.8] mean value: 0.78 key: train_accuracy value: [0.97777778 0.97777778 0.96666667 1. 1. 0.97777778 0.98888889 1. 1. 0.96666667] mean value: 0.9855555555555556 key: test_roc_auc value: [0.8 0.9 1. 0.5 0.8 0.7 0.8 0.8 0.7 0.8] mean value: 0.78 key: train_roc_auc value: [0.97777778 0.97777778 0.96666667 1. 1. 0.97777778 0.98888889 1. 1. 0.96666667] mean value: 0.9855555555555556 key: test_jcc value: [0.66666667 0.83333333 1. 0.375 0.66666667 0.57142857 0.66666667 0.66666667 0.5 0.66666667] mean value: 0.6613095238095238 key: train_jcc value: [0.95652174 0.95652174 0.93333333 1. 1. 0.95652174 0.97777778 1. 1. 0.9375 ] mean value: 0.9718176328502416 key: TN value: 38 mean value: 38.0 key: FP value: 10 mean value: 10.0 key: FN value: 12 mean value: 12.0 key: TP value: 40 mean value: 40.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.04 Accuracy on Blind test: 0.52 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.08591294 0.0870769 0.08603144 0.08604956 0.08540297 0.08636951 0.08703423 0.09059286 0.08867574 0.08594322] mean value: 0.08690893650054932 key: score_time value: [0.01451015 0.01454926 0.01424074 0.01429844 0.01511765 0.01439977 0.01509309 0.01519299 0.01466537 0.01518536] mean value: 0.014725279808044434 key: test_mcc value: [0.81649658 0.81649658 1. 1. 1. 0.6 1. 0.81649658 0.5 0.81649658] mean value: 0.8365986323710904 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.90909091 0.90909091 1. 1. 1. 0.8 1. 0.90909091 0.76923077 0.88888889] mean value: 0.9185392385392387 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.83333333 0.83333333 1. 1. 1. 0.8 1. 0.83333333 0.625 1. ] mean value: 0.8925000000000001 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 0.8 1. 1. 1. 0.8] mean value: 0.9600000000000002 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9 0.9 1. 1. 1. 0.8 1. 0.9 0.7 0.9] mean value: 0.9099999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9 0.9 1. 1. 1. 0.8 1. 0.9 0.7 0.9] mean value: 0.9099999999999999 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.83333333 0.83333333 1. 1. 1. 0.66666667 1. 0.83333333 0.625 0.8 ] mean value: 0.8591666666666666 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 43 mean value: 43.0 key: FP value: 2 mean value: 2.0 key: FN value: 7 mean value: 7.0 key: TP value: 48 mean value: 48.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.83 Accuracy on Blind test: 0.92 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.02613139 0.03143096 0.02982998 0.02666664 0.03157735 0.03069139 0.02667904 0.02771163 0.0300796 0.02920628] mean value: 0.02900042533874512 key: score_time value: [0.01907611 0.02446222 0.01595473 0.02101159 0.01648688 0.02567482 0.01766539 0.01646376 0.02223778 0.02316689] mean value: 0.020220017433166503 key: test_mcc value: [1. 0.81649658 1. 0.81649658 1. 0.81649658 0.81649658 1. 0.6 0.81649658] mean value: 0.8682482904638629 key: train_mcc value: [0.97801929 0.97801929 0.97801929 1. 1. 1. 0.97801929 1. 1. 1. ] mean value: 0.9912077175374605 key: test_fscore value: [1. 0.90909091 1. 0.90909091 1. 0.88888889 0.88888889 1. 0.8 0.88888889] mean value: 0.9284848484848485 key: train_fscore value: [0.98876404 0.98876404 0.98876404 1. 1. 1. 0.98901099 1. 1. 1. ] mean value: 0.9955303123842449 key: test_precision value: [1. 0.83333333 1. 0.83333333 1. 1. 1. 1. 0.8 1. ] mean value: 0.9466666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 0.97826087 1. 1. 1. ] mean value: 0.9978260869565216 key: test_recall value: [1. 1. 1. 1. 1. 0.8 0.8 1. 0.8 0.8] mean value: 0.9200000000000002 key: train_recall value: [0.97777778 0.97777778 0.97777778 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9933333333333334 key: test_accuracy value: [1. 0.9 1. 0.9 1. 0.9 0.9 1. 0.8 0.9] mean value: 0.93 key: train_accuracy value: [0.98888889 0.98888889 0.98888889 1. 1. 1. 0.98888889 1. 1. 1. ] mean value: 0.9955555555555555 key: test_roc_auc value: [1. 0.9 1. 0.9 1. 0.9 0.9 1. 0.8 0.9] mean value: 0.93 key: train_roc_auc value: [0.98888889 0.98888889 0.98888889 1. 1. 1. 0.98888889 1. 1. 1. ] mean value: 0.9955555555555555 key: test_jcc value: [1. 0.83333333 1. 0.83333333 1. 0.8 0.8 1. 0.66666667 0.8 ] mean value: 0.8733333333333334 key: train_jcc value: [0.97777778 0.97777778 0.97777778 1. 1. 1. 0.97826087 1. 1. 1. ] mean value: 0.991159420289855 key: TN value: 47 mean value: 47.0 key: FP value: 4 mean value: 4.0 key: FN value: 3 mean value: 3.0 key: TP value: 46 mean value: 46.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.89 Accuracy on Blind test: 0.95 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.01337504 0.01569223 0.01567912 0.01595926 0.01574159 0.01597762 0.01579881 0.01569128 0.01606631 0.01576662] mean value: 0.015574789047241211 key: score_time value: [0.0116837 0.01153421 0.01165748 0.01159096 0.01157904 0.01181698 0.0116744 0.01153874 0.0116055 0.01170111] mean value: 0.011638212203979491 key: test_mcc value: [ 0.6 0.40824829 1. 0.40824829 0.40824829 0. 0.21821789 0.65465367 -0.21821789 0.40824829] mean value: 0.3887646832563429 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.66666667 1. 0.72727273 0.72727273 0.44444444 0.66666667 0.83333333 0.5 0.66666667] mean value: 0.7032323232323232 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.75 1. 0.66666667 0.66666667 0.5 0.57142857 0.71428571 0.42857143 0.75 ] mean value: 0.6847619047619048 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.6 1. 0.8 0.8 0.4 0.8 1. 0.6 0.6] mean value: 0.74 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.7 1. 0.7 0.7 0.5 0.6 0.8 0.4 0.7] mean value: 0.6900000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8 0.7 1. 0.7 0.7 0.5 0.6 0.8 0.4 0.7] mean value: 0.6900000000000002 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.5 1. 0.57142857 0.57142857 0.28571429 0.5 0.71428571 0.33333333 0.5 ] mean value: 0.5642857142857143 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 32 mean value: 32.0 key: FP value: 13 mean value: 13.0 key: FN value: 18 mean value: 18.0 key: TP value: 37 mean value: 37.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.15 Accuracy on Blind test: 0.6 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.22175002 0.20551085 0.21514559 0.19258976 0.19375014 0.21860838 0.19352555 0.22049546 0.18577838 0.19826937] mean value: 0.20454235076904298 key: score_time value: [0.00891447 0.00950909 0.00972724 0.00901461 0.00917888 0.00884056 0.00981069 0.00889659 0.01069164 0.00902009] mean value: 0.009360384941101075 key: test_mcc value: [1. 0.81649658 1. 0.40824829 0.81649658 0.6 0.81649658 0.81649658 0.40824829 0.65465367] mean value: 0.7337136575346608 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.90909091 1. 0.66666667 0.88888889 0.8 0.88888889 0.88888889 0.72727273 0.75 ] mean value: 0.8519696969696969 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.83333333 1. 0.75 1. 0.8 1. 1. 0.66666667 1. ] mean value: 0.9049999999999999 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.6 0.8 0.8 0.8 0.8 0.8 0.6] mean value: 0.8200000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.9 1. 0.7 0.9 0.8 0.9 0.9 0.7 0.8] mean value: 0.86 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.9 1. 0.7 0.9 0.8 0.9 0.9 0.7 0.8] mean value: 0.86 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.83333333 1. 0.5 0.8 0.66666667 0.8 0.8 0.57142857 0.6 ] mean value: 0.7571428571428571 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 45 mean value: 45.0 key: FP value: 9 mean value: 9.0 key: FN value: 5 mean value: 5.0 key: TP value: 41 mean value: 41.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.84 Accuracy on Blind test: 0.92 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01039934 0.0146544 0.01419163 0.01512098 0.01400018 0.01431561 0.01405144 0.01417041 0.01411915 0.01453304] mean value: 0.01395561695098877 key: score_time value: [0.01178169 0.01186323 0.01167774 0.01193404 0.01269293 0.01163626 0.014534 0.01400065 0.01272821 0.01334691] mean value: 0.012619566917419434 key: test_mcc value: [0.65465367 0.6 0.81649658 0.6 0.81649658 0.40824829 0.40824829 0.65465367 0.6 0.5 ] mean value: 0.6058797084199132 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.83333333 0.8 0.90909091 0.8 0.90909091 0.72727273 0.72727273 0.83333333 0.8 0.76923077] mean value: 0.8108624708624708 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.71428571 0.8 0.83333333 0.8 0.83333333 0.66666667 0.66666667 0.71428571 0.8 0.625 ] mean value: 0.7453571428571428 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.8 1. 0.8 1. 0.8 0.8 1. 0.8 1. ] mean value: 0.9 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.8 0.9 0.8 0.9 0.7 0.7 0.8 0.8 0.7] mean value: 0.79 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8 0.8 0.9 0.8 0.9 0.7 0.7 0.8 0.8 0.7] mean value: 0.79 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.71428571 0.66666667 0.83333333 0.66666667 0.83333333 0.57142857 0.57142857 0.71428571 0.66666667 0.625 ] mean value: 0.6863095238095238 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 34 mean value: 34.0 key: FP value: 5 mean value: 5.0 key: FN value: 16 mean value: 16.0 key: TP value: 45 mean value: 45.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.22 Accuracy on Blind test: 0.68 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.0339694 0.0281744 0.02761769 0.02563477 0.03163433 0.03103805 0.03008103 0.02972746 0.02814841 0.02832222] mean value: 0.029434776306152342 key: score_time value: [0.01380706 0.02080441 0.02085018 0.02093291 0.02080822 0.02095509 0.02182198 0.02258253 0.02093005 0.020823 ] mean value: 0.02043154239654541 key: test_mcc value: [0.81649658 0.81649658 1. 0.5 0.81649658 0.6 0.6 0.65465367 0.21821789 0.81649658] mean value: 0.6838857884654874 key: train_mcc value: [1. 1. 0.97801929 1. 1. 1. 0.97801929 1. 1. 1. ] mean value: 0.9956038587687303 key: test_fscore value: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:206: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:207: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_CV['Resampling'] = rs_smnc /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:212: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:213: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy smnc_BT['Resampling'] = rs_smnc [0.90909091 0.90909091 1. 0.76923077 0.90909091 0.8 0.8 0.83333333 0.66666667 0.88888889] mean value: 0.8485392385392385 key: train_fscore value: [1. 1. 0.98876404 1. 1. 1. 0.98876404 1. 1. 1. ] mean value: 0.997752808988764 key: test_precision value: [0.83333333 0.83333333 1. 0.625 0.83333333 0.8 0.8 0.71428571 0.57142857 1. ] mean value: 0.8010714285714287 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 0.8 0.8 1. 0.8 0.8] mean value: 0.9200000000000002 key: train_recall value: [1. 1. 0.97777778 1. 1. 1. 0.97777778 1. 1. 1. ] mean value: 0.9955555555555555 key: test_accuracy value: [0.9 0.9 1. 0.7 0.9 0.8 0.8 0.8 0.6 0.9] mean value: 0.8300000000000001 key: train_accuracy value: [1. 1. 0.98888889 1. 1. 1. 0.98888889 1. 1. 1. ] mean value: 0.9977777777777778 key: test_roc_auc value: [0.9 0.9 1. 0.7 0.9 0.8 0.8 0.8 0.6 0.9] mean value: 0.8300000000000001 key: train_roc_auc value: [1. 1. 0.98888889 1. 1. 1. 0.98888889 1. 1. 1. ] mean value: 0.9977777777777778 key: test_jcc value: [0.83333333 0.83333333 1. 0.625 0.83333333 0.66666667 0.66666667 0.71428571 0.5 0.8 ] mean value: 0.7472619047619047 key: train_jcc value: [1. 1. 0.97777778 1. 1. 1. 0.97777778 1. 1. 1. ] mean value: 0.9955555555555555 key: TN value: 37 mean value: 37.0 key: FP value: 4 mean value: 4.0 key: FN value: 13 mean value: 13.0 key: TP value: 46 mean value: 46.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.16 Accuracy on Blind test: 0.62 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.1647613 0.21787906 0.20268655 0.18552685 0.1953547 0.18074489 0.18419719 0.18565655 0.18220353 0.20094514] mean value: 0.18999557495117186 key: score_time value: [0.02277732 0.02263975 0.02119422 0.02119184 0.02137756 0.02144146 0.02110291 0.02121115 0.01738858 0.02114844] mean value: 0.021147322654724122 key: test_mcc value: [0.81649658 0.65465367 0.81649658 0.5 1. 0.40824829 0.81649658 0.65465367 0.21821789 0.81649658] mean value: 0.6701759845826715 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.90909091 0.83333333 0.90909091 0.76923077 1. 0.72727273 0.90909091 0.83333333 0.66666667 0.88888889] mean value: 0.8445998445998445 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.83333333 0.71428571 0.83333333 0.625 1. 0.66666667 0.83333333 0.71428571 0.57142857 1. ] mean value: 0.7791666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 0.8 1. 1. 0.8 0.8] mean value: 0.9400000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9 0.8 0.9 0.7 1. 0.7 0.9 0.8 0.6 0.9] mean value: 0.8200000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9 0.8 0.9 0.7 1. 0.7 0.9 0.8 0.6 0.9] mean value: 0.8200000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.83333333 0.71428571 0.83333333 0.625 1. 0.57142857 0.83333333 0.71428571 0.5 0.8 ] mean value: 0.7424999999999999 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 35 mean value: 35.0 key: FP value: 3 mean value: 3.0 key: FN value: 15 mean value: 15.0 key: TP value: 47 mean value: 47.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.05 Accuracy on Blind test: 0.57 PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02255177 0.02571154 0.02445078 0.02460313 0.02436972 0.02525306 0.02503276 0.02916312 0.02374983 0.02672315] mean value: 0.025160884857177733 key: score_time value: [0.01158285 0.01172376 0.01152539 0.01155019 0.01155806 0.01169276 0.0116291 0.01217937 0.01163387 0.01157951] mean value: 0.01166548728942871 key: test_mcc value: [1. 0.6 1. 0.65465367 0.5 0.81649658 0.6 0.40824829 0. 0.81649658] mean value: 0.6395895123027293 key: train_mcc value: [0.97801929 0.93356387 0.93356387 0.95650071 0.91201231 0.91111111 0.91201231 0.93356387 0.95555556 0.93541435] mean value: 0.936131725451825 key: test_fscore value: [1. 0.8 1. 0.83333333 0.76923077 0.88888889 0.8 0.72727273 0.54545455 0.88888889] mean value: 0.8253069153069154 key: train_fscore value: [0.98901099 0.96703297 0.96703297 0.97826087 0.95652174 0.95555556 0.95652174 0.96703297 0.97777778 0.96774194] mean value: 0.9682489506753182 key: test_precision value: [1. 0.8 1. 0.71428571 0.625 1. 0.8 0.66666667 0.5 1. ] mean value: 0.8105952380952381 key: train_precision value: [0.97826087 0.95652174 0.95652174 0.95744681 0.93617021 0.95555556 0.93617021 0.95652174 0.97777778 0.9375 ] mean value: 0.9548446654332409 key: test_recall value: [1. 0.8 1. 1. 1. 0.8 0.8 0.8 0.6 0.8] mean value: 0.86 key: train_recall value: [1. 0.97777778 0.97777778 1. 0.97777778 0.95555556 0.97777778 0.97777778 0.97777778 1. ] mean value: 0.9822222222222223 key: test_accuracy value: [1. 0.8 1. 0.8 0.7 0.9 0.8 0.7 0.5 0.9] mean value: 0.8099999999999999 key: train_accuracy value: [0.98888889 0.96666667 0.96666667 0.97777778 0.95555556 0.95555556 0.95555556 0.96666667 0.97777778 0.96666667] mean value: 0.9677777777777778 key: test_roc_auc value: [1. 0.8 1. 0.8 0.7 0.9 0.8 0.7 0.5 0.9] mean value: 0.8099999999999999 key: train_roc_auc value: [0.98888889 0.96666667 0.96666667 0.97777778 0.95555556 0.95555556 0.95555556 0.96666667 0.97777778 0.96666667] mean value: 0.9677777777777778 key: test_jcc value: [1. 0.66666667 1. 0.71428571 0.625 0.8 0.66666667 0.57142857 0.375 0.8 ] mean value: 0.7219047619047619 key: train_jcc value: [0.97826087 0.93617021 0.93617021 0.95744681 0.91666667 0.91489362 0.91666667 0.93617021 0.95652174 0.9375 ] mean value: 0.9386467005858773 key: TN value: 38 mean value: 38.0 key: FP value: 7 mean value: 7.0 key: FN value: 12 mean value: 12.0 key: TP value: 43 mean value: 43.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.37 Accuracy on Blind test: 0.72 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.54653025 0.52189708 0.73818922 0.56501412 0.52099514 0.58223414 0.6111896 0.52498031 0.51234984 0.58435845] mean value: 0.5707738161087036 key: score_time value: [0.01446366 0.01307082 0.01306009 0.0130105 0.01183724 0.01319456 0.01186347 0.01306581 0.01307225 0.01450109] mean value: 0.013113951683044434 key: test_mcc value: [1. 0.6 1. 0.5 0.65465367 0.81649658 0.81649658 0.40824829 0.5 0.81649658] mean value: 0.7112391703955019 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 0.97801929 1. 1. ] mean value: 0.9978019293843652 key: test_fscore value: [1. 0.8 1. 0.76923077 0.83333333 0.88888889 0.88888889 0.72727273 0.76923077 0.88888889] mean value: 0.8565734265734266 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 0.98901099 1. 1. ] mean value: 0.9989010989010989 key: test_precision value: [1. 0.8 1. 0.625 0.71428571 1. 1. 0.66666667 0.625 1. ] mean value: 0.843095238095238 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 0.97826087 1. 1. ] mean value: 0.9978260869565216 key: test_recall value: [1. 0.8 1. 1. 1. 0.8 0.8 0.8 1. 0.8] mean value: 0.9 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.8 1. 0.7 0.8 0.9 0.9 0.7 0.7 0.9] mean value: 0.8400000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 0.98888889 1. 1. ] mean value: 0.9988888888888889 key: test_roc_auc value: [1. 0.8 1. 0.7 0.8 0.9 0.9 0.7 0.7 0.9] mean value: 0.8400000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 0.98888889 1. 1. ] mean value: 0.9988888888888889 key: test_jcc value: [1. 0.66666667 1. 0.625 0.71428571 0.8 0.8 0.57142857 0.625 0.8 ] mean value: 0.7602380952380952 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 0.97826087 1. 1. ] mean value: 0.9978260869565216 key: TN value: 39 mean value: 39.0 key: FP value: 5 mean value: 5.0 key: FN value: 11 mean value: 11.0 key: TP value: 45 mean value: 45.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.25 Accuracy on Blind test: 0.68 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01176167 0.01163745 0.00861454 0.0102284 0.0082593 0.0084765 0.00813198 0.00795722 0.00810385 0.00791645] mean value: 0.009108734130859376 key: score_time value: [0.01180577 0.00988865 0.00878239 0.00933552 0.00824714 0.00900102 0.00843334 0.00827384 0.00830865 0.00864124] mean value: 0.009071755409240722 key: test_mcc value: [ 0. 0.40824829 0.33333333 0.21821789 0.33333333 -0.21821789 0.21821789 0. 0. 0. ] mean value: 0.1293132847366522 key: train_mcc value: [0.41781451 0.57601843 0.47133199 0.70004007 0.50917508 0.43808583 0.57906602 0.52094589 0.55610507 0.51854497] mean value: 0.528712785434277 key: test_fscore value: [0.54545455 0.72727273 0.71428571 0.66666667 0.71428571 0.5 0.66666667 0.61538462 0.61538462 0.61538462] mean value: 0.638078588078588 key: train_fscore value: [0.73786408 0.80373832 0.76363636 0.85714286 0.77777778 0.74509804 0.79569892 0.78181818 0.7826087 0.78095238] mean value: 0.7826335616353517 key: test_precision value: [0.5 0.66666667 0.55555556 0.57142857 0.55555556 0.42857143 0.57142857 0.5 0.5 0.5 ] mean value: 0.5349206349206349 key: train_precision value: [0.65517241 0.69354839 0.64615385 0.79245283 0.66666667 0.66666667 0.77083333 0.66153846 0.76595745 0.68333333] mean value: 0.7002323385579375 key: test_recall value: [0.6 0.8 1. 0.8 1. 0.6 0.8 0.8 0.8 0.8] mean value: 0.8 key: train_recall value: [0.84444444 0.95555556 0.93333333 0.93333333 0.93333333 0.84444444 0.82222222 0.95555556 0.8 0.91111111] mean value: 0.8933333333333333 key: test_accuracy value: [0.5 0.7 0.6 0.6 0.6 0.4 0.6 0.5 0.5 0.5] mean value: 0.55 key: train_accuracy value: [0.7 0.76666667 0.71111111 0.84444444 0.73333333 0.71111111 0.78888889 0.73333333 0.77777778 0.74444444] mean value: 0.7511111111111111 key: test_roc_auc value: [0.5 0.7 0.6 0.6 0.6 0.4 0.6 0.5 0.5 0.5] mean value: 0.55 key: train_roc_auc value: [0.7 0.76666667 0.71111111 0.84444444 0.73333333 0.71111111 0.78888889 0.73333333 0.77777778 0.74444444] mean value: 0.7511111111111111 key: test_jcc value: [0.375 0.57142857 0.55555556 0.5 0.55555556 0.33333333 0.5 0.44444444 0.44444444 0.44444444] mean value: 0.47242063492063496 key: train_jcc value: [0.58461538 0.671875 0.61764706 0.75 0.63636364 0.59375 0.66071429 0.64179104 0.64285714 0.640625 ] mean value: 0.6440238553150099 key: TN value: 15 mean value: 15.0 key: FP value: 10 mean value: 10.0 key: FN value: 35 mean value: 35.0 key: TP value: 40 mean value: 40.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.39 Accuracy on Blind test: 0.65 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01204419 0.00879407 0.00843763 0.00823641 0.00820732 0.00811648 0.00903416 0.0081718 0.00817847 0.00820756] mean value: 0.008742809295654297 key: score_time value: [0.01041579 0.00831723 0.0082469 0.00830102 0.00827026 0.00827599 0.00908613 0.00831842 0.00828457 0.00827765] mean value: 0.008579397201538086 key: test_mcc value: [0.81649658 0. 0.81649658 0.40824829 0.40824829 0.5 0. 0. 0. 0.21821789] mean value: 0.31677076330191706 key: train_mcc value: [0.64700558 0.62609903 0.51571581 0.57792049 0.44992127 0.60238451 0.60059347 0.60059347 0.69162666 0.55610507] mean value: 0.5867965370349755 key: test_fscore value: [0.88888889 0.54545455 0.88888889 0.72727273 0.72727273 0.57142857 0.44444444 0.54545455 0.44444444 0.5 ] mean value: 0.6283549783549783 key: train_fscore value: [0.81395349 0.82105263 0.77083333 0.78651685 0.69879518 0.79069767 0.79545455 0.80434783 0.8372093 0.7826087 ] mean value: 0.7901469531877712 key: test_precision value: [1. 0.5 1. 0.66666667 0.66666667 1. 0.5 0.5 0.5 0.66666667] mean value: 0.7 key: train_precision value: [0.85365854 0.78 0.7254902 0.79545455 0.76315789 0.82926829 0.81395349 0.78723404 0.87804878 0.76595745] mean value: 0.7992223223759711 key: test_recall value: [0.8 0.6 0.8 0.8 0.8 0.4 0.4 0.6 0.4 0.4] mean value: 0.6000000000000001 key: train_recall value: [0.77777778 0.86666667 0.82222222 0.77777778 0.64444444 0.75555556 0.77777778 0.82222222 0.8 0.8 ] mean value: 0.7844444444444444 key: test_accuracy value: [0.9 0.5 0.9 0.7 0.7 0.7 0.5 0.5 0.5 0.6] mean value: 0.65 key: train_accuracy value: [0.82222222 0.81111111 0.75555556 0.78888889 0.72222222 0.8 0.8 0.8 0.84444444 0.77777778] mean value: 0.7922222222222222 key: test_roc_auc value: [0.9 0.5 0.9 0.7 0.7 0.7 0.5 0.5 0.5 0.6] mean value: 0.65 key: train_roc_auc value: [0.82222222 0.81111111 0.75555556 0.78888889 0.72222222 0.8 0.8 0.8 0.84444444 0.77777778] mean value: 0.7922222222222223 key: test_jcc value: [0.8 0.375 0.8 0.57142857 0.57142857 0.4 0.28571429 0.375 0.28571429 0.33333333] mean value: 0.4797619047619047 key: train_jcc value: [0.68627451 0.69642857 0.62711864 0.64814815 0.53703704 0.65384615 0.66037736 0.67272727 0.72 0.64285714] mean value: 0.6544814838406611 key: TN value: 35 mean value: 35.0 key: FP value: 20 mean value: 20.0 key: FN value: 15 mean value: 15.0 key: TP value: 30 mean value: 30.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.06 Accuracy on Blind test: 0.5 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.0103817 0.00782585 0.00789905 0.0080235 0.00852895 0.00891089 0.00892091 0.00831962 0.00858092 0.00874972] mean value: 0.008614110946655273 key: score_time value: [0.00999832 0.00927591 0.01990318 0.01393294 0.01388645 0.01472855 0.00999117 0.00985885 0.00980926 0.01003838] mean value: 0.012142300605773926 key: test_mcc value: [ 0.40824829 0.21821789 0. 0.40824829 0.81649658 -0.2 0.21821789 0. 0. 0.40824829] mean value: 0.22776772327912997 key: train_mcc value: [0.53452248 0.53990552 0.43808583 0.51314236 0.56980288 0.60971232 0.58137767 0.49897013 0.60540551 0.50418417] mean value: 0.5395108888868825 key: test_fscore value: [0.72727273 0.5 0.61538462 0.72727273 0.90909091 0.4 0.66666667 0.61538462 0.61538462 0.72727273] mean value: 0.6503729603729604 key: train_fscore value: [0.77419355 0.78350515 0.74509804 0.76595745 0.8 0.81632653 0.8 0.76767677 0.8125 0.77227723] mean value: 0.7837534715062253 key: test_precision value: [0.66666667 0.66666667 0.5 0.66666667 0.83333333 0.4 0.57142857 0.5 0.5 0.66666667] mean value: 0.5971428571428572 key: train_precision value: [0.75 0.73076923 0.66666667 0.73469388 0.72727273 0.75471698 0.76 0.7037037 0.76470588 0.69642857] mean value: 0.7288957640876937 key: test_recall value: [0.8 0.4 0.8 0.8 1. 0.4 0.8 0.8 0.8 0.8] mean value: 0.74 key: train_recall value: [0.8 0.84444444 0.84444444 0.8 0.88888889 0.88888889 0.84444444 0.84444444 0.86666667 0.86666667] mean value: 0.8488888888888889 key: test_accuracy value: [0.7 0.6 0.5 0.7 0.9 0.4 0.6 0.5 0.5 0.7] mean value: 0.6100000000000001 key: train_accuracy value: [0.76666667 0.76666667 0.71111111 0.75555556 0.77777778 0.8 0.78888889 0.74444444 0.8 0.74444444] mean value: 0.7655555555555555 key: test_roc_auc value: [0.7 0.6 0.5 0.7 0.9 0.4 0.6 0.5 0.5 0.7] mean value: 0.6100000000000001 key: train_roc_auc value: [0.76666667 0.76666667 0.71111111 0.75555556 0.77777778 0.8 0.78888889 0.74444444 0.8 0.74444444] mean value: 0.7655555555555555 key: test_jcc value: [0.57142857 0.33333333 0.44444444 0.57142857 0.83333333 0.25 0.5 0.44444444 0.44444444 0.57142857] mean value: 0.49642857142857133 key: train_jcc value: [0.63157895 0.6440678 0.59375 0.62068966 0.66666667 0.68965517 0.66666667 0.62295082 0.68421053 0.62903226] mean value: 0.6449268508950567 key: TN value: 24 mean value: 24.0 key: FP value: 13 mean value: 13.0 key: FN value: 26 mean value: 26.0 key: TP value: 37 mean value: 37.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.07 Accuracy on Blind test: 0.48 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.00865793 0.00888205 0.00870848 0.00853825 0.0088017 0.00992823 0.00926638 0.00858331 0.00977707 0.00882268] mean value: 0.00899660587310791 key: score_time value: [0.00861406 0.00844789 0.00833035 0.008533 0.00839972 0.00914431 0.00835848 0.00843406 0.00843239 0.00836515] mean value: 0.008505940437316895 key: test_mcc value: [0.65465367 0.40824829 0.6 0.6 0.81649658 0.21821789 0.40824829 0.21821789 0. 0.65465367] mean value: 0.4578736283743391 key: train_mcc value: [0.76486616 0.75574218 0.77777778 0.76026311 0.73405869 0.78478493 0.77777778 0.84632727 0.80498447 0.78086881] mean value: 0.7787451171317092 key: test_fscore value: [0.75 0.66666667 0.8 0.8 0.90909091 0.5 0.72727273 0.66666667 0.54545455 0.75 ] mean value: 0.7115151515151515 key: train_fscore value: [0.86746988 0.87640449 0.88888889 0.87058824 0.86363636 0.88095238 0.88888889 0.91954023 0.89411765 0.88372093] mean value: 0.8834207938737174 key: test_precision value: [1. 0.75 0.8 0.8 0.83333333 0.66666667 0.66666667 0.57142857 0.5 1. ] mean value: 0.7588095238095238 key: train_precision value: [0.94736842 0.88636364 0.88888889 0.925 0.88372093 0.94871795 0.88888889 0.95238095 0.95 0.92682927] mean value: 0.9198158934818188 key: test_recall value: [0.6 0.6 0.8 0.8 1. 0.4 0.8 0.8 0.6 0.6] mean value: 0.7 key: train_recall value: [0.8 0.86666667 0.88888889 0.82222222 0.84444444 0.82222222 0.88888889 0.88888889 0.84444444 0.84444444] mean value: 0.8511111111111112 key: test_accuracy value: [0.8 0.7 0.8 0.8 0.9 0.6 0.7 0.6 0.5 0.8] mean value: 0.72 key: train_accuracy value: [0.87777778 0.87777778 0.88888889 0.87777778 0.86666667 0.88888889 0.88888889 0.92222222 0.9 0.88888889] mean value: 0.8877777777777778 key: test_roc_auc value: [0.8 0.7 0.8 0.8 0.9 0.6 0.7 0.6 0.5 0.8] mean value: 0.72 key: train_roc_auc value: [0.87777778 0.87777778 0.88888889 0.87777778 0.86666667 0.88888889 0.88888889 0.92222222 0.9 0.88888889] mean value: 0.8877777777777778 key: test_jcc value: [0.6 0.5 0.66666667 0.66666667 0.83333333 0.33333333 0.57142857 0.5 0.375 0.6 ] mean value: 0.5646428571428571 key: train_jcc value: [0.76595745 0.78 0.8 0.77083333 0.76 0.78723404 0.8 0.85106383 0.80851064 0.79166667] mean value: 0.791526595744681 key: TN value: 37 mean value: 37.0 key: FP value: 15 mean value: 15.0 key: FN value: 13 mean value: 13.0 key: TP value: 35 mean value: 35.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.04 Accuracy on Blind test: 0.62 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.35339808 0.34032393 0.34420347 0.40963769 0.37293458 0.36176634 0.34549356 0.38289881 0.32440186 0.49558091] mean value: 0.37306392192840576 key: score_time value: [0.01189828 0.01193237 0.01191092 0.012012 0.01190543 0.01191354 0.0119226 0.01200247 0.01195073 0.01197505] mean value: 0.011942338943481446 key: test_mcc value: [1. 0.6 1. 0.5 0.65465367 0.40824829 0.40824829 0.5 0.65465367 0.81649658] mean value: 0.6542300503271407 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.8 1. 0.76923077 0.83333333 0.66666667 0.72727273 0.76923077 0.83333333 0.88888889] mean value: 0.8287956487956487 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.8 1. 0.625 0.71428571 0.75 0.66666667 0.625 0.71428571 1. ] mean value: 0.7895238095238095 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.8 1. 1. 1. 0.6 0.8 1. 1. 0.8] mean value: 0.9 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.8 1. 0.7 0.8 0.7 0.7 0.7 0.8 0.9] mean value: 0.8099999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.8 1. 0.7 0.8 0.7 0.7 0.7 0.8 0.9] mean value: 0.8099999999999999 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.66666667 1. 0.625 0.71428571 0.5 0.57142857 0.625 0.71428571 0.8 ] mean value: 0.7216666666666667 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 36 mean value: 36.0 key: FP value: 5 mean value: 5.0 key: FN value: 14 mean value: 14.0 key: TP value: 45 mean value: 45.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.12 Accuracy on Blind test: 0.6 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01333547 0.01328611 0.0099473 0.00956702 0.0093472 0.00933528 0.00904536 0.00918818 0.00934958 0.00936794] mean value: 0.010176944732666015 key: score_time value: [0.01137042 0.01056337 0.00862908 0.00830317 0.00825858 0.00827336 0.00822067 0.00820446 0.00816536 0.00829554] mean value: 0.008828401565551758 key: test_mcc value: [0.81649658 0.81649658 1. 0.65465367 0.65465367 0.6 0.81649658 0.40824829 0.81649658 0.81649658] mean value: 0.7400038536518447 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.90909091 0.90909091 1. 0.83333333 0.83333333 0.8 0.88888889 0.72727273 0.90909091 0.88888889] mean value: 0.8698989898989898 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.83333333 0.83333333 1. 0.71428571 0.71428571 0.8 1. 0.66666667 0.83333333 1. ] mean value: 0.8395238095238096 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 0.8 0.8 0.8 1. 0.8] mean value: 0.9200000000000002 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9 0.9 1. 0.8 0.8 0.8 0.9 0.7 0.9 0.9] mean value: 0.8600000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9 0.9 1. 0.8 0.8 0.8 0.9 0.7 0.9 0.9] mean value: 0.8600000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.83333333 0.83333333 1. 0.71428571 0.71428571 0.66666667 0.8 0.57142857 0.83333333 0.8 ] mean value: 0.7766666666666666 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 40 mean value: 40.0 key: FP value: 4 mean value: 4.0 key: FN value: 10 mean value: 10.0 key: TP value: 46 mean value: 46.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.89 Accuracy on Blind test: 0.95 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.08124042 0.07976699 0.08055592 0.08180213 0.0852704 0.08435702 0.0828104 0.08187795 0.08418012 0.08399606] mean value: 0.0825857400894165 key: score_time value: [0.01652694 0.01657915 0.01681757 0.01673293 0.0175488 0.01702619 0.0173912 0.01703215 0.01830769 0.01708388] mean value: 0.01710464954376221 key: test_mcc value: [1. 0.40824829 0.81649658 0.65465367 0.81649658 0.5 0.40824829 0.40824829 0.65465367 0.65465367] mean value: 0.6321699045370972 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.66666667 0.88888889 0.83333333 0.90909091 0.57142857 0.72727273 0.72727273 0.83333333 0.75 ] mean value: 0.7907287157287157 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.75 1. 0.71428571 0.83333333 1. 0.66666667 0.66666667 0.71428571 1. ] mean value: 0.8345238095238094 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.6 0.8 1. 1. 0.4 0.8 0.8 1. 0.6] mean value: 0.8 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.7 0.9 0.8 0.9 0.7 0.7 0.7 0.8 0.8] mean value: 0.8 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.7 0.9 0.8 0.9 0.7 0.7 0.7 0.8 0.8] mean value: 0.8 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.5 0.8 0.71428571 0.83333333 0.4 0.57142857 0.57142857 0.71428571 0.6 ] mean value: 0.6704761904761904 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 40 mean value: 40.0 key: FP value: 10 mean value: 10.0 key: FN value: 10 mean value: 10.0 key: TP value: 40 mean value: 40.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.49 Accuracy on Blind test: 0.78 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.0081377 0.00820899 0.00818825 0.00807595 0.00807285 0.00856829 0.00839448 0.00841856 0.00827169 0.00830626] mean value: 0.008264303207397461 key: score_time value: [0.00828624 0.00839925 0.00824952 0.00820351 0.00828624 0.00887203 0.00863862 0.00841212 0.00832582 0.00837159] mean value: 0.00840449333190918 key: test_mcc value: [ 1. 0.40824829 0.81649658 0.65465367 0.81649658 -0.5 0.40824829 0.21821789 0.81649658 0.65465367] mean value: 0.5293511555362851 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.72727273 0.88888889 0.83333333 0.90909091 0.46153846 0.72727273 0.66666667 0.90909091 0.75 ] mean value: 0.7873154623154623 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.66666667 1. 0.71428571 0.83333333 0.375 0.66666667 0.57142857 0.83333333 1. ] mean value: 0.7660714285714285 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.8 0.8 1. 1. 0.6 0.8 0.8 1. 0.6] mean value: 0.8400000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.7 0.9 0.8 0.9 0.3 0.7 0.6 0.9 0.8] mean value: 0.76 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.7 0.9 0.8 0.9 0.3 0.7 0.6 0.9 0.8] mean value: 0.76 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.57142857 0.8 0.71428571 0.83333333 0.3 0.57142857 0.5 0.83333333 0.6 ] mean value: 0.6723809523809523 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 34 mean value: 34.0 key: FP value: 8 mean value: 8.0 key: FN value: 16 mean value: 16.0 key: TP value: 42 mean value: 42.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.56 Accuracy on Blind test: 0.8 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.0857687 1.02543187 1.06359982 1.03614068 1.06371474 1.02588892 1.02457833 1.05441475 1.06075287 1.04914474] mean value: 1.048943543434143 key: score_time value: [0.09435582 0.09166527 0.087533 0.09539175 0.08773398 0.0948875 0.094208 0.09529018 0.09117222 0.09602284] mean value: 0.09282605648040772 key: test_mcc value: [1. 0.81649658 1. 0.81649658 1. 0.65465367 0.81649658 0.40824829 0.65465367 0.65465367] mean value: 0.7821699045370971 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.88888889 1. 0.90909091 1. 0.75 0.88888889 0.72727273 0.83333333 0.75 ] mean value: 0.8747474747474746 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 0.83333333 1. 1. 1. 0.66666667 0.71428571 1. ] mean value: 0.9214285714285714 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.8 1. 1. 1. 0.6 0.8 0.8 1. 0.6] mean value: 0.86 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.9 1. 0.9 1. 0.8 0.9 0.7 0.8 0.8] mean value: 0.8800000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.9 1. 0.9 1. 0.8 0.9 0.7 0.8 0.8] mean value: 0.8800000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.8 1. 0.83333333 1. 0.6 0.8 0.57142857 0.71428571 0.6 ] mean value: 0.7919047619047619 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 45 mean value: 45.0 key: FP value: 7 mean value: 7.0 key: FN value: 5 mean value: 5.0 key: TP value: 43 mean value: 43.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.55 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Accuracy on Blind test: 0.8 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.89968204 0.85457993 0.8354466 0.8666656 0.85017776 0.82677722 0.88512731 0.85213256 0.84619737 0.91214228] mean value: 0.8628928661346436 key: score_time value: [0.13997507 0.14476967 0.14723778 0.16803837 0.16983438 0.17666507 0.17594314 0.1609211 0.17945981 0.17022514] mean value: 0.16330695152282715 key: test_mcc value: [0.81649658 0.81649658 0.81649658 0.81649658 1. 0.81649658 0.6 0.40824829 0.81649658 0.65465367] mean value: 0.7561881446738197 key: train_mcc value: [0.95555556 0.95555556 0.93356387 0.95555556 0.95650071 0.95650071 0.95650071 0.93356387 1. 0.97801929] mean value: 0.9581315847088575 key: test_fscore value: [0.90909091 0.88888889 0.88888889 0.90909091 1. 0.88888889 0.8 0.72727273 0.90909091 0.75 ] mean value: 0.867121212121212 key: train_fscore value: [0.97777778 0.97777778 0.96629213 0.97777778 0.97826087 0.97727273 0.97826087 0.96629213 1. 0.98876404] mean value: 0.9788476114343239 key: test_precision value: [0.83333333 1. 1. 0.83333333 1. 1. 0.8 0.66666667 0.83333333 1. ] mean value: 0.8966666666666667 key: train_precision value: [0.97777778 0.97777778 0.97727273 0.97777778 0.95744681 1. 0.95744681 0.97727273 1. 1. ] mean value: 0.9802772404900064 key: test_recall value: [1. 0.8 0.8 1. 1. 0.8 0.8 0.8 1. 0.6] mean value: 0.86 key: train_recall value: [0.97777778 0.97777778 0.95555556 0.97777778 1. 0.95555556 1. 0.95555556 1. 0.97777778] mean value: 0.9777777777777779 key: test_accuracy value: [0.9 0.9 0.9 0.9 1. 0.9 0.8 0.7 0.9 0.8] mean value: 0.8700000000000001 key: train_accuracy value: [0.97777778 0.97777778 0.96666667 0.97777778 0.97777778 0.97777778 0.97777778 0.96666667 1. 0.98888889] mean value: 0.9788888888888889 key: test_roc_auc value: [0.9 0.9 0.9 0.9 1. 0.9 0.8 0.7 0.9 0.8] mean value: 0.8700000000000001 key: train_roc_auc value: [0.97777778 0.97777778 0.96666667 0.97777778 0.97777778 0.97777778 0.97777778 0.96666667 1. 0.98888889] mean value: 0.9788888888888889 key: test_jcc value: [0.83333333 0.8 0.8 0.83333333 1. 0.8 0.66666667 0.57142857 0.83333333 0.6 ] mean value: 0.7738095238095237 key: train_jcc value: [0.95652174 0.95652174 0.93478261 0.95652174 0.95744681 0.95555556 0.95744681 0.93478261 1. 0.97777778] mean value: 0.9587357385137218 key: TN value: 44 mean value: 44.0 key: FP value: 7 mean value: 7.0 key: FN value: 6 mean value: 6.0 key: TP value: 43 mean value: 43.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.61 Accuracy on Blind test: 0.82 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.04202104 0.08573484 0.03951287 0.03615594 0.03516817 0.03643227 0.03565812 0.03513455 0.03435874 0.03614831] mean value: 0.04163248538970947 key: score_time value: [0.01022768 0.01082444 0.01064587 0.01083302 0.0103128 0.01038003 0.01106954 0.01054955 0.01016593 0.01010156] mean value: 0.010511040687561035 key: test_mcc value: [1. 0.81649658 1. 1. 1. 0.6 0.81649658 0.6 0.5 1. ] mean value: 0.8332993161855452 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.90909091 1. 1. 1. 0.8 0.88888889 0.8 0.76923077 1. ] mean value: 0.9167210567210569 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.83333333 1. 1. 1. 0.8 1. 0.8 0.625 1. ] mean value: 0.9058333333333334 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 0.8 0.8 0.8 1. 1. ] mean value: 0.9400000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.9 1. 1. 1. 0.8 0.9 0.8 0.7 1. ] mean value: 0.9099999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.9 1. 1. 1. 0.8 0.9 0.8 0.7 1. ] mean value: 0.9099999999999999 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.83333333 1. 1. 1. 0.66666667 0.8 0.66666667 0.625 1. ] mean value: 0.8591666666666666 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 44 mean value: 44.0 key: FP value: 3 mean value: 3.0 key: FN value: 6 mean value: 6.0 key: TP value: 47 mean value: 47.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.89 Accuracy on Blind test: 0.95 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02282596 0.03948879 0.03882122 0.04212666 0.0416882 0.04012561 0.04158378 0.0415206 0.04133034 0.04192233] mean value: 0.03914334774017334 key: score_time value: [0.02050471 0.02193856 0.02390456 0.02119875 0.02115989 0.02110076 0.01587558 0.02117825 0.02245665 0.020365 ] mean value: 0.02096827030181885 key: test_mcc value: [0.81649658 0.21821789 1. 0.65465367 0.81649658 0. 0.6 0.65465367 0.5 0.81649658] mean value: 0.6077014974435124 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.90909091 0.66666667 1. 0.83333333 0.90909091 0.61538462 0.8 0.83333333 0.76923077 0.88888889] mean value: 0.8225019425019425 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.83333333 0.57142857 1. 0.71428571 0.83333333 0.5 0.8 0.71428571 0.625 1. ] mean value: 0.7591666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.8 1. 1. 1. 0.8 0.8 1. 1. 0.8] mean value: 0.9200000000000002 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9 0.6 1. 0.8 0.9 0.5 0.8 0.8 0.7 0.9] mean value: 0.79 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9 0.6 1. 0.8 0.9 0.5 0.8 0.8 0.7 0.9] mean value: 0.79 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.83333333 0.5 1. 0.71428571 0.83333333 0.44444444 0.66666667 0.71428571 0.625 0.8 ] mean value: 0.7131349206349207 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 33 mean value: 33.0 key: FP value: 4 mean value: 4.0 key: FN value: 17 mean value: 17.0 key: TP value: 46 mean value: 46.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.5 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.02296448 0.0084753 0.00825715 0.00825763 0.00829768 0.00805593 0.00811934 0.00863338 0.00841451 0.00900841] mean value: 0.009848380088806152 key: score_time value: [0.01073384 0.00855923 0.00831366 0.00836492 0.00844288 0.00820661 0.00821209 0.00819874 0.00902271 0.00844789] mean value: 0.008650255203247071 key: test_mcc value: [ 0. 0.40824829 0.6 0.2 0.40824829 0. -0.2 0.2 -0.2 0.21821789] mean value: 0.16347144711637185 key: train_mcc value: [0.4000988 0.38118125 0.40492914 0.4949134 0.40249224 0.48900965 0.44992127 0.40249224 0.33366304 0.40249224] mean value: 0.41611932587025435 key: test_fscore value: [0.54545455 0.66666667 0.8 0.6 0.66666667 0.54545455 0.4 0.6 0.4 0.5 ] mean value: 0.5724242424242425 key: train_fscore value: [0.69662921 0.66666667 0.6746988 0.72289157 0.68235294 0.74725275 0.69879518 0.68235294 0.65909091 0.68235294] mean value: 0.6913083902191556 key: test_precision value: [0.5 0.75 0.8 0.6 0.75 0.5 0.4 0.6 0.4 0.66666667] mean value: 0.5966666666666668 key: train_precision value: [0.70454545 0.71794872 0.73684211 0.78947368 0.725 0.73913043 0.76315789 0.725 0.6744186 0.725 ] mean value: 0.730051689613847 key: test_recall value: [0.6 0.6 0.8 0.6 0.6 0.6 0.4 0.6 0.4 0.4] mean value: 0.56 key: train_recall value: [0.68888889 0.62222222 0.62222222 0.66666667 0.64444444 0.75555556 0.64444444 0.64444444 0.64444444 0.64444444] mean value: 0.6577777777777778 key: test_accuracy value: [0.5 0.7 0.8 0.6 0.7 0.5 0.4 0.6 0.4 0.6] mean value: 0.58 key: train_accuracy value: [0.7 0.68888889 0.7 0.74444444 0.7 0.74444444 0.72222222 0.7 0.66666667 0.7 ] mean value: 0.7066666666666667 key: test_roc_auc value: [0.5 0.7 0.8 0.6 0.7 0.5 0.4 0.6 0.4 0.6] mean value: 0.5800000000000001 key: train_roc_auc value: [0.7 0.68888889 0.7 0.74444444 0.7 0.74444444 0.72222222 0.7 0.66666667 0.7 ] mean value: 0.7066666666666667 key: test_jcc value: [0.375 0.5 0.66666667 0.42857143 0.5 0.375 0.25 0.42857143 0.25 0.33333333] mean value: 0.4107142857142857 key: train_jcc value: [0.53448276 0.5 0.50909091 0.56603774 0.51785714 0.59649123 0.53703704 0.51785714 0.49152542 0.51785714] mean value: 0.528823652096811 key: TN value: 30 mean value: 30.0 key: FP value: 22 mean value: 22.0 key: FN value: 20 mean value: 20.0 key: TP value: 28 mean value: 28.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.14 Accuracy on Blind test: 0.48 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01120234 0.01247239 0.01248765 0.01223207 0.01297379 0.01218271 0.01372743 0.01458359 0.01382494 0.01353407] mean value: 0.012922096252441406 key: score_time value: [0.00857997 0.01098967 0.01123166 0.01120615 0.01122999 0.01121783 0.01131988 0.01128626 0.01141262 0.01127839] mean value: 0.010975241661071777 key: test_mcc value: [1. 0.81649658 0.65465367 0.40824829 0.33333333 0.81649658 0.65465367 0.5 0.2 0.65465367] mean value: 0.6038535797776581 key: train_mcc value: [0.97801929 0.83553169 0.77919372 0.89087081 0.89442719 0.93541435 0.95650071 0.95650071 0.97801929 0.77919372] mean value: 0.8983671497421686 key: test_fscore value: [1. 0.88888889 0.83333333 0.72727273 0.71428571 0.90909091 0.83333333 0.76923077 0.6 0.75 ] mean value: 0.8025435675435675 key: train_fscore value: [0.98901099 0.90243902 0.89108911 0.94623656 0.94736842 0.96774194 0.97826087 0.97826087 0.98876404 0.86075949] mean value: 0.9449931315733553 key: test_precision value: [1. 1. 0.71428571 0.66666667 0.55555556 0.83333333 0.71428571 0.625 0.6 1. ] mean value: 0.7709126984126984 key: train_precision value: [0.97826087 1. 0.80357143 0.91666667 0.9 0.9375 0.95744681 0.95744681 1. 1. ] mean value: 0.945089258182459 key: test_recall value: [1. 0.8 1. 0.8 1. 1. 1. 1. 0.6 0.6] mean value: 0.8799999999999999 key: train_recall value: [1. 0.82222222 1. 0.97777778 1. 1. 1. 1. 0.97777778 0.75555556] mean value: 0.9533333333333331 key: test_accuracy value: [1. 0.9 0.8 0.7 0.6 0.9 0.8 0.7 0.6 0.8] mean value: 0.78 key: train_accuracy value: [0.98888889 0.91111111 0.87777778 0.94444444 0.94444444 0.96666667 0.97777778 0.97777778 0.98888889 0.87777778] mean value: 0.9455555555555556 key: test_roc_auc value: [1. 0.9 0.8 0.7 0.6 0.9 0.8 0.7 0.6 0.8] mean value: 0.78 key: train_roc_auc value: [0.98888889 0.91111111 0.87777778 0.94444444 0.94444444 0.96666667 0.97777778 0.97777778 0.98888889 0.87777778] mean value: 0.9455555555555556 key: test_jcc value: [1. 0.8 0.71428571 0.57142857 0.55555556 0.83333333 0.71428571 0.625 0.42857143 0.6 ] mean value: 0.6842460317460317 key: train_jcc value: [0.97826087 0.82222222 0.80357143 0.89795918 0.9 0.9375 0.95744681 0.95744681 0.97777778 0.75555556] mean value: 0.8987740654386946 key: TN value: 34 mean value: 34.0 key: FP value: 6 mean value: 6.0 key: FN value: 16 mean value: 16.0 key: TP value: 44 mean value: 44.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.27 Accuracy on Blind test: 0.68 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.00876498 0.01228714 0.01203871 0.01212764 0.01211023 0.01215172 0.01181269 0.01210976 0.01228261 0.01225233] mean value: 0.011793780326843261 key: score_time value: [0.00829244 0.01176238 0.01127028 0.01124716 0.01124883 0.01142573 0.01126313 0.01129913 0.01135111 0.01130509] mean value: 0.011046528816223145 key: test_mcc value: [0.81649658 0.33333333 0.81649658 0.65465367 0.33333333 0.65465367 0.33333333 0.21821789 0.5 0.6 ] mean value: 0.5260518393507398 key: train_mcc value: [0.91111111 0.42919754 0.82548988 0.95555556 0.93541435 0.91473203 0.4108907 0.83553169 1. 1. ] mean value: 0.8217922862521331 key: test_fscore value: [0.88888889 0.71428571 0.88888889 0.83333333 0.71428571 0.83333333 0.33333333 0.66666667 0.76923077 0.8 ] mean value: 0.7442246642246643 key: train_fscore value: [0.95555556 0.74380165 0.90697674 0.97777778 0.96774194 0.95744681 0.44827586 0.91836735 1. 1. ] mean value: 0.8875943683414192 key: test_precision value: [1. 0.55555556 1. 0.71428571 0.55555556 0.71428571 1. 0.57142857 0.625 0.8 ] mean value: 0.753611111111111 key: train_precision value: [0.95555556 0.59210526 0.95121951 0.97777778 0.9375 0.91836735 1. 0.8490566 1. 1. ] mean value: 0.9181582059398711 key: test_recall value: [0.8 1. 0.8 1. 1. 1. 0.2 0.8 1. 0.8] mean value: 0.8400000000000001 key: train_recall value: [0.95555556 1. 0.86666667 0.97777778 1. 1. 0.28888889 1. 1. 1. ] mean value: 0.9088888888888889 key: test_accuracy value: [0.9 0.6 0.9 0.8 0.6 0.8 0.6 0.6 0.7 0.8] mean value: 0.73 key: train_accuracy value: [0.95555556 0.65555556 0.91111111 0.97777778 0.96666667 0.95555556 0.64444444 0.91111111 1. 1. ] mean value: 0.8977777777777778 key: test_roc_auc value: [0.9 0.6 0.9 0.8 0.6 0.8 0.6 0.6 0.7 0.8] mean value: 0.7300000000000001 key: train_roc_auc value: [0.95555556 0.65555556 0.91111111 0.97777778 0.96666667 0.95555556 0.64444444 0.91111111 1. 1. ] mean value: 0.8977777777777778 key: test_jcc value: [0.8 0.55555556 0.8 0.71428571 0.55555556 0.71428571 0.2 0.5 0.625 0.66666667] mean value: 0.6131349206349206 key: train_jcc value: [0.91489362 0.59210526 0.82978723 0.95652174 0.9375 0.91836735 0.28888889 0.8490566 1. 1. ] mean value: 0.8287120692953408 key: TN value: 31 mean value: 31.0 key: FP value: 8 mean value: 8.0 key: FN value: 19 mean value: 19.0 key: TP value: 42 mean value: 42.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.51 Accuracy on Blind test: 0.78 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.07988095 0.07883573 0.07912493 0.07934546 0.07975411 0.07876873 0.07892227 0.0793283 0.07942939 0.07979774] mean value: 0.07931876182556152 key: score_time value: [0.01414919 0.01410151 0.01419544 0.01427197 0.01422954 0.01440668 0.01421189 0.01422381 0.01437879 0.01422191] mean value: 0.014239072799682617 key: test_mcc value: [0.65465367 0.81649658 1. 0.81649658 1. 0.81649658 0.81649658 0.65465367 0.65465367 1. ] mean value: 0.8229947335834836 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.83333333 0.90909091 1. 0.90909091 1. 0.88888889 0.88888889 0.83333333 0.83333333 1. ] mean value: 0.9095959595959597 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.71428571 0.83333333 1. 0.83333333 1. 1. 1. 0.71428571 0.71428571 1. ] mean value: 0.880952380952381 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 0.8 0.8 1. 1. 1. ] mean value: 0.96 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.9 1. 0.9 1. 0.9 0.9 0.8 0.8 1. ] mean value: 0.9 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8 0.9 1. 0.9 1. 0.9 0.9 0.8 0.8 1. ] mean value: 0.9 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.71428571 0.83333333 1. 0.83333333 1. 0.8 0.8 0.71428571 0.71428571 1. ] mean value: 0.840952380952381 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 42 mean value: 42.0 key: FP value: 2 mean value: 2.0 key: FN value: 8 mean value: 8.0 key: TP value: 48 mean value: 48.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.89 Accuracy on Blind test: 0.95 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.02679038 0.02585912 0.02684498 0.03183603 0.04586864 0.03085876 0.03211308 0.03402162 0.03574443 0.03347778] mean value: 0.03234148025512695 key: score_time value: [0.02189565 0.01761818 0.02150726 0.0278151 0.02133465 0.02162409 0.02341056 0.02291417 0.03275824 0.01854229] mean value: 0.022942018508911134 key: test_mcc value: [0.81649658 0.81649658 1. 1. 1. 0.6 0.81649658 0.65465367 0.81649658 1. ] mean value: 0.8520639994418883 key: train_mcc value: [0.97801929 1. 0.97801929 1. 1. 1. 0.97801929 1. 1. 1. ] mean value: 0.9934057881530954 key: test_fscore value: [0.88888889 0.90909091 1. 1. 1. 0.8 0.88888889 0.83333333 0.90909091 1. ] mean value: 0.9229292929292929 key: train_fscore value: [0.98876404 1. 0.98876404 1. 1. 1. 0.98901099 1. 1. 1. ] mean value: 0.9966539078898629 key: test_precision value: [1. 0.83333333 1. 1. 1. 0.8 1. 0.71428571 0.83333333 1. ] mean value: 0.9180952380952382 key: train_precision value: [1. 1. 1. 1. 1. 1. 0.97826087 1. 1. 1. ] mean value: 0.9978260869565216 key: test_recall value: [0.8 1. 1. 1. 1. 0.8 0.8 1. 1. 1. ] mean value: 0.9400000000000001 key: train_recall value: [0.97777778 1. 0.97777778 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9955555555555555 key: test_accuracy value: [0.9 0.9 1. 1. 1. 0.8 0.9 0.8 0.9 1. ] mean value: 0.9199999999999999 key: train_accuracy value: [0.98888889 1. 0.98888889 1. 1. 1. 0.98888889 1. 1. 1. ] mean value: 0.9966666666666667 key: test_roc_auc value: [0.9 0.9 1. 1. 1. 0.8 0.9 0.8 0.9 1. ] mean value: 0.9199999999999999 key: train_roc_auc value: [0.98888889 1. 0.98888889 1. 1. 1. 0.98888889 1. 1. 1. ] mean value: 0.9966666666666667 key: test_jcc value: [0.8 0.83333333 1. 1. 1. 0.66666667 0.8 0.71428571 0.83333333 1. ] mean value: 0.8647619047619047 key: train_jcc value: [0.97777778 1. 0.97777778 1. 1. 1. 0.97826087 1. 1. 1. ] mean value: 0.9933816425120773 key: TN value: 45 mean value: 45.0 key: FP value: 3 mean value: 3.0 key: FN value: 5 mean value: 5.0 key: TP value: 47 mean value: 47.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.95 Accuracy on Blind test: 0.98 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.01471233 0.01499677 0.01495695 0.01577234 0.01556015 0.01557922 0.01554871 0.01555872 0.01553726 0.01553893] mean value: 0.015376138687133788 key: score_time value: [0.01128864 0.01108527 0.0116055 0.01157355 0.01152849 0.01179886 0.01158834 0.01157236 0.0115447 0.01676178] mean value: 0.012034749984741211 key: test_mcc value: [0.81649658 0.40824829 1. 0.65465367 0.65465367 0.21821789 0.21821789 0.65465367 0.5 0.65465367] mean value: 0.5779795334695482 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.90909091 0.66666667 1. 0.83333333 0.83333333 0.5 0.66666667 0.83333333 0.76923077 0.75 ] mean value: 0.7761655011655011 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.83333333 0.75 1. 0.71428571 0.71428571 0.66666667 0.57142857 0.71428571 0.625 1. ] mean value: 0.7589285714285714 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.6 1. 1. 1. 0.4 0.8 1. 1. 0.6] mean value: 0.8400000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9 0.7 1. 0.8 0.8 0.6 0.6 0.8 0.7 0.8] mean value: 0.77 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9 0.7 1. 0.8 0.8 0.6 0.6 0.8 0.7 0.8] mean value: 0.77 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.83333333 0.5 1. 0.71428571 0.71428571 0.33333333 0.5 0.71428571 0.625 0.6 ] mean value: 0.653452380952381 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 35 mean value: 35.0 key: FP value: 8 mean value: 8.0 key: FN value: 15 mean value: 15.0 key: TP value: 42 mean value: 42.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.03 Accuracy on Blind test: 0.52 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.21461725 0.18806386 0.1732049 0.16499853 0.17371368 0.16821599 0.14020443 0.19140482 0.18640518 0.16943955] mean value: 0.1770268201828003 key: score_time value: [0.00959754 0.00923371 0.00887346 0.00884986 0.00876808 0.00874138 0.00899005 0.00879264 0.00887346 0.00937819] mean value: 0.009009838104248047 key: test_mcc value: [1. 0.81649658 1. 0.81649658 1. 0.81649658 0.81649658 1. 0.65465367 1. ] mean value: 0.8920639994418881 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.90909091 1. 0.90909091 1. 0.88888889 0.88888889 1. 0.83333333 1. ] mean value: 0.942929292929293 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.83333333 1. 0.83333333 1. 1. 1. 1. 0.71428571 1. ] mean value: 0.9380952380952381 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 0.8 0.8 1. 1. 1. ] mean value: 0.96 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.9 1. 0.9 1. 0.9 0.9 1. 0.8 1. ] mean value: 0.9400000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.9 1. 0.9 1. 0.9 0.9 1. 0.8 1. ] mean value: 0.9400000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.83333333 1. 0.83333333 1. 0.8 0.8 1. 0.71428571 1. ] mean value: 0.8980952380952381 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 46 mean value: 46.0 key: FP value: 2 mean value: 2.0 key: FN value: 4 mean value: 4.0 key: TP value: 48 mean value: 48.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.95 Accuracy on Blind test: 0.98 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01019168 0.01433969 0.01385474 0.01470232 0.0138483 0.01425314 0.01388431 0.01397109 0.0143342 0.01450634] mean value: 0.013788580894470215 key: score_time value: [0.01203132 0.01153278 0.0118506 0.01162958 0.01282692 0.01163149 0.01344275 0.01311135 0.01172233 0.01355076] mean value: 0.012332987785339356 key: test_mcc value: [1. 0.65465367 0.65465367 1. 1. 0.5 0.65465367 0.65465367 1. 0.65465367] mean value: 0.7773268353539886 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [1. 0.75 0.75 1. 1. 0.57142857 0.75 0.75 1. 0.75 ] mean value: 0.8321428571428571 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.6 0.6 1. 1. 0.4 0.6 0.6 1. 0.6] mean value: 0.74 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.8 0.8 1. 1. 0.7 0.8 0.8 1. 0.8] mean value: 0.8700000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.8 0.8 1. 1. 0.7 0.8 0.8 1. 0.8] mean value: 0.8700000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.6 0.6 1. 1. 0.4 0.6 0.6 1. 0.6] mean value: 0.74 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 50 mean value: 50.0 key: FP value: 13 mean value: 13.0 key: FN value: 0 mean value: 0.0 key: TP value: 37 mean value: 37.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.65 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02094769 0.01264119 0.01544523 0.02882671 0.03682876 0.02940774 0.0313282 0.03155279 0.05608463 0.03202128] mean value: 0.029508423805236817 key: score_time value: [0.01188612 0.01171374 0.0116632 0.01729679 0.01832533 0.02263856 0.02219057 0.0126164 0.01564741 0.01932001] mean value: 0.01632981300354004 key: test_mcc value: [0.81649658 0.6 1. 0.5 0.65465367 0.6 0.6 0.21821789 0. 0.81649658] mean value: 0.5805864722799422 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 0.97801929 0.97801929 1. ] mean value: 0.9956038587687303 key: test_fscore value: [0.90909091 0.8 1. 0.76923077 0.83333333 0.8 0.8 0.66666667 0.54545455 0.88888889] mean value: 0.8012665112665113 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 0.98901099 0.98876404 1. ] mean value: 0.997777503395481 key: test_precision value: [0.83333333 0.8 1. 0.625 0.71428571 0.8 0.8 0.57142857 0.5 1. ] mean value: 0.7644047619047619 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 0.97826087 1. 1. ] mean value: 0.9978260869565216 key: test_recall value: [1. 0.8 1. 1. 1. 0.8 0.8 0.8 0.6 0.8] mean value: 0.86 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 0.97777778 1. ] mean value: 0.9977777777777778 key: test_accuracy value: [0.9 0.8 1. 0.7 0.8 0.8 0.8 0.6 0.5 0.9] mean value: 0.78 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 0.98888889 0.98888889 1. ] mean value: 0.9977777777777778 key: test_roc_auc value: [0.9 0.8 1. 0.7 0.8 0.8 0.8 0.6 0.5 0.9] mean value: 0.78 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 0.98888889 0.98888889 1. ] mean value: 0.9977777777777778 key: test_jcc value: [0.83333333 0.66666667 1. 0.625 0.71428571 0.66666667 0.66666667 0.5 0.375 0.8 ] mean value: 0.6847619047619047 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 0.97826087 0.97777778 1. ] mean value: 0.9956038647342995 key: TN value: 35 mean value: 35.0 key: FP value: 7 mean value: 7.0 key: FN value: 15 mean value: 15.0 key: TP value: 43 mean value: 43.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.16 Accuracy on Blind test: 0.62 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.09823203 0.18534827 0.08709049 0.1154747 0.18197918 0.18414021 0.16416526 0.08809209 0.2873745 0.2193656 ] mean value: 0.1611262321472168 key: score_time value: [0.02172661 0.01154947 0.01647282 0.02121782 0.02233553 0.02070236 0.01157141 0.01160717 0.02128386 0.01674223] mean value: 0.017520928382873537 key: test_mcc value: [0.81649658 0.65465367 0.81649658 0.5 0.81649658 0.21821789 0.6 0.40824829 0. 0.65465367] mean value: 0.5485263264898987 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 0.86666667 0.97801929 1. ] mean value: 0.9844685960510319 key: test_fscore value: [0.90909091 0.83333333 0.90909091 0.76923077 0.90909091 0.66666667 0.8 0.72727273 0.54545455 0.75 ] mean value: 0.7819230769230769 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 0.93333333 0.98876404 1. ] mean value: 0.9922097378277155 key: test_precision value: [0.83333333 0.71428571 0.83333333 0.625 0.83333333 0.57142857 0.8 0.66666667 0.5 1. ] mean value: 0.7377380952380953 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 0.93333333 1. 1. ] mean value: 0.9933333333333334 key: test_recall value: [1. 1. 1. 1. 1. 0.8 0.8 0.8 0.6 0.6] mean value: 0.86 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 0.93333333 0.97777778 1. ] mean value: 0.9911111111111112 key: test_accuracy value: [0.9 0.8 0.9 0.7 0.9 0.6 0.8 0.7 0.5 0.8] mean value: 0.76 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 0.93333333 0.98888889 1. ] /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:282: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:283: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_CV['Resampling'] = rs_ros /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:288: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:289: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy ros_BT['Resampling'] = rs_ros mean value: 0.9922222222222222 key: test_roc_auc value: [0.9 0.8 0.9 0.7 0.9 0.6 0.8 0.7 0.5 0.8] mean value: 0.76 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 0.93333333 0.98888889 1. ] mean value: 0.9922222222222222 key: test_jcc value: [0.83333333 0.71428571 0.83333333 0.625 0.83333333 0.5 0.66666667 0.57142857 0.375 0.6 ] mean value: 0.6552380952380952 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 0.875 0.97777778 1. ] mean value: 0.9852777777777778 key: TN value: 33 mean value: 33.0 key: FP value: 7 mean value: 7.0 key: FN value: 17 mean value: 17.0 key: TP value: 43 mean value: 43.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.03 Accuracy on Blind test: 0.52 PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02247024 0.02279019 0.0214591 0.02027655 0.02126312 0.02295542 0.02090645 0.02397966 0.02241564 0.02300167] mean value: 0.022151803970336913 key: score_time value: [0.0113349 0.01142359 0.01140141 0.01136398 0.01136851 0.01148033 0.01136971 0.01144123 0.01137614 0.01134181] mean value: 0.011390161514282227 key: test_mcc value: [0.4472136 1. 0. 0. 0. 1. 0. 0. 0.40824829 1. ] mean value: 0.3855461885963821 key: train_mcc value: [0.88527041 0.96225045 0.92307692 1. 0.92307692 0.88527041 0.96225045 0.92307692 0.92450142 0.96291111] mean value: 0.9351685024695351 key: test_fscore value: [0.5 1. 0.66666667 0.4 0.57142857 1. 0.4 0.57142857 0.66666667 1. ] mean value: 0.6776190476190476 key: train_fscore value: [0.94117647 0.98039216 0.96153846 1. 0.96153846 0.94339623 0.98039216 0.96153846 0.96296296 0.98039216] mean value: 0.9673327515169913 key: test_precision value: [1. 1. 0.5 0.5 0.5 1. 0.5 0.5 0.5 1. ] mean value: 0.7 key: train_precision value: [0.96 1. 0.96153846 1. 0.96153846 0.92592593 1. 0.96153846 0.96296296 1. ] mean value: 0.9733504273504273 key: test_recall value: [0.33333333 1. 1. 0.33333333 0.66666667 1. 0.33333333 0.66666667 1. 1. ] mean value: 0.7333333333333333 key: train_recall value: [0.92307692 0.96153846 0.96153846 1. 0.96153846 0.96153846 0.96153846 0.96153846 0.96296296 0.96153846] mean value: 0.9616809116809117 key: test_accuracy value: [0.66666667 1. 0.5 0.5 0.5 1. 0.5 0.5 0.6 1. ] mean value: 0.6766666666666665 key: train_accuracy value: [0.94230769 0.98076923 0.96153846 1. 0.96153846 0.94230769 0.98076923 0.96153846 0.96226415 0.98113208] mean value: 0.9674165457184325 key: test_roc_auc value: [0.66666667 1. 0.5 0.5 0.5 1. 0.5 0.5 0.66666667 1. ] mean value: 0.6833333333333333 key: train_roc_auc value: [0.94230769 0.98076923 0.96153846 1. 0.96153846 0.94230769 0.98076923 0.96153846 0.96225071 0.98076923] mean value: 0.9673789173789175 key: test_jcc value: [0.33333333 1. 0.5 0.25 0.4 1. 0.25 0.4 0.5 1. ] mean value: 0.5633333333333332 key: train_jcc value: [0.88888889 0.96153846 0.92592593 1. 0.92592593 0.89285714 0.96153846 0.92592593 0.92857143 0.96153846] mean value: 0.9372710622710623 key: TN value: 18 mean value: 18.0 key: FP value: 8 mean value: 8.0 key: FN value: 11 mean value: 11.0 key: TP value: 21 mean value: 21.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.17 Accuracy on Blind test: 0.57 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.3368907 0.35755754 0.36199379 0.30242968 0.30465245 0.3230257 0.34169865 0.33659816 0.40250373 0.39995384] mean value: 0.3467304229736328 key: score_time value: [0.01330423 0.01177645 0.01205063 0.01192522 0.01168323 0.01168942 0.01167512 0.01225448 0.01178122 0.01063108] mean value: 0.011877107620239257 key: test_mcc value: [0.70710678 1. 0.70710678 0. 0. 1. 0. 0.4472136 1. 1. ] mean value: 0.5861427157873053 key: train_mcc value: [0.96225045 0.96225045 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9924500897298753 key: test_fscore value: [0.8 1. 0.85714286 0.4 0.57142857 1. 0.4 0.75 1. 1. ] mean value: 0.7778571428571428 key: train_fscore value: [0.98039216 0.98039216 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.996078431372549 key: test_precision value: [1. 1. 0.75 0.5 0.5 1. 0.5 0.6 1. 1. ] mean value: 0.7849999999999999 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 1. 1. 0.33333333 0.66666667 1. 0.33333333 1. 1. 1. ] mean value: 0.8 key: train_recall value: [0.96153846 0.96153846 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9923076923076923 key: test_accuracy value: [0.83333333 1. 0.83333333 0.5 0.5 1. 0.5 0.66666667 1. 1. ] mean value: 0.7833333333333334 key: train_accuracy value: [0.98076923 0.98076923 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9961538461538462 key: test_roc_auc value: [0.83333333 1. 0.83333333 0.5 0.5 1. 0.5 0.66666667 1. 1. ] mean value: 0.7833333333333334 key: train_roc_auc value: [0.98076923 0.98076923 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9961538461538462 key: test_jcc value: [0.66666667 1. 0.75 0.25 0.4 1. 0.25 0.6 1. 1. ] mean value: 0.6916666666666667 key: train_jcc value: [0.96153846 0.96153846 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9923076923076923 key: TN value: 22 mean value: 22.0 key: FP value: 6 mean value: 6.0 key: FN value: 7 mean value: 7.0 key: TP value: 23 mean value: 23.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.25 Accuracy on Blind test: 0.62 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01113558 0.01096749 0.00920558 0.00855684 0.00815439 0.00800776 0.00789642 0.00879645 0.00837684 0.00784945] mean value: 0.008894681930541992 key: score_time value: [0.01130128 0.01066971 0.00945187 0.0082593 0.00822663 0.00822568 0.00823283 0.00896049 0.00820088 0.00893879] mean value: 0.00904674530029297 key: test_mcc value: [-0.33333333 0.70710678 0. -0.33333333 0. 0.4472136 -0.4472136 -0.70710678 -0.16666667 0.40824829] mean value: -0.04250850428694703 key: train_mcc value: [0.50336201 0.64676167 0.69230769 0.58080232 0.77151675 0.75878691 0.62279916 0.81312325 0.67348073 0.51261937] mean value: 0.6575559848126133 key: test_fscore value: [0.33333333 0.85714286 0.66666667 0.33333333 0.66666667 0.75 0.5 0. 0.4 0.5 ] mean value: 0.5007142857142858 key: train_fscore value: [0.76363636 0.83333333 0.84615385 0.8 0.88888889 0.88135593 0.82142857 0.89795918 0.81632653 0.77419355] mean value: 0.8323276198317204 key: test_precision value: [0.33333333 0.75 0.5 0.33333333 0.5 0.6 0.4 0. 0.33333333 1. ] mean value: 0.475 key: train_precision value: [0.72413793 0.73529412 0.84615385 0.75862069 0.85714286 0.78787879 0.76666667 0.95652174 0.90909091 0.66666667] mean value: 0.8008174211066882 key: test_recall value: [0.33333333 1. 1. 0.33333333 1. 1. 0.66666667 0. 0.5 0.33333333] mean value: 0.6166666666666666 key: train_recall value: [0.80769231 0.96153846 0.84615385 0.84615385 0.92307692 1. 0.88461538 0.84615385 0.74074074 0.92307692] mean value: 0.8779202279202278 key: test_accuracy value: [0.33333333 0.83333333 0.5 0.33333333 0.5 0.66666667 0.33333333 0.16666667 0.4 0.6 ] mean value: 0.4666666666666666 key: train_accuracy value: [0.75 0.80769231 0.84615385 0.78846154 0.88461538 0.86538462 0.80769231 0.90384615 0.83018868 0.73584906] mean value: 0.8219883889695211 key: test_roc_auc value: [0.33333333 0.83333333 0.5 0.33333333 0.5 0.66666667 0.33333333 0.16666667 0.41666667 0.66666667] mean value: 0.4750000000000001 key: train_roc_auc value: [0.75 0.80769231 0.84615385 0.78846154 0.88461538 0.86538462 0.80769231 0.90384615 0.83190883 0.73931624] mean value: 0.8225071225071225 key: test_jcc value: [0.2 0.75 0.5 0.2 0.5 0.6 0.33333333 0. 0.25 0.33333333] mean value: 0.36666666666666664 key: train_jcc value: [0.61764706 0.71428571 0.73333333 0.66666667 0.8 0.78787879 0.6969697 0.81481481 0.68965517 0.63157895] mean value: 0.7152830192554758 key: TN value: 9 mean value: 9.0 key: FP value: 11 mean value: 11.0 key: FN value: 20 mean value: 20.0 key: TP value: 18 mean value: 18.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.39 Accuracy on Blind test: 0.65 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00813365 0.00844741 0.00814033 0.00801945 0.00898266 0.00798416 0.00815678 0.00803804 0.00806212 0.00834966] mean value: 0.008231425285339355 key: score_time value: [0.00833273 0.00839019 0.00825405 0.00831342 0.00851583 0.00821781 0.00835562 0.00828004 0.00859928 0.00834107] mean value: 0.008360004425048828 key: test_mcc value: [ 0. 0.33333333 -0.33333333 0. 0.33333333 0.33333333 -0.33333333 -0.70710678 -0.16666667 0.16666667] mean value: -0.03737734478532143 key: train_mcc value: [0.57735027 0.73131034 0.61538462 0.65433031 0.84615385 0.70064905 0.58080232 0.6172134 0.58547009 0.54921597] mean value: 0.6457880196732791 key: test_fscore value: [0.57142857 0.66666667 0.33333333 0.4 0.66666667 0.66666667 0.33333333 0. 0.4 0.66666667] mean value: 0.4704761904761905 key: train_fscore value: [0.79245283 0.86792453 0.80769231 0.83018868 0.92307692 0.85714286 0.8 0.81481481 0.79245283 0.77777778] mean value: 0.8263523548429209 key: test_precision value: [0.5 0.66666667 0.33333333 0.5 0.66666667 0.66666667 0.33333333 0. 0.33333333 0.66666667] mean value: 0.4666666666666666 key: train_precision value: [0.77777778 0.85185185 0.80769231 0.81481481 0.92307692 0.8 0.75862069 0.78571429 0.80769231 0.75 ] mean value: 0.8077240958275441 key: test_recall value: [0.66666667 0.66666667 0.33333333 0.33333333 0.66666667 0.66666667 0.33333333 0. 0.5 0.66666667] mean value: 0.4833333333333333 key: train_recall value: [0.80769231 0.88461538 0.80769231 0.84615385 0.92307692 0.92307692 0.84615385 0.84615385 0.77777778 0.80769231] mean value: 0.847008547008547 key: test_accuracy value: [0.5 0.66666667 0.33333333 0.5 0.66666667 0.66666667 0.33333333 0.16666667 0.4 0.6 ] mean value: 0.4833333333333333 key: train_accuracy value: [0.78846154 0.86538462 0.80769231 0.82692308 0.92307692 0.84615385 0.78846154 0.80769231 0.79245283 0.77358491] mean value: 0.8219883889695211 key: test_roc_auc value: [0.5 0.66666667 0.33333333 0.5 0.66666667 0.66666667 0.33333333 0.16666667 0.41666667 0.58333333] mean value: 0.4833333333333333 key: train_roc_auc value: [0.78846154 0.86538462 0.80769231 0.82692308 0.92307692 0.84615385 0.78846154 0.80769231 0.79273504 0.77421652] mean value: 0.8220797720797719 key: test_jcc value: [0.4 0.5 0.2 0.25 0.5 0.5 0.2 0. 0.25 0.5 ] mean value: 0.32999999999999996 key: train_jcc value: [0.65625 0.76666667 0.67741935 0.70967742 0.85714286 0.75 0.66666667 0.6875 0.65625 0.63636364] mean value: 0.7063936601033376 key: TN value: 14 mean value: 14.0 key: FP value: 15 mean value: 15.0 key: FN value: 15 mean value: 15.0 key: TP value: 14 mean value: 14.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.02 Accuracy on Blind test: 0.45 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01033497 0.00774837 0.00769258 0.00766277 0.00772595 0.00759411 0.00768042 0.00764728 0.00764275 0.00769997] mean value: 0.007942914962768555 key: score_time value: [0.01190901 0.00875068 0.00876427 0.0087657 0.00875902 0.0089035 0.00881863 0.00920081 0.00875378 0.00871611] mean value: 0.009134149551391602 key: test_mcc value: [ 0.70710678 0.70710678 0. -0.4472136 0.4472136 0.4472136 -0.4472136 0. 0.66666667 0.66666667] mean value: 0.27475468957064286 key: train_mcc value: [0.34641016 0.34641016 0.54006172 0.65433031 0.50951017 0.54006172 0.65433031 0.57735027 0.43447293 0.50997151] mean value: 0.511290926764467 key: test_fscore value: [0.8 0.8 0.57142857 0. 0.75 0.5 0.5 0.57142857 0.8 0.8 ] mean value: 0.6092857142857143 key: train_fscore value: [0.66666667 0.66666667 0.76 0.82352941 0.72340426 0.76 0.82352941 0.78431373 0.71698113 0.75471698] mean value: 0.7479808250879637 key: test_precision value: [1. 1. 0.5 0. 0.6 1. 0.4 0.5 0.66666667 1. ] mean value: 0.6666666666666667 key: train_precision value: [0.68 0.68 0.79166667 0.84 0.80952381 0.79166667 0.84 0.8 0.73076923 0.74074074] mean value: 0.7704367114367114 key: test_recall value: [0.66666667 0.66666667 0.66666667 0. 1. 0.33333333 0.66666667 0.66666667 1. 0.66666667] mean value: 0.6333333333333333 key: train_recall value: [0.65384615 0.65384615 0.73076923 0.80769231 0.65384615 0.73076923 0.80769231 0.76923077 0.7037037 0.76923077] mean value: 0.728062678062678 key: test_accuracy value: [0.83333333 0.83333333 0.5 0.33333333 0.66666667 0.66666667 0.33333333 0.5 0.8 0.8 ] mean value: 0.6266666666666666 key: train_accuracy value: [0.67307692 0.67307692 0.76923077 0.82692308 0.75 0.76923077 0.82692308 0.78846154 0.71698113 0.75471698] mean value: 0.7548621190130624 key: test_roc_auc value: [0.83333333 0.83333333 0.5 0.33333333 0.66666667 0.66666667 0.33333333 0.5 0.83333333 0.83333333] mean value: 0.6333333333333333 key: train_roc_auc value: [0.67307692 0.67307692 0.76923077 0.82692308 0.75 0.76923077 0.82692308 0.78846154 0.71723647 0.75498575] mean value: 0.7549145299145299 key: test_jcc value: [0.66666667 0.66666667 0.4 0. 0.6 0.33333333 0.33333333 0.4 0.66666667 0.66666667] mean value: 0.47333333333333344 key: train_jcc value: [0.5 0.5 0.61290323 0.7 0.56666667 0.61290323 0.7 0.64516129 0.55882353 0.60606061] mean value: 0.6002518544074522 key: TN value: 18 mean value: 18.0 key: FP value: 11 mean value: 11.0 key: FN value: 11 mean value: 11.0 key: TP value: 18 mean value: 18.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.1 Accuracy on Blind test: 0.55 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.00983238 0.00817156 0.00807023 0.00817275 0.00810385 0.00809503 0.00810146 0.00812173 0.00830746 0.00820637] mean value: 0.008318281173706055 key: score_time value: [0.00890064 0.00827098 0.00825214 0.00835896 0.00818491 0.00825906 0.00822496 0.00825191 0.00823522 0.00826621] mean value: 0.008320498466491699 key: test_mcc value: [-0.70710678 0.70710678 0. -0.4472136 -0.4472136 0.70710678 -0.4472136 -0.4472136 0. 0.40824829] mean value: -0.06734993103494209 key: train_mcc value: [0.58789635 0.77151675 0.80829038 0.84615385 0.84615385 0.84866842 0.80829038 0.77849894 0.71546507 0.81612228] mean value: 0.7827056268909882 key: test_fscore value: [0.28571429 0.8 0.66666667 0. 0.5 0.8 0.5 0.5 0.57142857 0.5 ] mean value: 0.5123809523809524 key: train_fscore value: [0.80701754 0.88 0.90566038 0.92307692 0.92307692 0.92592593 0.90566038 0.89285714 0.86666667 0.89795918] mean value: 0.8927901063853682 key: test_precision value: [0.25 1. 0.5 0. 0.4 1. 0.4 0.4 0.4 1. ] mean value: 0.535 key: train_precision value: [0.74193548 0.91666667 0.88888889 0.92307692 0.92307692 0.89285714 0.88888889 0.83333333 0.78787879 0.95652174] mean value: 0.8753124777668958 key: test_recall value: [0.33333333 0.66666667 1. 0. 0.66666667 0.66666667 0.66666667 0.66666667 1. 0.33333333] mean value: 0.5999999999999999 key: train_recall value: [0.88461538 0.84615385 0.92307692 0.92307692 0.92307692 0.96153846 0.92307692 0.96153846 0.96296296 0.84615385] mean value: 0.9155270655270658 key: test_accuracy value: [0.16666667 0.83333333 0.5 0.33333333 0.33333333 0.83333333 0.33333333 0.33333333 0.4 0.6 ] mean value: 0.4666666666666667 key: train_accuracy value: [0.78846154 0.88461538 0.90384615 0.92307692 0.92307692 0.92307692 0.90384615 0.88461538 0.8490566 0.90566038] mean value: 0.888933236574746 key: test_roc_auc value: [0.16666667 0.83333333 0.5 0.33333333 0.33333333 0.83333333 0.33333333 0.33333333 0.5 0.66666667] mean value: 0.4833333333333333 key: train_roc_auc value: [0.78846154 0.88461538 0.90384615 0.92307692 0.92307692 0.92307692 0.90384615 0.88461538 0.8468661 0.9045584 ] mean value: 0.8886039886039887 key: test_jcc value: [0.16666667 0.66666667 0.5 0. 0.33333333 0.66666667 0.33333333 0.33333333 0.4 0.33333333] mean value: 0.37333333333333335 key: train_jcc value: [0.67647059 0.78571429 0.82758621 0.85714286 0.85714286 0.86206897 0.82758621 0.80645161 0.76470588 0.81481481] mean value: 0.807968427761662 key: TN value: 10 mean value: 10.0 key: FP value: 12 mean value: 12.0 key: FN value: 19 mean value: 19.0 key: TP value: 17 mean value: 17.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.17 Accuracy on Blind test: 0.52 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.27118325 0.26555204 0.26822782 0.3543098 0.23144531 0.2879324 0.27575207 0.28139663 0.31252456 0.28410864] mean value: 0.2832432508468628 key: score_time value: [0.01188612 0.01264691 0.01187563 0.0120225 0.01204729 0.01216674 0.01212144 0.01220417 0.01201892 0.0119679 ] mean value: 0.012095761299133301 key: test_mcc value: [ 0.33333333 1. 0.4472136 -0.4472136 0. 1. 0. -0.33333333 -0.16666667 1. ] mean value: 0.2833333333333333 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 1. 0.75 0. 0.57142857 1. 0.4 0.33333333 0.4 1. ] mean value: 0.6121428571428572 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 1. 0.6 0. 0.5 1. 0.5 0.33333333 0.33333333 1. ] mean value: 0.5933333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 1. 1. 0. 0.66666667 1. 0.33333333 0.33333333 0.5 1. ] mean value: 0.65 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 1. 0.66666667 0.33333333 0.5 1. 0.5 0.33333333 0.4 1. ] mean value: 0.64 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66666667 1. 0.66666667 0.33333333 0.5 1. 0.5 0.33333333 0.41666667 1. ] mean value: 0.6416666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 1. 0.6 0. 0.4 1. 0.25 0.2 0.25 1. ] mean value: 0.52 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 18 mean value: 18.0 key: FP value: 10 mean value: 10.0 key: FN value: 11 mean value: 11.0 key: TP value: 19 mean value: 19.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.18 Accuracy on Blind test: 0.6 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01426125 0.01220822 0.00889158 0.0087297 0.00895548 0.00863051 0.00851607 0.00860262 0.00862265 0.00863194] mean value: 0.009605002403259278 key: score_time value: [0.01187658 0.0103178 0.00851917 0.00812912 0.00841165 0.00818539 0.00814438 0.00818205 0.0081892 0.0081985 ] mean value: 0.008815383911132813 key: test_mcc value: [0.70710678 1. 0. 0.4472136 0. 0.70710678 1. 0.70710678 0.66666667 1. ] mean value: 0.6235200605726268 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 1. 0.57142857 0.5 0.57142857 0.85714286 1. 0.85714286 0.8 1. ] mean value: 0.7957142857142857 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.5 1. 0.5 0.75 1. 0.75 0.66666667 1. ] mean value: 0.8166666666666668 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 1. 0.66666667 0.33333333 0.66666667 1. 1. 1. 1. 1. ] mean value: 0.8333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.83333333 1. 0.5 0.66666667 0.5 0.83333333 1. 0.83333333 0.8 1. ] mean value: 0.7966666666666666 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.83333333 1. 0.5 0.66666667 0.5 0.83333333 1. 0.83333333 0.83333333 1. ] mean value: 0.8 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 1. 0.4 0.33333333 0.4 0.75 1. 0.75 0.66666667 1. ] mean value: 0.6966666666666667 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 22 mean value: 22.0 key: FP value: 5 mean value: 5.0 key: FN value: 7 mean value: 7.0 key: TP value: 24 mean value: 24.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.95 Accuracy on Blind test: 0.98 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.08186579 0.07851267 0.07875395 0.08117771 0.07774711 0.07864809 0.07936549 0.08071136 0.07908726 0.07877016] mean value: 0.07946395874023438 key: score_time value: [0.01654387 0.01662087 0.01674056 0.01655841 0.01825023 0.01663804 0.0165503 0.01738572 0.01664352 0.01671124] mean value: 0.01686427593231201 key: test_mcc value: [-0.33333333 0.70710678 0. 0. -0.70710678 0.70710678 0. -0.4472136 0.16666667 0.40824829] mean value: 0.050147480948378606 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.33333333 0.8 0.57142857 0.4 0.28571429 0.8 0.57142857 0.5 0.5 0.5 ] mean value: 0.5261904761904763 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.33333333 1. 0.5 0.5 0.25 1. 0.5 0.4 0.5 1. ] mean value: 0.5983333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.33333333 0.66666667 0.66666667 0.33333333 0.33333333 0.66666667 0.66666667 0.66666667 0.5 0.33333333] mean value: 0.5166666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.33333333 0.83333333 0.5 0.5 0.16666667 0.83333333 0.5 0.33333333 0.6 0.6 ] mean value: 0.5199999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.33333333 0.83333333 0.5 0.5 0.16666667 0.83333333 0.5 0.33333333 0.58333333 0.66666667] mean value: 0.525 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.2 0.66666667 0.4 0.25 0.16666667 0.66666667 0.4 0.33333333 0.33333333 0.33333333] mean value: 0.375 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 15 mean value: 15.0 key: FP value: 14 mean value: 14.0 key: FN value: 14 mean value: 14.0 key: TP value: 15 mean value: 15.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.26 Accuracy on Blind test: 0.65 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00797677 0.00787926 0.00788188 0.00788665 0.00801349 0.0082798 0.00797129 0.0079236 0.00790405 0.00862122] mean value: 0.00803380012512207 key: score_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.0082202 0.00814605 0.00813627 0.00815105 0.00814056 0.008214 0.00821114 0.00814772 0.00879383 0.00855756] mean value: 0.00827183723449707 key: test_mcc value: [ 0. 0.70710678 -0.4472136 -0.70710678 -0.70710678 -0.4472136 0. 0.4472136 0.66666667 0.66666667] mean value: 0.017901295664682747 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.4 0.8 0.5 0. 0.28571429 0. 0.57142857 0.75 0.8 0.8 ] mean value: 0.49071428571428577 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 1. 0.4 0. 0.25 0. 0.5 0.6 0.66666667 1. ] mean value: 0.4916666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.33333333 0.66666667 0.66666667 0. 0.33333333 0. 0.66666667 1. 1. 0.66666667] mean value: 0.5333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.5 0.83333333 0.33333333 0.16666667 0.16666667 0.33333333 0.5 0.66666667 0.8 0.8 ] mean value: 0.51 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.83333333 0.33333333 0.16666667 0.16666667 0.33333333 0.5 0.66666667 0.83333333 0.83333333] mean value: 0.5166666666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.25 0.66666667 0.33333333 0. 0.16666667 0. 0.4 0.6 0.66666667 0.66666667] mean value: 0.375 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 14 mean value: 14.0 key: FP value: 14 mean value: 14.0 key: FN value: 15 mean value: 15.0 key: TP value: 15 mean value: 15.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.21 Accuracy on Blind test: 0.57 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [0.98296475 0.98631954 0.98036337 0.98156142 0.97943497 0.98355842 0.98102999 0.97444534 0.98225522 0.97287917] mean value: 0.980481219291687 key: score_time value: [0.0900774 0.09229064 0.09402585 0.09391332 0.09271097 0.0916388 0.09372568 0.09287024 0.09086537 0.08647847] mean value: 0.09185967445373536 key: test_mcc value: [0. 1. 0. 0.4472136 0. 1. 0. 0. 1. 0.66666667] mean value: 0.41138802621666243 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.4 1. 0.66666667 0.5 0.57142857 1. 0.57142857 0.57142857 1. 0.8 ] mean value: 0.7080952380952381 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 1. 0.5 1. 0.5 1. 0.5 0.5 1. 1. ] mean value: 0.75 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.33333333 1. 1. 0.33333333 0.66666667 1. 0.66666667 0.66666667 1. 0.66666667] mean value: 0.7333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.5 1. 0.5 0.66666667 0.5 1. 0.5 0.5 1. 0.8 ] mean value: 0.6966666666666665 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 1. 0.5 0.66666667 0.5 1. 0.5 0.5 1. 0.83333333] mean value: 0.7 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.25 1. 0.5 0.33333333 0.4 1. 0.4 0.4 1. 0.66666667] mean value: 0.595 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 19 mean value: 19.0 key: FP value: 8 mean value: 8.0 key: FN value: 10 mean value: 10.0 key: TP value: 21 mean value: 21.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.56 Accuracy on Blind test: 0.8 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.77408147 0.83756614 0.84048009 0.8518939 0.8126564 0.85278058 0.81466842 0.89787817 0.79662037 0.82353354] mean value: 0.8302159070968628 key: score_time value: [0.18825769 0.19139767 0.17149496 0.18060875 0.18776417 0.12078071 0.18363857 0.19443631 0.18648338 0.21026301] mean value: 0.18151252269744872 key: test_mcc value: [0. 1. 0. 0.4472136 0. 1. 0. 0.70710678 0.66666667 0.66666667] mean value: 0.4487653710019838 key: train_mcc value: [0.9258201 0.96225045 1. 0.96225045 1. 0.96225045 1. 0.96225045 0.92450142 1. ] mean value: 0.9699323318871482 key: test_fscore value: [0.4 1. 0.66666667 0.5 0.57142857 1. 0.57142857 0.85714286 0.8 0.8 ] mean value: 0.7166666666666666 key: train_fscore value: [0.96 0.98039216 1. 0.98039216 1. 0.98113208 1. 0.98113208 0.96296296 1. ] mean value: 0.9846011427631851 key: test_precision value: [0.5 1. 0.5 1. 0.5 1. 0.5 0.75 0.66666667 1. ] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) 0.7416666666666667 key: train_precision value: [1. 1. 1. 1. 1. 0.96296296 1. 0.96296296 0.96296296 1. ] mean value: 0.9888888888888889 key: test_recall value: [0.33333333 1. 1. 0.33333333 0.66666667 1. 0.66666667 1. 1. 0.66666667] mean value: 0.7666666666666667 key: train_recall value: [0.92307692 0.96153846 1. 0.96153846 1. 1. 1. 1. 0.96296296 1. ] mean value: 0.980911680911681 key: test_accuracy value: [0.5 1. 0.5 0.66666667 0.5 1. 0.5 0.83333333 0.8 0.8 ] mean value: 0.71 key: train_accuracy value: [0.96153846 0.98076923 1. 0.98076923 1. 0.98076923 1. 0.98076923 0.96226415 1. ] mean value: 0.9846879535558781 key: test_roc_auc value: [0.5 1. 0.5 0.66666667 0.5 1. 0.5 0.83333333 0.83333333 0.83333333] mean value: 0.7166666666666666 key: train_roc_auc value: [0.96153846 0.98076923 1. 0.98076923 1. 0.98076923 1. 0.98076923 0.96225071 1. ] mean value: 0.9846866096866098 key: test_jcc value: [0.25 1. 0.5 0.33333333 0.4 1. 0.4 0.75 0.66666667 0.66666667] mean value: 0.5966666666666667 key: train_jcc value: [0.92307692 0.96153846 1. 0.96153846 1. 0.96296296 1. 0.96296296 0.92857143 1. ] mean value: 0.9700651200651201 key: TN value: 19 mean value: 19.0 key: FP value: 7 mean value: 7.0 key: FN value: 10 mean value: 10.0 key: TP value: 22 mean value: 22.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.51 Accuracy on Blind test: 0.78 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.06364822 0.03136325 0.02988315 0.02997828 0.030056 0.03207469 0.03320622 0.03187776 0.03167343 0.04203773] mean value: 0.035579872131347653 key: score_time value: [0.01013684 0.01014662 0.01000309 0.01048088 0.01002479 0.01074505 0.00997162 0.01008677 0.01084256 0.01022935] mean value: 0.010266757011413575 key: test_mcc value: [0.70710678 1. 0.33333333 0.4472136 0.33333333 0.70710678 1. 1. 1. 1. ] mean value: 0.752809382453972 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 1. 0.66666667 0.5 0.66666667 0.85714286 1. 1. 1. 1. ] mean value: 0.849047619047619 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.66666667 1. 0.66666667 0.75 1. 1. 1. 1. ] mean value: 0.9083333333333332 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 1. 0.66666667 0.33333333 0.66666667 1. 1. 1. 1. 1. ] mean value: 0.8333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333 1. 1. 1. 1. ] mean value: 0.8666666666666668 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333 1. 1. 1. 1. ] mean value: 0.8666666666666668 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 1. 0.5 0.33333333 0.5 0.75 1. 1. 1. 1. ] mean value: 0.775 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 26 mean value: 26.0 key: FP value: 5 mean value: 5.0 key: FN value: 3 mean value: 3.0 key: TP value: 24 mean value: 24.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 1.0 Accuracy on Blind test: 1.0 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.01090717 0.04319477 0.03406215 0.0360713 0.03453875 0.03405213 0.04831815 0.01464033 0.01464176 0.01442671] mean value: 0.028485321998596193 key: score_time value: [0.01095223 0.02088284 0.02107692 0.01997852 0.02296853 0.0319159 0.01285052 0.01170683 0.01175642 0.01163101] mean value: 0.01757197380065918 key: test_mcc value: [ 0.70710678 1. 0. -0.33333333 0. 0.70710678 0.33333333 0.70710678 0. 0.61237244] mean value: 0.37336927792554375 key: train_mcc value: [1. 0.84615385 1. 0.96225045 0.96225045 0.96225045 1. 1. 1. 1. ] mean value: 0.9732905192101976 key: test_fscore value: [0.8 1. 0.4 0.33333333 0.57142857 0.8 0.66666667 0.8 0. 0.85714286] mean value: 0.6228571428571429 key: train_fscore value: [1. 0.92307692 1. 0.98039216 0.98113208 0.98113208 1. 1. 1. 1. ] mean value: 0.9865733230883065 key: test_precision value: [1. 1. 0.5 0.33333333 0.5 1. 0.66666667 1. 0. 0.75 ] mean value: 0.675 key: train_precision value: [1. 0.92307692 1. 1. 0.96296296 0.96296296 1. 1. 1. 1. ] mean value: 0.9849002849002849 key: test_recall value: [0.66666667 1. 0.33333333 0.33333333 0.66666667 0.66666667 0.66666667 0.66666667 0. 1. ] mean value: 0.6 key: train_recall value: [1. 0.92307692 1. 0.96153846 1. 1. 1. 1. 1. 1. ] mean value: 0.9884615384615385 key: test_accuracy value: [0.83333333 1. 0.5 0.33333333 0.5 0.83333333 0.66666667 0.83333333 0.6 0.8 ] mean value: 0.69 key: train_accuracy value: [1. 0.92307692 1. 0.98076923 0.98076923 0.98076923 1. 1. 1. 1. ] mean value: 0.9865384615384617 key: test_roc_auc value: [0.83333333 1. 0.5 0.33333333 0.5 0.83333333 0.66666667 0.83333333 0.5 0.75 ] mean value: 0.675 key: train_roc_auc value: [1. 0.92307692 1. 0.98076923 0.98076923 0.98076923 1. 1. 1. 1. ] mean value: 0.9865384615384617 key: test_jcc value: [0.66666667 1. 0.25 0.2 0.4 0.66666667 0.5 0.66666667 0. 0.75 ] mean value: 0.51 key: train_jcc value: [1. 0.85714286 1. 0.96153846 0.96296296 0.96296296 1. 1. 1. 1. ] mean value: 0.9744607244607245 key: TN value: 22 mean value: 22.0 key: FP value: 11 mean value: 11.0 key: FN value: 7 mean value: 7.0 key: TP value: 18 mean value: 18.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.24 Accuracy on Blind test: 0.6 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.0219574 0.00874662 0.00845957 0.00799561 0.00798368 0.00791121 0.00849724 0.00794101 0.00807071 0.00823379] mean value: 0.009579682350158691 key: score_time value: [0.00983024 0.00915194 0.00822258 0.0083971 0.00818539 0.00826788 0.00891376 0.00871086 0.00815988 0.00857592] mean value: 0.008641552925109864 key: test_mcc value: [-0.33333333 0.70710678 0. 0. -0.4472136 0. -0.70710678 0.33333333 -0.61237244 0.40824829] mean value: -0.06513377407318895 key: train_mcc value: [0.34848139 0.54006172 0.4233902 0.4259217 0.46709937 0.50336201 0.4259217 0.50037023 0.39888558 0.43447293] mean value: 0.44679668423593266 key: test_fscore value: [0.33333333 0.8 0.66666667 0.4 0.5 0.4 0.28571429 0.66666667 0.33333333 0.5 ] mean value: 0.48857142857142855 key: train_fscore value: [0.69090909 0.76 0.70588235 0.72727273 0.70833333 0.73469388 0.72727273 0.75471698 0.72413793 0.71698113] mean value: 0.7250200153522106 key: test_precision value: [0.33333333 1. 0.5 0.5 0.4 0.5 0.25 0.66666667 0.25 1. ] mean value: 0.5399999999999999 key: train_precision value: [0.65517241 0.79166667 0.72 0.68965517 0.77272727 0.7826087 0.68965517 0.74074074 0.67741935 0.7037037 ] mean value: 0.7223349192949957 key: test_recall value: [0.33333333 0.66666667 1. 0.33333333 0.66666667 0.33333333 0.33333333 0.66666667 0.5 0.33333333] mean value: 0.5166666666666666 key: train_recall value: [0.73076923 0.73076923 0.69230769 0.76923077 0.65384615 0.69230769 0.76923077 0.76923077 0.77777778 0.73076923] mean value: 0.7316239316239317 key: test_accuracy value: [0.33333333 0.83333333 0.5 0.5 0.33333333 0.5 0.16666667 0.66666667 0.2 0.6 ] mean value: 0.46333333333333326 key: train_accuracy value: [0.67307692 0.76923077 0.71153846 0.71153846 0.73076923 0.75 0.71153846 0.75 0.69811321 0.71698113] mean value: 0.722278664731495 key: test_roc_auc value: [0.33333333 0.83333333 0.5 0.5 0.33333333 0.5 0.16666667 0.66666667 0.25 0.66666667] mean value: 0.475 key: train_roc_auc value: [0.67307692 0.76923077 0.71153846 0.71153846 0.73076923 0.75 0.71153846 0.75 0.6965812 0.71723647] mean value: 0.7221509971509972 key: test_jcc value: [0.2 0.66666667 0.5 0.25 0.33333333 0.25 0.16666667 0.5 0.2 0.33333333] mean value: 0.34 key: train_jcc value: [0.52777778 0.61290323 0.54545455 0.57142857 0.5483871 0.58064516 0.57142857 0.60606061 0.56756757 0.55882353] mean value: 0.5690476653000371 key: TN value: 12 mean value: 12.0 key: FP value: 14 mean value: 14.0 key: FN value: 17 mean value: 17.0 key: TP value: 15 mean value: 15.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.21 Accuracy on Blind test: 0.57 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.00844765 0.0117445 0.01213598 0.01206732 0.01215124 0.01197219 0.01199651 0.01190472 0.01303363 0.01203942] mean value: 0.01174931526184082 key: score_time value: [0.00813985 0.01085377 0.0108459 0.01131415 0.01135468 0.01144958 0.01139641 0.01133323 0.01140904 0.01141429] mean value: 0.010951089859008788 key: test_mcc value: [ 0.4472136 1. 0.4472136 0. -0.33333333 1. -0.33333333 0.4472136 0. 1. ] mean value: 0.3674974119833207 key: train_mcc value: [0.89056356 0.9258201 0.96225045 0.75878691 0.89056356 0.89056356 0.82305489 0.92307692 0.76178523 0.85164138] mean value: 0.8678106557240634 key: test_fscore value: [0.5 1. 0.75 0.57142857 0.33333333 1. 0.33333333 0.75 0.57142857 1. ] mean value: 0.6809523809523809 key: train_fscore value: [0.93877551 0.96 0.98113208 0.88135593 0.93877551 0.93877551 0.9122807 0.96153846 0.8852459 0.92592593] mean value: 0.9323805529145449 key: test_precision value: [1. 1. 0.6 0.5 0.33333333 1. 0.33333333 0.6 0.4 1. ] mean value: 0.6766666666666667 key: train_precision value: [1. 1. 0.96296296 0.78787879 1. 1. 0.83870968 0.96153846 0.79411765 0.89285714] mean value: 0.9238064679715533 key: test_recall value: [0.33333333 1. 1. 0.66666667 0.33333333 1. 0.33333333 1. 1. 1. ] mean value: 0.7666666666666666 key: train_recall value: [0.88461538 0.92307692 1. 1. 0.88461538 0.88461538 1. 0.96153846 1. 0.96153846] mean value: 0.95 key: test_accuracy value: [0.66666667 1. 0.66666667 0.5 0.33333333 1. 0.33333333 0.66666667 0.4 1. ] mean value: 0.6566666666666666 key: train_accuracy value: [0.94230769 0.96153846 0.98076923 0.86538462 0.94230769 0.94230769 0.90384615 0.96153846 0.86792453 0.9245283 ] mean value: 0.929245283018868 key: test_roc_auc value: [0.66666667 1. 0.66666667 0.5 0.33333333 1. 0.33333333 0.66666667 0.5 1. ] mean value: 0.6666666666666666 key: train_roc_auc value: [0.94230769 0.96153846 0.98076923 0.86538462 0.94230769 0.94230769 0.90384615 0.96153846 0.86538462 0.92521368] mean value: 0.929059829059829 key: test_jcc value: [0.33333333 1. 0.6 0.4 0.2 1. 0.2 0.6 0.4 1. ] mean value: 0.5733333333333334 key: train_jcc value: [0.88461538 0.92307692 0.96296296 0.78787879 0.88461538 0.88461538 0.83870968 0.92592593 0.79411765 0.86206897] mean value: 0.8748587043686173 key: TN value: 16 mean value: 16.0 key: FP value: 7 mean value: 7.0 key: FN value: 13 mean value: 13.0 key: TP value: 22 mean value: 22.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.03 Accuracy on Blind test: 0.4 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.00824594 0.00882649 0.01191044 0.01159239 0.01171517 0.01183748 0.01176429 0.011832 0.01183248 0.01164579] mean value: 0.011120247840881347 key: score_time value: [0.00841475 0.00876427 0.01136231 0.01142454 0.01130295 0.0113647 0.01149154 0.01136804 0.01151252 0.01137519] mean value: 0.010838079452514648 key: test_mcc value: [ 0.4472136 0.70710678 0.70710678 0. 0. 1. 0. 0.70710678 -0.16666667 1. ] mean value: 0.4401867272392934 key: train_mcc value: [0.28867513 0.89056356 1. 1. 1. 0.80829038 0.85634884 0.92307692 0.89227454 0.50219975] mean value: 0.8161429111522169 key: test_fscore value: [0.75 0.8 0.8 0.4 0.57142857 1. 0.4 0.85714286 0.4 1. ] mean value: 0.697857142857143 key: train_fscore value: [0.7027027 0.93877551 1. 1. 1. 0.90196078 0.91666667 0.96153846 0.94736842 0.76470588] mean value: 0.9133718428831212 key: test_precision value: [0.6 1. 1. 0.5 0.5 1. 0.5 0.75 0.33333333 1. ] mean value: 0.7183333333333333 key: train_precision value: [0.54166667 1. 1. 1. 1. 0.92 1. 0.96153846 0.9 0.61904762] mean value: 0.8942252747252747 key: test_recall value: [1. 0.66666667 0.66666667 0.33333333 0.66666667 1. 0.33333333 1. 0.5 1. ] mean value: 0.7166666666666666 key: train_recall value: [1. 0.88461538 1. 1. 1. 0.88461538 0.84615385 0.96153846 1. 1. ] mean value: 0.9576923076923076 key: test_accuracy value: [0.66666667 0.83333333 0.83333333 0.5 0.5 1. 0.5 0.83333333 0.4 1. ] mean value: 0.7066666666666668 key: train_accuracy value: [0.57692308 0.94230769 1. 1. 1. 0.90384615 0.92307692 0.96153846 0.94339623 0.69811321] mean value: 0.8949201741654571 key: test_roc_auc value: [0.66666667 0.83333333 0.83333333 0.5 0.5 1. 0.5 0.83333333 0.41666667 1. ] mean value: 0.7083333333333333 key: train_roc_auc value: [0.57692308 0.94230769 1. 1. 1. 0.90384615 0.92307692 0.96153846 0.94230769 0.7037037 ] mean value: 0.8953703703703704 key: test_jcc value: [0.6 0.66666667 0.66666667 0.25 0.4 1. 0.25 0.75 0.25 1. ] mean value: 0.5833333333333333 key: train_jcc value: [0.54166667 0.88461538 1. 1. 1. 0.82142857 0.84615385 0.92592593 0.9 0.61904762] mean value: 0.8538838013838014 key: TN value: 20 mean value: 20.0 key: FP value: 8 mean value: 8.0 key: FN value: 9 mean value: 9.0 key: TP value: 21 mean value: 21.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.06 Accuracy on Blind test: 0.52 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.06980157 0.07130361 0.07226229 0.06922984 0.06929612 0.07238531 0.0756197 0.07484674 0.07620382 0.07381821] mean value: 0.07247672080993653 key: score_time value: [0.01429987 0.01539707 0.01418209 0.01416135 0.01432991 0.01474619 0.01519608 0.01451135 0.01416588 0.01511621] mean value: 0.014610600471496583 key: test_mcc value: [0.70710678 1. 0.33333333 0.4472136 0.33333333 0.70710678 1. 1. 1. 1. ] mean value: 0.752809382453972 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 1. 0.66666667 0.5 0.66666667 0.85714286 1. 1. 1. 1. ] mean value: 0.849047619047619 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.66666667 1. 0.66666667 0.75 1. 1. 1. 1. ] mean value: 0.9083333333333332 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 1. 0.66666667 0.33333333 0.66666667 1. 1. 1. 1. 1. ] mean value: 0.8333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333 1. 1. 1. 1. ] mean value: 0.8666666666666668 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333 1. 1. 1. 1. ] mean value: 0.8666666666666668 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 1. 0.5 0.33333333 0.5 0.75 1. 1. 1. 1. ] mean value: 0.775 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 26 mean value: 26.0 key: FP value: 5 mean value: 5.0 key: FN value: 3 mean value: 3.0 key: TP value: 24 mean value: 24.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 1.0 Accuracy on Blind test: 1.0 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.02761078 0.0292685 0.03312254 0.02849674 0.03523755 0.0358634 0.02830291 0.02811313 0.03103304 0.03638864] mean value: 0.031343722343444826 key: score_time value: [0.02042437 0.02199721 0.02151895 0.03123069 0.02501988 0.02050543 0.02155209 0.03574848 0.02209949 0.02571082] mean value: 0.024580740928649904 key: test_mcc value: [0.70710678 1. 0.33333333 0.4472136 0.33333333 0.70710678 1. 1. 1. 1. ] mean value: 0.752809382453972 key: train_mcc value: [1. 0.96225045 1. 0.96225045 1. 1. 1. 0.96225045 0.96296296 1. ] mean value: 0.9849714308911093 key: test_fscore value: [0.8 1. 0.66666667 0.5 0.66666667 0.85714286 1. 1. 1. 1. ] mean value: 0.849047619047619 key: train_fscore value: [1. 0.98039216 1. 0.98039216 1. 1. 1. 0.98039216 0.98113208 1. ] mean value: 0.9922308546059935 key: test_precision value: [1. 1. 0.66666667 1. 0.66666667 0.75 1. 1. 1. 1. ] mean value: 0.9083333333333332 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 1. 0.66666667 0.33333333 0.66666667 1. 1. 1. 1. 1. ] mean value: 0.8333333333333333 key: train_recall value: [1. 0.96153846 1. 0.96153846 1. 1. 1. 0.96153846 0.96296296 1. ] mean value: 0.9847578347578348 key: test_accuracy value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333 1. 1. 1. 1. ] mean value: 0.8666666666666668 key: train_accuracy value: [1. 0.98076923 1. 0.98076923 1. 1. 1. 0.98076923 0.98113208 1. ] mean value: 0.9923439767779391 key: test_roc_auc value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333 1. 1. 1. 1. ] mean value: 0.8666666666666668 key: train_roc_auc value: [1. 0.98076923 1. 0.98076923 1. 1. 1. 0.98076923 0.98148148 1. ] mean value: 0.9923789173789175 key: test_jcc value: [0.66666667 1. 0.5 0.33333333 0.5 0.75 1. 1. 1. 1. ] mean value: 0.775 key: train_jcc value: [1. 0.96153846 1. 0.96153846 1. 1. 1. 0.96153846 0.96296296 1. ] mean value: 0.9847578347578348 key: TN value: 26 mean value: 26.0 key: FP value: 5 mean value: 5.0 key: FN value: 3 mean value: 3.0 key: TP value: 24 mean value: 24.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.95 Accuracy on Blind test: 0.98 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.01054978 0.01045513 0.00980616 0.00982642 0.00995708 0.0100565 0.00980353 0.00985241 0.00990748 0.00987434] mean value: 0.010008883476257325 key: score_time value: [0.00911832 0.00907159 0.00849557 0.00847793 0.00844884 0.00848508 0.00845075 0.00847316 0.00876307 0.00851512] mean value: 0.008629941940307617 key: test_mcc value: [ 0.33333333 0.70710678 0. -0.4472136 -0.4472136 0.4472136 0. -0.4472136 0.40824829 0.40824829] mean value: 0.09625095044476913 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.8 0.57142857 0. 0.5 0.5 0.57142857 0.5 0.66666667 0.5 ] mean value: 0.5276190476190477 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") [0.66666667 1. 0.5 0. 0.4 1. 0.5 0.4 0.5 1. ] mean value: 0.5966666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.66666667 0.66666667 0. 0.66666667 0.33333333 0.66666667 0.66666667 1. 0.33333333] mean value: 0.5666666666666667 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.83333333 0.5 0.33333333 0.33333333 0.66666667 0.5 0.33333333 0.6 0.6 ] mean value: 0.5366666666666665 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66666667 0.83333333 0.5 0.33333333 0.33333333 0.66666667 0.5 0.33333333 0.66666667 0.66666667] mean value: 0.55 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.66666667 0.4 0. 0.33333333 0.33333333 0.4 0.33333333 0.5 0.33333333] mean value: 0.38 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 15 mean value: 15.0 key: FP value: 13 mean value: 13.0 key: FN value: 14 mean value: 14.0 key: TP value: 16 mean value: 16.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.28 Accuracy on Blind test: 0.62 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.10271955 0.10487604 0.08293653 0.10259652 0.08842254 0.10237932 0.10384226 0.10312128 0.10364127 0.10579896] mean value: 0.10003342628479003 key: score_time value: [0.00865936 0.00893712 0.00901771 0.0088377 0.00872445 0.0088408 0.0089922 0.00864983 0.0091002 0.00891113] mean value: 0.00886704921722412 key: test_mcc value: [0.70710678 1. 0. 0.4472136 0. 0.70710678 1. 1. 1. 1. ] mean value: 0.6861427157873052 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 1. 0.57142857 0.5 0.57142857 0.85714286 1. 1. 1. 1. ] mean value: 0.8300000000000001 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.5 1. 0.5 0.75 1. 1. 1. 1. ] mean value: 0.875 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 1. 0.66666667 0.33333333 0.66666667 1. 1. 1. 1. 1. ] mean value: 0.8333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.83333333 1. 0.5 0.66666667 0.5 0.83333333 1. 1. 1. 1. ] mean value: 0.8333333333333334 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.83333333 1. 0.5 0.66666667 0.5 0.83333333 1. 1. 1. 1. ] mean value: 0.8333333333333334 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 1. 0.4 0.33333333 0.4 0.75 1. 1. 1. 1. ] mean value: 0.755 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 24 mean value: 24.0 key: FP value: 5 mean value: 5.0 key: FN value: 5 mean value: 5.0 key: TP value: 24 mean value: 24.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.95 Accuracy on Blind test: 0.98 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.00854087 0.00976038 0.00851274 0.00856066 0.00975871 0.00889325 0.00963783 0.00880289 0.00930786 0.01002502] mean value: 0.009180021286010743 key: score_time value: [0.00873137 0.00865483 0.0095017 0.00902653 0.00855947 0.00916696 0.00919914 0.00919485 0.00843978 0.00921845] mean value: 0.008969306945800781 key: test_mcc value: [ 0.4472136 0.4472136 -0.70710678 0. -0.33333333 -0.4472136 -0.4472136 0.4472136 -0.40824829 -0.61237244] mean value: -0.16138472451795804 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.75 0.5 0. 0.57142857 0.33333333 0.5 0.5 0.5 0. 0. ] mean value: 0.3654761904761905 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.6 1. 0. 0.5 0.33333333 0.4 0.4 1. 0. 0. ] mean value: 0.42333333333333334 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.33333333 0. 0.66666667 0.33333333 0.66666667 0.66666667 0.33333333 0. 0. ] mean value: 0.4 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.66666667 0.16666667 0.5 0.33333333 0.33333333 0.33333333 0.66666667 0.4 0.2 ] mean value: 0.42666666666666664 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66666667 0.66666667 0.16666667 0.5 0.33333333 0.33333333 0.33333333 0.66666667 0.33333333 0.25 ] mean value: 0.425 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.6 0.33333333 0. 0.4 0.2 0.33333333 0.33333333 0.33333333 0. 0. ] mean value: 0.2533333333333333 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 13 mean value: 13.0 key: FP value: 17 mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:356: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:357: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_CV['Resampling'] = rs_rus /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:362: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:363: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rus_BT['Resampling'] = rs_rus 17.0 key: FN value: 16 mean value: 16.0 key: TP value: 12 mean value: 12.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.14 Accuracy on Blind test: 0.5 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.01034141 0.0121088 0.01195264 0.01200914 0.02302814 0.01293015 0.0122416 0.01224279 0.01262784 0.01239896] mean value: 0.01318814754486084 key: score_time value: [0.01122975 0.01138377 0.01147366 0.01134562 0.02079654 0.01142693 0.01140499 0.01142883 0.01149654 0.01142836] mean value: 0.012341499328613281 key: test_mcc value: [0. 1. 1. 0. 0. 1. 0. 0.4472136 1. 1. ] mean value: 0.5447213595499958 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 0.96225045 1. 1. ] mean value: 0.9962250448649377 key: test_fscore value: [0.4 1. 1. 0.4 0.57142857 1. 0.4 0.75 1. 1. ] mean value: 0.7521428571428571 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 0.98113208 1. 1. ] mean value: 0.9981132075471699 key: test_precision value: [0.5 1. 1. 0.5 0.5 1. 0.5 0.6 1. 1. ] mean value: 0.76 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 0.96296296 1. 1. ] mean value: 0.9962962962962962 key: test_recall value: [0.33333333 1. 1. 0.33333333 0.66666667 1. 0.33333333 1. 1. 1. ] mean value: 0.7666666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.5 1. 1. 0.5 0.5 1. 0.5 0.66666667 1. 1. ] mean value: 0.7666666666666666 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 0.98076923 1. 1. ] mean value: 0.998076923076923 key: test_roc_auc value: [0.5 1. 1. 0.5 0.5 1. 0.5 0.66666667 1. 1. ] mean value: 0.7666666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 0.98076923 1. 1. ] mean value: 0.998076923076923 key: test_jcc value: [0.25 1. 1. 0.25 0.4 1. 0.25 0.6 1. 1. ] mean value: 0.675 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 0.96296296 1. 1. ] mean value: 0.9962962962962962 key: TN value: 22 mean value: 22.0 key: FP value: 7 mean value: 7.0 key: FN value: 7 mean value: 7.0 key: TP value: 22 mean value: 22.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.14 Accuracy on Blind test: 0.57 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.07401347 0.07836699 0.078619 0.07845759 0.07878184 0.07833385 0.07831335 0.07814956 0.07846451 0.07824159] mean value: 0.07797417640686036 key: score_time value: [0.01146913 0.01154041 0.01154804 0.01152658 0.01153302 0.01152396 0.0114727 0.01152277 0.01153612 0.01147652] mean value: 0.011514925956726074 key: test_mcc value: [0.33333333 1. 1. 0. 0. 1. 0. 0.4472136 0.66666667 1. ] mean value: 0.5447213595499958 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 0.96225045 1. 1. ] mean value: 0.9962250448649377 key: test_fscore value: [0.66666667 1. 1. 0.4 0.57142857 1. 0.4 0.75 0.8 1. ] mean value: 0.7588095238095238 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 0.98113208 1. 1. ] mean value: 0.9981132075471699 key: test_precision value: [0.66666667 1. 1. 0.5 0.5 1. 0.5 0.6 0.66666667 1. ] mean value: 0.7433333333333334 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 0.96296296 1. 1. ] mean value: 0.9962962962962962 key: test_recall value: [0.66666667 1. 1. 0.33333333 0.66666667 1. 0.33333333 1. 1. 1. ] mean value: 0.8 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 1. 1. 0.5 0.5 1. 0.5 0.66666667 0.8 1. ] mean value: 0.7633333333333333 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 0.98076923 1. 1. ] mean value: 0.998076923076923 key: test_roc_auc value: [0.66666667 1. 1. 0.5 0.5 1. 0.5 0.66666667 0.83333333 1. ] mean value: 0.7666666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 0.98076923 1. 1. ] mean value: 0.998076923076923 key: test_jcc value: [0.5 1. 1. 0.25 0.4 1. 0.25 0.6 0.66666667 1. ] mean value: 0.6666666666666667 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 0.96296296 1. 1. ] mean value: 0.9962962962962962 key: TN value: 21 mean value: 21.0 key: FP value: 6 mean value: 6.0 key: FN value: 8 mean value: 8.0 key: TP value: 23 mean value: 23.0 key: trainingY_neg value: 29 mean value: 29.0 key: trainingY_pos value: 29 mean value: 29.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.18 Accuracy on Blind test: 0.6 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( PASS: sorting df by score that is mapped onto the order I want ============================================================== Running several classification models (n): 24 List of models: ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)) ('Gaussian NB', GaussianNB()) ('Naive Bayes', BernoulliNB()) ('K-Nearest Neighbors', KNeighborsClassifier()) ('SVC', SVC(random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ('LDA', LinearDiscriminantAnalysis()) ('Multinomial', MultinomialNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42)) ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=10)) ================================================================ Running classifier: 1 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02776575 0.02869368 0.02271485 0.02522039 0.02290487 0.02502799 0.02245021 0.05590749 0.02390695 0.03170252] mean value: 0.028629469871520995 key: score_time value: [0.01155543 0.01159883 0.01160884 0.01166868 0.01157451 0.01157188 0.01154232 0.01169586 0.01163626 0.01184702] mean value: 0.011629962921142578 key: test_mcc value: [1. 0.65465367 0.81649658 0.65465367 0.81649658 0.81649658 0.40824829 0.81649658 0.65465367 0. ] mean value: 0.6638195626298699 key: train_mcc value: [0.93541435 0.93541435 0.93541435 0.95650071 0.95555556 0.91201231 0.88910845 0.93541435 0.95555556 0.95555556] mean value: 0.9365945526249924 key: test_fscore value: [1. 0.83333333 0.88888889 0.83333333 0.88888889 0.90909091 0.72727273 0.90909091 0.83333333 0.54545455] mean value: 0.8368686868686869 key: train_fscore value: [0.96774194 0.96774194 0.96774194 0.97826087 0.97777778 0.95652174 0.94382022 0.96774194 0.97777778 0.97777778] mean value: 0.9682903908683571 key: test_precision value: [1. 0.71428571 1. 0.71428571 1. 0.83333333 0.66666667 0.83333333 0.71428571 0.5 ] mean value: 0.7976190476190477 key: train_precision value: [0.9375 0.9375 0.9375 0.95744681 0.97777778 0.93617021 0.95454545 0.9375 0.97777778 0.97777778] mean value: 0.9531495809155384 key: test_recall value: [1. 1. 0.8 1. 0.8 1. 0.8 1. 1. 0.6] mean value: 0.9 key: train_recall value: [1. 1. 1. 1. 0.97777778 0.97777778 0.93333333 1. 0.97777778 0.97777778] mean value: 0.9844444444444445 key: test_accuracy value: [1. 0.8 0.9 0.8 0.9 0.9 0.7 0.9 0.8 0.5] mean value: 0.82 key: train_accuracy value: [0.96666667 0.96666667 0.96666667 0.97777778 0.97777778 0.95555556 0.94444444 0.96666667 0.97777778 0.97777778] mean value: 0.9677777777777777 key: test_roc_auc value: [1. 0.8 0.9 0.8 0.9 0.9 0.7 0.9 0.8 0.5] mean value: 0.82 key: train_roc_auc value: [0.96666667 0.96666667 0.96666667 0.97777778 0.97777778 0.95555556 0.94444444 0.96666667 0.97777778 0.97777778] mean value: 0.9677777777777778 key: test_jcc value: [1. 0.71428571 0.8 0.71428571 0.8 0.83333333 0.57142857 0.83333333 0.71428571 0.375 ] mean value: 0.7355952380952381 key: train_jcc value: [0.9375 0.9375 0.9375 0.95744681 0.95652174 0.91666667 0.89361702 0.9375 0.95652174 0.95652174] mean value: 0.9387295713845207 key: TN value: 37 mean value: 37.0 key: FP value: 5 mean value: 5.0 key: FN value: 13 mean value: 13.0 key: TP value: 45 mean value: 45.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.37 Accuracy on Blind test: 0.72 Running classifier: 2 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.56633496 0.55252409 0.53593993 0.68272257 0.54451585 0.53557205 0.54859138 0.74416137 0.54470611 0.56081605] mean value: 0.5815884351730347 key: score_time value: [0.01307201 0.01323223 0.01380658 0.01179194 0.01315022 0.01324105 0.01309943 0.0132587 0.01182222 0.01328754] mean value: 0.01297619342803955 key: test_mcc value: [1. 0.81649658 0.6 0.65465367 0.81649658 0.65465367 1. 1. 0.40824829 0.21821789] mean value: 0.7168766683971262 key: train_mcc value: [0.97801929 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9978019293843652 key: test_fscore value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [1. 0.90909091 0.8 0.83333333 0.88888889 0.83333333 1. 1. 0.72727273 0.66666667] mean value: 0.8658585858585859 key: train_fscore value: [0.98901099 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9989010989010989 key: test_precision value: [1. 0.83333333 0.8 0.71428571 1. 0.71428571 1. 1. 0.66666667 0.57142857] mean value: 0.8300000000000001 key: train_precision value: [0.97826087 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9978260869565216 key: test_recall value: [1. 1. 0.8 1. 0.8 1. 1. 1. 0.8 0.8] mean value: 0.9200000000000002 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.9 0.8 0.8 0.9 0.8 1. 1. 0.7 0.6] mean value: 0.85 key: train_accuracy value: [0.98888889 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9988888888888889 key: test_roc_auc value: [1. 0.9 0.8 0.8 0.9 0.8 1. 1. 0.7 0.6] mean value: 0.85 key: train_roc_auc value: [0.98888889 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9988888888888889 key: test_jcc value: [1. 0.83333333 0.66666667 0.71428571 0.8 0.71428571 1. 1. 0.57142857 0.5 ] mean value: 0.78 key: train_jcc value: [0.97826087 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9978260869565216 key: TN value: 39 mean value: 39.0 key: FP value: 4 mean value: 4.0 key: FN value: 11 mean value: 11.0 key: TP value: 46 mean value: 46.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.25 Accuracy on Blind test: 0.68 Running classifier: 3 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01261568 0.01144147 0.00931716 0.00832486 0.00855613 0.00928593 0.00807047 0.00803423 0.00799537 0.00809693] mean value: 0.009173822402954102 key: score_time value: [0.01160741 0.00956869 0.00901723 0.00961471 0.00909519 0.00840259 0.00830054 0.00824618 0.00833321 0.00828719] mean value: 0.009047293663024902 key: test_mcc value: [ 0.5 0. -0.33333333 0.5 0. 0.40824829 -0.33333333 0.40824829 0. 0. ] mean value: 0.11498299142610595 key: train_mcc value: [0.45226702 0.53931937 0.53931937 0.57906602 0.43808583 0.69509522 0.48001536 0.56568542 0.51066218 0.68957028] mean value: 0.5489086080136931 key: test_fscore value: [0.76923077 0.66666667 0.57142857 0.76923077 0.54545455 0.72727273 0.57142857 0.72727273 0.54545455 0.54545455] mean value: 0.6438894438894438 key: train_fscore value: [0.75675676 0.78899083 0.78899083 0.79569892 0.74509804 0.85416667 0.76635514 0.8 0.77669903 0.84782609] mean value: 0.792058229501609 key: test_precision value: [0.625 0.5 0.44444444 0.625 0.5 0.66666667 0.44444444 0.66666667 0.5 0.5 ] mean value: 0.5472222222222223 key: train_precision value: [0.63636364 0.671875 0.671875 0.77083333 0.66666667 0.80392157 0.66129032 0.7 0.68965517 0.82978723] mean value: 0.7102267934028078 key: test_recall value: [1. 1. 0.8 1. 0.6 0.8 0.8 0.8 0.6 0.6] mean value: 0.7999999999999999 key: train_recall value: [0.93333333 0.95555556 0.95555556 0.82222222 0.84444444 0.91111111 0.91111111 0.93333333 0.88888889 0.86666667] mean value: 0.9022222222222223 key: test_accuracy value: [0.7 0.5 0.4 0.7 0.5 0.7 0.4 0.7 0.5 0.5] mean value: 0.5599999999999999 key: train_accuracy value: [0.7 0.74444444 0.74444444 0.78888889 0.71111111 0.84444444 0.72222222 0.76666667 0.74444444 0.84444444] mean value: 0.7611111111111111 key: test_roc_auc value: [0.7 0.5 0.4 0.7 0.5 0.7 0.4 0.7 0.5 0.5] mean value: 0.5599999999999999 key: train_roc_auc value: [0.7 0.74444444 0.74444444 0.78888889 0.71111111 0.84444444 0.72222222 0.76666667 0.74444444 0.84444444] mean value: 0.7611111111111111 key: test_jcc value: [0.625 0.5 0.4 0.625 0.375 0.57142857 0.4 0.57142857 0.375 0.375 ] mean value: 0.4817857142857143 key: train_jcc value: [0.60869565 0.65151515 0.65151515 0.66071429 0.59375 0.74545455 0.62121212 0.66666667 0.63492063 0.73584906] mean value: 0.6570293265776243 key: TN value: 16 mean value: 16.0 key: FP value: 10 mean value: 10.0 key: FN value: 34 mean value: 34.0 key: TP value: 40 mean value: 40.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.39 Accuracy on Blind test: 0.65 Running classifier: 4 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.0094583 0.00919294 0.00911546 0.00833702 0.00844932 0.00869441 0.00868297 0.00884938 0.00957847 0.00917697] mean value: 0.008953523635864259 key: score_time value: [0.00851822 0.00911808 0.01154637 0.009516 0.00891685 0.00901747 0.0085547 0.00905323 0.00843143 0.00888371] mean value: 0.009155607223510743 key: test_mcc value: [0.81649658 0.65465367 0.2 0.2 0.5 0.40824829 0.65465367 0.2 0. 0. ] mean value: 0.3634052212807543 key: train_mcc value: [0.49897013 0.58137767 0.53346507 0.55555556 0.57792049 0.53346507 0.53346507 0.51111111 0.68888889 0.80178373] mean value: 0.5816002788156436 key: test_fscore value: [0.88888889 0.83333333 0.6 0.6 0.57142857 0.66666667 0.83333333 0.6 0.28571429 0.44444444] mean value: 0.6323809523809524 key: train_fscore value: [0.76767677 0.8 0.76923077 0.77777778 0.79120879 0.76404494 0.76404494 0.75555556 0.84444444 0.89655172] mean value: 0.7930535717672486 key: test_precision value: [1. 0.71428571 0.6 0.6 1. 0.75 0.71428571 0.6 0.5 0.5 ] mean value: 0.6978571428571428 key: train_precision value: [0.7037037 0.76 0.76086957 0.77777778 0.7826087 0.77272727 0.77272727 0.75555556 0.84444444 0.92857143] mean value: 0.7858985716377022 key: test_recall value: [0.8 1. 0.6 0.6 0.4 0.6 1. 0.6 0.2 0.4] mean value: 0.62 key: train_recall value: [0.84444444 0.84444444 0.77777778 0.77777778 0.8 0.75555556 0.75555556 0.75555556 0.84444444 0.86666667] mean value: 0.8022222222222222 key: test_accuracy value: [0.9 0.8 0.6 0.6 0.7 0.7 0.8 0.6 0.5 0.5] mean value: 0.67 key: train_accuracy value: [0.74444444 0.78888889 0.76666667 0.77777778 0.78888889 0.76666667 0.76666667 0.75555556 0.84444444 0.9 ] mean value: 0.7899999999999999 key: test_roc_auc value: [0.9 0.8 0.6 0.6 0.7 0.7 0.8 0.6 0.5 0.5] mean value: 0.67 key: train_roc_auc value: [0.74444444 0.78888889 0.76666667 0.77777778 0.78888889 0.76666667 0.76666667 0.75555556 0.84444444 0.9 ] mean value: 0.7899999999999999 key: test_jcc value: [0.8 0.71428571 0.42857143 0.42857143 0.4 0.5 0.71428571 0.42857143 0.16666667 0.28571429] mean value: 0.4866666666666667 key: train_jcc value: [0.62295082 0.66666667 0.625 0.63636364 0.65454545 0.61818182 0.61818182 0.60714286 0.73076923 0.8125 ] mean value: 0.6592302301523614 key: TN value: 36 mean value: 36.0 key: FP value: 19 mean value: 19.0 key: FN value: 14 mean value: 14.0 key: TP value: 31 mean value: 31.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.06 Accuracy on Blind test: 0.5 Running classifier: 5 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01051545 0.00867343 0.0085032 0.00867558 0.00853562 0.00811458 0.00812817 0.00796604 0.0099206 0.00903177] mean value: 0.008806443214416504 key: score_time value: [0.01103592 0.00978136 0.00917578 0.00924969 0.01515198 0.01001072 0.01416135 0.00911379 0.01416039 0.01609468] mean value: 0.011793565750122071 key: test_mcc value: [ 0.81649658 0.65465367 0.5 0. -0.2 0.2 0.5 0.40824829 0.40824829 -0.21821789] mean value: 0.3069428942327437 key: train_mcc value: [0.47863442 0.49897013 0.56056066 0.52421865 0.49103499 0.47087096 0.47863442 0.53990552 0.54433105 0.56056066] mean value: 0.5147721476862103 key: test_fscore value: [0.90909091 0.83333333 0.76923077 0.61538462 0.4 0.6 0.76923077 0.72727273 0.72727273 0.5 ] mean value: 0.6850815850815851 key: train_fscore value: [0.76 0.76767677 0.79166667 0.78 0.76923077 0.75 0.76 0.78350515 0.78787879 0.79166667] mean value: 0.7741624812758834 key: test_precision value: [0.83333333 0.71428571 0.625 0.5 0.4 0.6 0.625 0.66666667 0.66666667 0.42857143] mean value: 0.6059523809523809 key: train_precision value: [0.69090909 0.7037037 0.74509804 0.70909091 0.6779661 0.70588235 0.69090909 0.73076923 0.72222222 0.74509804] mean value: 0.7121648780671712 key: test_recall value: [1. 1. 1. 0.8 0.4 0.6 1. 0.8 0.8 0.6] mean value: 0.7999999999999999 key: train_recall value: [0.84444444 0.84444444 0.84444444 0.86666667 0.88888889 0.8 0.84444444 0.84444444 0.86666667 0.84444444] mean value: 0.848888888888889 key: test_accuracy value: [0.9 0.8 0.7 0.5 0.4 0.6 0.7 0.7 0.7 0.4] mean value: 0.6400000000000001 key: train_accuracy value: [0.73333333 0.74444444 0.77777778 0.75555556 0.73333333 0.73333333 0.73333333 0.76666667 0.76666667 0.77777778] mean value: 0.7522222222222222 key: test_roc_auc value: [0.9 0.8 0.7 0.5 0.4 0.6 0.7 0.7 0.7 0.4] mean value: 0.6400000000000001 key: train_roc_auc value: [0.73333333 0.74444444 0.77777778 0.75555556 0.73333333 0.73333333 0.73333333 0.76666667 0.76666667 0.77777778] mean value: 0.7522222222222222 key: test_jcc value: [0.83333333 0.71428571 0.625 0.44444444 0.25 0.42857143 0.625 0.57142857 0.57142857 0.33333333] mean value: 0.5396825396825397 key: train_jcc value: [0.61290323 0.62295082 0.65517241 0.63934426 0.625 0.6 0.61290323 0.6440678 0.65 0.65517241] mean value: 0.6317514157776494 key: TN value: 24 mean value: 24.0 key: FP value: 10 mean value: 10.0 key: FN value: 26 mean value: 26.0 key: TP value: 40 mean value: 40.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.07 Accuracy on Blind test: 0.48 Running classifier: 6 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.00882292 0.00860882 0.00860405 0.00861382 0.00862885 0.00865412 0.00877261 0.00858068 0.00863194 0.00862861] mean value: 0.008654642105102538 key: score_time value: [0.00841212 0.0083425 0.00839615 0.00844121 0.00848246 0.00840759 0.00846624 0.00836325 0.00846195 0.00850344] mean value: 0.008427691459655762 key: test_mcc value: [0.5 0.81649658 0.65465367 0.40824829 0.5 0.65465367 0.40824829 0.6 0.40824829 0.2 ] mean value: 0.515054879373527 key: train_mcc value: [0.84465303 0.8001976 0.75724019 0.78086881 0.80498447 0.75724019 0.73624773 0.75724019 0.79036782 0.80498447] mean value: 0.7834024490981437 key: test_fscore value: [0.57142857 0.90909091 0.75 0.66666667 0.57142857 0.75 0.72727273 0.8 0.66666667 0.6 ] mean value: 0.7012554112554111 key: train_fscore value: [0.92134831 0.8988764 0.87356322 0.88372093 0.89411765 0.87356322 0.86046512 0.87356322 0.87804878 0.89411765] mean value: 0.8851384495390617 key: test_precision value: [1. 0.83333333 1. 0.75 1. 1. 0.66666667 0.8 0.75 0.6 ] mean value: 0.8400000000000001 key: train_precision value: [0.93181818 0.90909091 0.9047619 0.92682927 0.95 0.9047619 0.90243902 0.9047619 0.97297297 0.95 ] mean value: 0.9257436070850705 key: test_recall value: [0.4 1. 0.6 0.6 0.4 0.6 0.8 0.8 0.6 0.6] mean value: 0.6399999999999999 key: train_recall value: [0.91111111 0.88888889 0.84444444 0.84444444 0.84444444 0.84444444 0.82222222 0.84444444 0.8 0.84444444] mean value: 0.8488888888888889 key: test_accuracy value: [0.7 0.9 0.8 0.7 0.7 0.8 0.7 0.8 0.7 0.6] mean value: 0.74 key: train_accuracy value: [0.92222222 0.9 0.87777778 0.88888889 0.9 0.87777778 0.86666667 0.87777778 0.88888889 0.9 ] mean value: 0.89 key: test_roc_auc value: [0.7 0.9 0.8 0.7 0.7 0.8 0.7 0.8 0.7 0.6] mean value: 0.74 key: train_roc_auc value: [0.92222222 0.9 0.87777778 0.88888889 0.9 0.87777778 0.86666667 0.87777778 0.88888889 0.9 ] mean value: 0.89 key: test_jcc value: [0.4 0.83333333 0.6 0.5 0.4 0.6 0.57142857 0.66666667 0.5 0.42857143] mean value: 0.55 key: train_jcc value: [0.85416667 0.81632653 0.7755102 0.79166667 0.80851064 0.7755102 0.75510204 0.7755102 0.7826087 0.80851064] mean value: 0.7943422489254721 key: TN value: 42 mean value: 42.0 key: FP value: 18 mean value: 18.0 key: FN value: 8 mean value: 8.0 key: TP value: 32 mean value: 32.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.04 Accuracy on Blind test: 0.62 Running classifier: 7 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.46989155 0.45708156 0.47301435 0.51050806 0.4922812 0.58298516 0.6829381 0.55199766 0.33458424 0.63565779] mean value: 0.5190939664840698 key: score_time value: [0.01193571 0.0119791 0.01196957 0.01409888 0.01202154 0.01195478 0.01197529 0.01233053 0.01202965 0.01204038] mean value: 0.012233543395996093 key: test_mcc value: [1. 0.65465367 0.6 0.65465367 0.81649658 0.65465367 0.81649658 1. 0.65465367 0.21821789] mean value: 0.7069825734923353 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.83333333 0.8 0.83333333 0.88888889 0.83333333 0.90909091 1. 0.83333333 0.66666667] mean value: 0.8597979797979798 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.71428571 0.8 0.71428571 1. 0.71428571 0.83333333 1. 0.71428571 0.57142857] mean value: 0.8061904761904763 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.8 1. 0.8 1. 1. 1. 1. 0.8] mean value: 0.9400000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.8 0.8 0.8 0.9 0.8 0.9 1. 0.8 0.6] mean value: 0.8400000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.8 0.8 0.8 0.9 0.8 0.9 1. 0.8 0.6] mean value: 0.8400000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.71428571 0.66666667 0.71428571 0.8 0.71428571 0.83333333 1. 0.71428571 0.5 ] mean value: 0.7657142857142858 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 37 mean value: 37.0 key: FP value: 3 mean value: 3.0 key: FN value: 13 mean value: 13.0 key: TP value: 47 mean value: 47.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.12 Accuracy on Blind test: 0.6 Running classifier: 8 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01784778 0.01463175 0.00978041 0.00983381 0.00975132 0.00979137 0.01004624 0.00982881 0.00951791 0.00948429] mean value: 0.011051368713378907 key: score_time value: [0.01210833 0.01203465 0.0088129 0.00876093 0.00856209 0.00864077 0.00851083 0.00841141 0.00842595 0.00868106] mean value: 0.009294891357421875 key: test_mcc value: [1. 1. 0.81649658 1. 0.6 0.65465367 0.81649658 0.6 0.6 0.81649658] mean value: 0.7904143413491156 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 0.88888889 1. 0.8 0.83333333 0.90909091 0.8 0.8 0.90909091] mean value: 0.894040404040404 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 1. 0.8 0.71428571 0.83333333 0.8 0.8 0.83333333] mean value: 0.878095238095238 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.8 1. 0.8 1. 1. 0.8 0.8 1. ] mean value: 0.9200000000000002 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 0.9 1. 0.8 0.8 0.9 0.8 0.8 0.9] mean value: 0.89 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.9 1. 0.8 0.8 0.9 0.8 0.8 0.9] mean value: 0.89 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 0.8 1. 0.66666667 0.71428571 0.83333333 0.66666667 0.66666667 0.83333333] mean value: 0.818095238095238 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 43 mean value: 43.0 key: FP value: 4 mean value: 4.0 key: FN value: 7 mean value: 7.0 key: TP value: 46 mean value: 46.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.89 Accuracy on Blind test: 0.95 Running classifier: 9 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.08306623 0.08243489 0.08078671 0.08000588 0.08049583 0.08101439 0.08041883 0.08083487 0.07945704 0.0797565 ] mean value: 0.08082711696624756 key: score_time value: [0.01788282 0.01666999 0.017452 0.01670957 0.01666594 0.01666021 0.01663876 0.01678586 0.01667166 0.01662517] mean value: 0.01687619686126709 key: test_mcc value: [0.5 0.81649658 0.40824829 0.65465367 0.65465367 0.81649658 0.33333333 0.6 0.40824829 0.2 ] mean value: 0.5392130417532466 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.57142857 0.90909091 0.72727273 0.83333333 0.75 0.88888889 0.71428571 0.8 0.66666667 0.6 ] mean value: 0.7460966810966811 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.83333333 0.66666667 0.71428571 1. 1. 0.55555556 0.8 0.75 0.6 ] mean value: 0.7919841269841269 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.4 1. 0.8 1. 0.6 0.8 1. 0.8 0.6 0.6] mean value: 0.76 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.7 0.9 0.7 0.8 0.8 0.9 0.6 0.8 0.7 0.6] mean value: 0.75 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.7 0.9 0.7 0.8 0.8 0.9 0.6 0.8 0.7 0.6] mean value: 0.75 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.4 0.83333333 0.57142857 0.71428571 0.6 0.8 0.55555556 0.66666667 0.5 0.42857143] mean value: 0.606984126984127 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 37 mean value: 37.0 key: FP value: 12 mean value: 12.0 key: FN value: 13 mean value: 13.0 key: TP value: 38 mean value: 38.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.49 Accuracy on Blind test: 0.78 Running classifier: 10 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00919604 0.00881147 0.00983024 0.00988555 0.00845003 0.00822234 0.00817466 0.00906372 0.0087781 0.00858617] mean value: 0.008899831771850586 key: score_time value: [0.00908685 0.00836706 0.00952148 0.01046968 0.00823426 0.00821829 0.00818181 0.00913 0.00822067 0.00824046] mean value: 0.008767056465148925 key: test_mcc value: [-0.21821789 0.81649658 0.40824829 0.65465367 0.6 0.81649658 0.81649658 0.6 0.2 0.40824829] mean value: 0.5102422104182889 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.90909091 0.72727273 0.83333333 0.8 0.90909091 0.90909091 0.8 0.6 0.66666667] mean value: 0.7654545454545454 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.42857143 0.83333333 0.66666667 0.71428571 0.8 0.83333333 0.83333333 0.8 0.6 0.75 ] mean value: 0.7259523809523809 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.6 1. 0.8 1. 0.8 1. 1. 0.8 0.6 0.6] mean value: 0.82 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.4 0.9 0.7 0.8 0.8 0.9 0.9 0.8 0.6 0.7] mean value: 0.75 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.4 0.9 0.7 0.8 0.8 0.9 0.9 0.8 0.6 0.7] mean value: 0.75 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0.83333333 0.57142857 0.71428571 0.66666667 0.83333333 0.83333333 0.66666667 0.42857143 0.5 ] mean value: 0.6380952380952382 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 34 mean value: 34.0 key: FP value: 9 mean value: 9.0 key: FN value: 16 mean value: 16.0 key: TP value: 41 mean value: 41.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.56 Accuracy on Blind test: 0.8 Running classifier: 11 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.12863898 1.05638361 1.04797721 1.22669673 1.21202326 1.01976037 1.10352778 1.02443147 1.04925394 1.07379055] mean value: 1.094248390197754 key: score_time value: [0.10170126 0.09575319 0.09421945 0.10475135 0.0917902 0.09088731 0.0953505 0.08893514 0.09012246 0.09573841] mean value: 0.0949249267578125 key: test_mcc value: [0.65465367 0.81649658 0.6 0.81649658 0.65465367 1. 0.81649658 0.81649658 0.6 0.2 ] mean value: 0.6975293665126858 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.75 0.90909091 0.8 0.90909091 0.75 1. 0.90909091 0.88888889 0.8 0.6 ] mean value: 0.8316161616161617 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.83333333 0.8 0.83333333 1. 1. 0.83333333 1. 0.8 0.6 ] mean value: 0.8700000000000001 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.6 1. 0.8 1. 0.6 1. 1. 0.8 0.8 0.6] mean value: 0.8200000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.9 0.8 0.9 0.8 1. 0.9 0.9 0.8 0.6] mean value: 0.8400000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8 0.9 0.8 0.9 0.8 1. 0.9 0.9 0.8 0.6] mean value: 0.8400000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.6 0.83333333 0.66666667 0.83333333 0.6 1. 0.83333333 0.8 0.66666667 0.42857143] mean value: 0.7261904761904763 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 43 mean value: 43.0 key: FP value: 9 mean value: 9.0 key: FN value: 7 mean value: 7.0 key: TP value: 41 mean value: 41.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.55 Accuracy on Blind test: 0.8 Running classifier: 12 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.83751202 0.89611864 0.86344433 0.82031369 0.8383913 0.87373209 0.85556459 0.87743616 0.84090114 0.83468747] mean value: 0.8538101434707641 key: score_time value: [0.1183486 0.17216015 0.1454649 0.1703856 0.16838622 0.20413017 0.18524504 0.20958233 0.19292188 0.14789653] mean value: 0.17145214080810547 key: test_mcc value: [1. 0.81649658 0.81649658 0.81649658 0.81649658 1. 0.81649658 0.6 0.6 0.2 ] mean value: 0.748248290463863 key: train_mcc value: [0.97801929 1. 1. 1. 0.95555556 0.95650071 0.95555556 0.95555556 0.93356387 0.93356387] mean value: 0.9668314417898023 key: test_fscore value: [1. 0.90909091 0.88888889 0.90909091 0.88888889 1. 0.90909091 0.8 0.8 0.6 ] mean value: 0.8705050505050507 key: train_fscore value: [0.98876404 1. 1. 1. 0.97777778 0.97727273 0.97777778 0.97777778 0.96629213 0.96629213] mean value: 0.9831954375212801 key: test_precision value: [1. 0.83333333 1. 0.83333333 1. 1. 0.83333333 0.8 0.8 0.6 ] mean value: 0.8700000000000001 key: train_precision value: [1. 1. 1. 1. 0.97777778 1. 0.97777778 0.97777778 0.97727273 0.97727273] mean value: 0.9887878787878787 key: test_recall value: [1. 1. 0.8 1. 0.8 1. 1. 0.8 0.8 0.6] mean value: 0.8800000000000001 key: train_recall value: [0.97777778 1. 1. 1. 0.97777778 0.95555556 0.97777778 0.97777778 0.95555556 0.95555556] mean value: 0.9777777777777779 key: test_accuracy value: [1. 0.9 0.9 0.9 0.9 1. 0.9 0.8 0.8 0.6] mean value: 0.8700000000000001 key: train_accuracy value: [0.98888889 1. 1. 1. 0.97777778 0.97777778 0.97777778 0.97777778 0.96666667 0.96666667] mean value: 0.9833333333333334 key: test_roc_auc value: [1. 0.9 0.9 0.9 0.9 1. 0.9 0.8 0.8 0.6] mean value: 0.8700000000000001 key: train_roc_auc value: [0.98888889 1. 1. 1. 0.97777778 0.97777778 0.97777778 0.97777778 0.96666667 0.96666667] mean value: 0.9833333333333334 key: test_jcc value: [1. 0.83333333 0.8 0.83333333 0.8 1. 0.83333333 0.66666667 0.66666667 0.42857143] mean value: 0.7861904761904762 key: train_jcc value: [0.97777778 1. 1. 1. 0.95652174 0.95555556 0.95652174 0.95652174 0.93478261 0.93478261] mean value: 0.9672463768115943 key: TN value: 43 mean value: 43.0 key: FP value: 6 mean value: 6.0 key: FN value: 7 mean value: 7.0 key: TP value: 44 mean value: 44.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.67 Accuracy on Blind test: 0.85 Running classifier: 13 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_p... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.10435414 0.03400135 0.04029894 0.03453851 0.03732896 0.03765655 0.0378046 0.0363245 0.03529716 0.03576374] mean value: 0.0433368444442749 key: score_time value: [0.01012611 0.00994301 0.01033235 0.01006889 0.01035452 0.01092505 0.01093435 0.01010299 0.01055551 0.01002288] mean value: 0.010336565971374511 key: test_mcc value: [1. 0.81649658 0.81649658 0.81649658 1. 0.81649658 0.81649658 0.81649658 1. 1. ] mean value: 0.8898979485566356 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.90909091 0.88888889 0.90909091 1. 0.90909091 0.90909091 0.90909091 1. 1. ] mean value: 0.9434343434343434 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.83333333 1. 0.83333333 1. 0.83333333 0.83333333 0.83333333 1. 1. ] mean value: 0.9166666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.8 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9800000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.9 0.9 0.9 1. 0.9 0.9 0.9 1. 1. ] mean value: 0.9400000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.9 0.9 0.9 1. 0.9 0.9 0.9 1. 1. ] mean value: 0.9400000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.83333333 0.8 0.83333333 1. 0.83333333 0.83333333 0.83333333 1. 1. ] mean value: 0.8966666666666667 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 45 mean value: 45.0 key: FP value: 1 mean value: 1.0 key: FN value: 5 mean value: 5.0 key: TP value: 49 mean value: 49.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.89 Accuracy on Blind test: 0.95 Running classifier: 14 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.0220542 0.03752089 0.05885172 0.03988481 0.04153132 0.04049897 0.04043698 0.04112816 0.04124546 0.03902507] mean value: 0.04021775722503662 key: score_time value: [0.01765323 0.01775956 0.02387667 0.0141654 0.0216701 0.02373743 0.0230279 0.02206993 0.0226841 0.01399088] mean value: 0.02006351947784424 key: test_mcc value: [0.40824829 0.33333333 0.40824829 0.6 0.6 0.6 0.81649658 0.40824829 0.21821789 0.40824829] mean value: 0.48010409663525044 key: train_mcc value: [1. 1. 1. 1. 0.97801929 1. 1. 1. 1. 1. ] mean value: 0.9978019293843652 key: test_fscore value: [0.66666667 0.71428571 0.72727273 0.8 0.8 0.8 0.90909091 0.72727273 0.66666667 0.66666667] mean value: 0.7477922077922079 key: train_fscore value: [1. 1. 1. 1. 0.98876404 1. 1. 1. 1. 1. ] mean value: 0.9988764044943821 key: test_precision value: [0.75 0.55555556 0.66666667 0.8 0.8 0.8 0.83333333 0.66666667 0.57142857 0.75 ] mean value: 0.7193650793650793 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.6 1. 0.8 0.8 0.8 0.8 1. 0.8 0.8 0.6] mean value: 0.8 key: train_recall value: [1. 1. 1. 1. 0.97777778 1. 1. 1. 1. 1. ] mean value: 0.9977777777777778 key: test_accuracy value: [0.7 0.6 0.7 0.8 0.8 0.8 0.9 0.7 0.6 0.7] mean value: 0.73 key: train_accuracy value: [1. 1. 1. 1. 0.98888889 1. 1. 1. 1. 1. ] mean value: 0.9988888888888889 key: test_roc_auc value: [0.7 0.6 0.7 0.8 0.8 0.8 0.9 0.7 0.6 0.7] mean value: 0.73 key: train_roc_auc value: [1. 1. 1. 1. 0.98888889 1. 1. 1. 1. 1. ] mean value: 0.9988888888888889 key: test_jcc value: [0.5 0.55555556 0.57142857 0.66666667 0.66666667 0.66666667 0.83333333 0.57142857 0.5 0.5 ] mean value: 0.6031746031746031 key: train_jcc value: [1. 1. 1. 1. 0.97777778 1. 1. 1. 1. 1. ] mean value: 0.9977777777777778 key: TN value: 33 mean value: 33.0 key: FP value: 10 mean value: 10.0 key: FN value: 17 mean value: 17.0 key: TP value: 40 mean value: 40.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.5 Running classifier: 15 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.0215857 0.00902247 0.00941372 0.00821018 0.00910091 0.00885034 0.00864315 0.00913239 0.00845718 0.00905919] mean value: 0.010147523880004884 key: score_time value: [0.01546955 0.00928783 0.00828052 0.00871253 0.00915241 0.00861788 0.00836587 0.00900817 0.00822067 0.00899863] mean value: 0.00941140651702881 key: test_mcc value: [ 1. 0.2 0.21821789 0. 0.40824829 0.21821789 0.21821789 -0.2 -0.6 -0.40824829] mean value: 0.10546536707079768 key: train_mcc value: [0.51161666 0.4260261 0.35564338 0.42263985 0.4260261 0.44992127 0.4472136 0.40249224 0.42222222 0.4454354 ] mean value: 0.4309236824763807 key: test_fscore value: [1. 0.6 0.5 0.54545455 0.72727273 0.5 0.66666667 0.4 0.2 0.36363636] mean value: 0.5503030303030303 key: train_fscore value: [0.75 0.69047619 0.6741573 0.70454545 0.69047619 0.69879518 0.70588235 0.68235294 0.71111111 0.71264368] mean value: 0.7020440402981192 key: test_precision value: [1. 0.6 0.66666667 0.5 0.66666667 0.66666667 0.57142857 0.4 0.2 0.33333333] mean value: 0.5604761904761905 key: train_precision value: [0.76744186 0.74358974 0.68181818 0.72093023 0.74358974 0.76315789 0.75 0.725 0.71111111 0.73809524] mean value: 0.7344734005964116 key: test_recall value: [1. 0.6 0.4 0.6 0.8 0.4 0.8 0.4 0.2 0.4] mean value: 0.56 key: train_recall value: [0.73333333 0.64444444 0.66666667 0.68888889 0.64444444 0.64444444 0.66666667 0.64444444 0.71111111 0.68888889] mean value: 0.6733333333333335 key: test_accuracy value: [1. 0.6 0.6 0.5 0.7 0.6 0.6 0.4 0.2 0.3] mean value: 0.55 key: train_accuracy value: [0.75555556 0.71111111 0.67777778 0.71111111 0.71111111 0.72222222 0.72222222 0.7 0.71111111 0.72222222] mean value: 0.7144444444444444 key: test_roc_auc value: [1. 0.6 0.6 0.5 0.7 0.6 0.6 0.4 0.2 0.3] mean value: 0.55 key: train_roc_auc value: [0.75555556 0.71111111 0.67777778 0.71111111 0.71111111 0.72222222 0.72222222 0.7 0.71111111 0.72222222] mean value: 0.7144444444444444 key: test_jcc value: [1. 0.42857143 0.33333333 0.375 0.57142857 0.33333333 0.5 0.25 0.11111111 0.22222222] mean value: 0.4125 key: train_jcc value: [0.6 0.52727273 0.50847458 0.54385965 0.52727273 0.53703704 0.54545455 0.51785714 0.55172414 0.55357143] mean value: 0.5412523971790637 key: TN value: 27 mean value: 27.0 key: FP value: 22 mean value: 22.0 key: FN value: 23 mean value: 23.0 key: TP value: 28 mean value: 28.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.14 Accuracy on Blind test: 0.48 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01105547 0.01381016 0.01291037 0.01365805 0.01394439 0.01359439 0.01429915 0.01413941 0.01459932 0.01359248] mean value: 0.013560318946838379 key: score_time value: [0.00817347 0.0110867 0.01157665 0.01226854 0.01200342 0.01189685 0.01202917 0.01192331 0.01188517 0.01133728] mean value: 0.01141805648803711 key: test_mcc value: [1. 0.65465367 0.6 0.65465367 0.81649658 0.65465367 0.33333333 0.81649658 0.65465367 0. ] mean value: 0.6184941178020693 key: train_mcc value: [0.97801929 0.93541435 0.97801929 0.89442719 0.97801929 0.97801929 0.79772404 0.97801929 1. 0.97801929] mean value: 0.9495681335972777 key: test_fscore value: [1. 0.83333333 0.8 0.83333333 0.88888889 0.83333333 0.71428571 0.90909091 0.83333333 0.54545455] mean value: 0.8191053391053391 key: train_fscore value: [0.98876404 0.96774194 0.98901099 0.94736842 0.98876404 0.98901099 0.9 0.98901099 1. 0.98901099] mean value: 0.9748682402468098 key: test_precision value: [1. 0.71428571 0.8 0.71428571 1. 0.71428571 0.55555556 0.83333333 0.71428571 0.5 ] mean value: 0.7546031746031747 key: train_precision value: [1. 0.9375 0.97826087 0.9 1. 0.97826087 0.81818182 0.97826087 1. 0.97826087] mean value: 0.9568725296442688 key: test_recall value: [1. 1. 0.8 1. 0.8 1. 1. 1. 1. 0.6] mean value: 0.9199999999999999 key: train_recall value: [0.97777778 1. 1. 1. 0.97777778 1. 1. 1. 1. 1. ] mean value: 0.9955555555555555 key: test_accuracy value: [1. 0.8 0.8 0.8 0.9 0.8 0.6 0.9 0.8 0.5] mean value: 0.79 key: train_accuracy value: [0.98888889 0.96666667 0.98888889 0.94444444 0.98888889 0.98888889 0.88888889 0.98888889 1. 0.98888889] mean value: 0.9733333333333334 key: test_roc_auc value: [1. 0.8 0.8 0.8 0.9 0.8 0.6 0.9 0.8 0.5] mean value: 0.79 key: train_roc_auc value: [0.98888889 0.96666667 0.98888889 0.94444444 0.98888889 0.98888889 0.88888889 0.98888889 1. 0.98888889] mean value: 0.9733333333333333 key: test_jcc value: [1. 0.71428571 0.66666667 0.71428571 0.8 0.71428571 0.55555556 0.83333333 0.71428571 0.375 ] mean value: 0.7087698412698413 key: train_jcc value: [0.97777778 0.9375 0.97826087 0.9 0.97777778 0.97826087 0.81818182 0.97826087 1. 0.97826087] mean value: 0.9524280851998244 key: TN value: 33 mean value: 33.0 key: FP value: 4 mean value: 4.0 key: FN value: 17 mean value: 17.0 key: TP value: 46 mean value: 46.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.31 Accuracy on Blind test: 0.65 Running classifier: 17 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.00869608 0.01205468 0.0119195 0.01242995 0.01249123 0.01260424 0.0124619 0.01284695 0.01233792 0.01319003] mean value: 0.01210324764251709 key: score_time value: [0.00816989 0.01123571 0.0111959 0.01133227 0.0114491 0.01152349 0.01132083 0.01165223 0.01228309 0.01179123] mean value: 0.01119537353515625 key: test_mcc value: [1. 0.5 0.81649658 0.65465367 0.65465367 0.65465367 1. 0.33333333 0.40824829 0.5 ] mean value: 0.6522039216848854 key: train_mcc value: [0.95650071 0.91201231 1. 1. 0.93541435 1. 0.91201231 0.39223227 0.82962978 0.72486118] mean value: 0.8662662908352459 key: test_fscore value: [1. 0.76923077 0.88888889 0.83333333 0.75 0.83333333 1. 0.33333333 0.72727273 0.76923077] mean value: 0.7904623154623154 key: train_fscore value: [0.97826087 0.95652174 1. 1. 0.96551724 1. 0.95454545 0.42105263 0.9047619 0.86538462] mean value: 0.9046044456345884 key: test_precision value: [1. 0.625 1. 0.71428571 1. 0.71428571 1. 1. 0.66666667 0.625 ] mean value: 0.8345238095238094 key: train_precision value: [0.95744681 0.93617021 1. 1. 1. 1. 0.97674419 1. 0.97435897 0.76271186] mean value: 0.9607432046088862 key: test_recall value: [1. 1. 0.8 1. 0.6 1. 1. 0.2 0.8 1. ] mean value: 0.8399999999999999 key: train_recall value: [1. 0.97777778 1. 1. 0.93333333 1. 0.93333333 0.26666667 0.84444444 1. ] mean value: 0.8955555555555555 key: test_accuracy value: [1. 0.7 0.9 0.8 0.8 0.8 1. 0.6 0.7 0.7] mean value: 0.8 key: train_accuracy value: [0.97777778 0.95555556 1. 1. 0.96666667 1. 0.95555556 0.63333333 0.91111111 0.84444444] mean value: 0.9244444444444445 key: test_roc_auc value: [1. 0.7 0.9 0.8 0.8 0.8 1. 0.6 0.7 0.7] mean value: 0.8 key: train_roc_auc value: [0.97777778 0.95555556 1. 1. 0.96666667 1. 0.95555556 0.63333333 0.91111111 0.84444444] mean value: 0.9244444444444445 key: test_jcc value: [1. 0.625 0.8 0.71428571 0.6 0.71428571 1. 0.2 0.57142857 0.625 ] mean value: 0.6849999999999999 key: train_jcc value: [0.95744681 0.91666667 1. 1. 0.93333333 1. 0.91304348 0.26666667 0.82608696 0.76271186] mean value: 0.8575955774366694 key: TN value: 38 mean value: 38.0 key: FP value: 8 mean value: 8.0 key: FN value: 12 mean value: 12.0 key: TP value: 42 mean value: 42.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.26 Accuracy on Blind test: 0.65 Running classifier: 18 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.0937829 0.08539081 0.08131576 0.08085942 0.081738 0.07984424 0.08620453 0.08208632 0.08311081 0.0840354 ] mean value: 0.08383681774139404 key: score_time value: [0.01697683 0.01498771 0.01476073 0.0150857 0.01448298 0.01486278 0.01463366 0.01593375 0.0157733 0.01465559] mean value: 0.015215301513671875 key: test_mcc value: [1. 0.65465367 0.81649658 0.81649658 0.81649658 0.65465367 1. 0.81649658 0.6 1. ] mean value: 0.8175293665126858 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.83333333 0.88888889 0.90909091 0.90909091 0.83333333 1. 0.90909091 0.8 1. ] mean value: 0.9082828282828282 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.71428571 1. 0.83333333 0.83333333 0.71428571 1. 0.83333333 0.8 1. ] mean value: 0.8728571428571428 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.8 1. 1. 1. 1. 1. 0.8 1. ] mean value: 0.96 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.8 0.9 0.9 0.9 0.8 1. 0.9 0.8 1. ] mean value: 0.9 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.8 0.9 0.9 0.9 0.8 1. 0.9 0.8 1. ] mean value: 0.9 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.71428571 0.8 0.83333333 0.83333333 0.71428571 1. 0.83333333 0.66666667 1. ] mean value: 0.8395238095238096 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 42 mean value: 42.0 key: FP value: 2 mean value: 2.0 key: FN value: 8 mean value: 8.0 key: TP value: 48 mean value: 48.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.89 Accuracy on Blind test: 0.95 Running classifier: 19 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.02795291 0.029001 0.03056097 0.02775407 0.02784705 0.02628183 0.02716231 0.02904415 0.03186536 0.02968788] mean value: 0.028715753555297853 key: score_time value: [0.01913691 0.01803756 0.01878023 0.01808381 0.01785254 0.02040362 0.02372098 0.02432561 0.02418256 0.02507067] mean value: 0.02095944881439209 key: test_mcc value: [1. 0.81649658 0.81649658 1. 0.81649658 0.81649658 0.81649658 0.81649658 0.81649658 1. ] mean value: 0.8715476066494082 key: train_mcc value: [1. 1. 1. 0.97801929 1. 1. 1. 1. 1. 1. ] mean value: 0.9978019293843652 key: test_fscore value: [1. 0.90909091 0.88888889 1. 0.88888889 0.90909091 0.90909091 0.90909091 0.88888889 1. ] mean value: 0.9303030303030303 key: train_fscore value: [1. 1. 1. 0.98876404 1. 1. 1. 1. 1. 1. ] mean value: 0.9988764044943821 key: test_precision value: [1. 0.83333333 1. 1. 1. 0.83333333 0.83333333 0.83333333 1. 1. ] mean value: 0.9333333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.8 1. 0.8 1. 1. 1. 0.8 1. ] mean value: 0.9400000000000001 key: train_recall value: [1. 1. 1. 0.97777778 1. 1. 1. 1. 1. 1. ] mean value: 0.9977777777777778 key: test_accuracy value: [1. 0.9 0.9 1. 0.9 0.9 0.9 0.9 0.9 1. ] mean value: 0.93 key: train_accuracy value: [1. 1. 1. 0.98888889 1. 1. 1. 1. 1. 1. ] mean value: 0.9988888888888889 key: test_roc_auc value: [1. 0.9 0.9 1. 0.9 0.9 0.9 0.9 0.9 1. ] mean value: 0.93 key: train_roc_auc value: [1. 1. 1. 0.98888889 1. 1. 1. 1. 1. 1. ] mean value: 0.9988888888888889 key: test_jcc value: [1. 0.83333333 0.8 1. 0.8 0.83333333 0.83333333 0.83333333 0.8 1. ] mean value: 0.8733333333333334 key: train_jcc value: [1. 1. 1. 0.97777778 1. 1. 1. 1. 1. 1. ] mean value: 0.9977777777777778 key: TN value: 46 mean value: 46.0 key: FP value: 3 mean value: 3.0 key: FN value: 4 mean value: 4.0 key: TP value: 47 mean value: 47.0 key: trainingY_neg /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.95 Accuracy on Blind test: 0.98 Running classifier: 20 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.01426291 0.01899099 0.06119943 0.03039908 0.03261042 0.03212762 0.01616931 0.01589775 0.01599836 0.0489819 ] mean value: 0.028663778305053712 key: score_time value: [0.01212502 0.01236367 0.01209068 0.01212645 0.01205349 0.01278925 0.01178861 0.01174092 0.01172686 0.01259255] mean value: 0.01213974952697754 key: test_mcc value: [0.21821789 0.81649658 0.40824829 0.65465367 0.65465367 1. 0.5 0.6 0.2 0.21821789] mean value: 0.5270487993279529 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.90909091 0.72727273 0.83333333 0.75 1. 0.76923077 0.8 0.6 0.66666667] mean value: 0.7555594405594406 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.83333333 0.66666667 0.71428571 1. 1. 0.625 0.8 0.6 0.57142857] mean value: 0.7477380952380951 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.4 1. 0.8 1. 0.6 1. 1. 0.8 0.6 0.8] mean value: 0.8 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.9 0.7 0.8 0.8 1. 0.7 0.8 0.6 0.6] mean value: 0.7499999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.6 0.9 0.7 0.8 0.8 1. 0.7 0.8 0.6 0.6] mean value: 0.75 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0.83333333 0.57142857 0.71428571 0.6 1. 0.625 0.66666667 0.42857143 0.5 ] mean value: 0.6272619047619048 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 35 mean value: 35.0 key: FP value: 10 mean value: 10.0 key: FN value: 15 mean value: 15.0 key: TP value: 40 mean value: 40.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.03 Accuracy on Blind test: 0.52 Running classifier: 21 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.19596338 0.1739645 0.1458776 0.17693067 0.1970973 0.17478251 0.19787335 0.19073057 0.1710403 0.17166138] mean value: 0.17959215641021728 key: score_time value: [0.00906062 0.0088172 0.00977397 0.00960827 0.00882506 0.00957179 0.00906038 0.00920892 0.00884843 0.00902319] mean value: 0.009179782867431641 key: test_mcc value: [1. 0.81649658 0.81649658 0.81649658 1. 0.65465367 0.81649658 0.81649658 1. 1. ] mean value: 0.8737136575346607 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.90909091 0.88888889 0.90909091 1. 0.83333333 0.90909091 0.90909091 1. 1. ] mean value: 0.9358585858585858 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.83333333 1. 0.83333333 1. 0.71428571 0.83333333 0.83333333 1. 1. ] mean value: 0.9047619047619048 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.8 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9800000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.9 0.9 0.9 1. 0.8 0.9 0.9 1. 1. ] mean value: 0.93 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.9 0.9 0.9 1. 0.8 0.9 0.9 1. 1. ] mean value: 0.93 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.83333333 0.8 0.83333333 1. 0.71428571 0.83333333 0.83333333 1. 1. ] mean value: 0.8847619047619049 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 44 mean value: 44.0 key: FP value: 1 mean value: 1.0 key: FN value: 6 mean value: 6.0 key: TP value: 49 mean value: 49.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.95 Accuracy on Blind test: 0.98 Running classifier: 22 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01014853 0.01743078 0.01398611 0.01395059 0.0140667 0.01543117 0.01630807 0.03985023 0.01398015 0.01428962] mean value: 0.016944193840026857 key: score_time value: [0.01163626 0.01192975 0.01302242 0.01280975 0.01169062 0.01553082 0.01562738 0.0132699 0.01170063 0.01315784] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) mean value: 0.013037538528442383 key: test_mcc value: [0.5 1. 0.81649658 0.81649658 0.65465367 0.81649658 1. 0.65465367 0.5 0.65465367] mean value: 0.7413450754907109 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.57142857 1. 0.88888889 0.88888889 0.75 0.88888889 1. 0.75 0.57142857 0.75 ] mean value: 0.805952380952381 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.4 1. 0.8 0.8 0.6 0.8 1. 0.6 0.4 0.6] mean value: 0.7 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.7 1. 0.9 0.9 0.8 0.9 1. 0.8 0.7 0.8] mean value: 0.85 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.7 1. 0.9 0.9 0.8 0.9 1. 0.8 0.7 0.8] mean value: 0.85 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.4 1. 0.8 0.8 0.6 0.8 1. 0.6 0.4 0.6] mean value: 0.7 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: TN value: 50 mean value: 50.0 key: FP value: 15 mean value: 15.0 key: FN value: 0 mean value: 0.0 key: TP value: 35 mean value: 35.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.65 Running classifier: 23 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02866507 0.03145432 0.03152037 0.03154778 0.03143167 0.03134251 0.03164673 0.03147626 0.03186297 0.03150988] mean value: 0.03124575614929199 key: score_time value: [0.01959229 0.0194881 0.0223341 0.01981282 0.01142001 0.01322937 0.01942945 0.02004266 0.02273679 0.02062583] mean value: 0.018871140480041505 key: test_mcc value: [1. 0.65465367 0.6 0.65465367 0.81649658 0.65465367 0.81649658 0.81649658 0.6 0. ] mean value: 0.661345075490711 key: train_mcc value: [1. 1. 1. 1. 1. 1. 0.97801929 1. 1. 0.97801929] mean value: 0.9956038587687303 key: test_fscore value: [1. 0.83333333 0.8 0.83333333 0.88888889 0.83333333 0.90909091 0.88888889 0.8 0.54545455] mean value: 0.8332323232323233 key: train_fscore value: [1. 1. 1. 1. 1. 1. 0.98901099 1. 1. 0.98901099] mean value: 0.9978021978021978 key: test_precision value: [1. 0.71428571 0.8 0.71428571 1. 0.71428571 0.83333333 1. 0.8 0.5 ] mean value: 0.8076190476190476 key: train_precision value: [1. 1. 1. 1. 1. 1. 0.97826087 1. 1. 0.97826087] mean value: 0.9956521739130434 key: test_recall value: [1. 1. 0.8 1. 0.8 1. 1. 0.8 0.8 0.6] mean value: 0.8800000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.8 0.8 0.8 0.9 0.8 0.9 0.9 0.8 0.5] mean value: 0.82 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 0.98888889 1. 1. 0.98888889] mean value: 0.9977777777777778 key: test_roc_auc value: [1. 0.8 0.8 0.8 0.9 0.8 0.9 0.9 0.8 0.5] mean value: 0.82 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 0.98888889 1. 1. 0.98888889] mean value: 0.9977777777777778 key: test_jcc value: [1. 0.71428571 0.66666667 0.71428571 0.8 0.71428571 0.83333333 0.8 0.66666667 0.375 ] mean value: 0.728452380952381 key: train_jcc value: [1. 1. 1. 1. 1. 1. 0.97826087 1. 1. 0.97826087] mean value: 0.9956521739130434 key: TN value: 38 mean value: 38.0 key: FP value: 6 mean value: 6.0 key: FN value: 12 mean value: 12.0 key: TP value: 44 mean value: 44.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: 0.16 Accuracy on Blind test: 0.62 Running classifier: 24 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.18429089 0.17889333 0.2217567 0.20420456 0.27858353 0.26535463 0.194134 0.18192577 0.20066214 0.18395448] mean value: 0.20937600135803222 key: score_time value: [0.02141452 0.0196619 0.02247691 0.02075529 0.01194358 0.02149749 0.02219009 0.02246118 0.0197928 0.0219295 ] mean value: 0.020412325859069824 key: test_mcc value: [1. 0.81649658 0.6 0.81649658 1. 0.21821789 0.81649658 0.6 0.6 0. ] mean value: 0.6467707633019171 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.97801929] mean value: 0.9978019293843652 key: test_fscore value: [1. 0.90909091 0.8 0.90909091 1. 0.66666667 0.90909091 0.8 0.8 0.54545455] mean value: 0.833939393939394 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.98901099] mean value: 0.9989010989010989 key: test_precision value: [1. 0.83333333 0.8 0.83333333 1. 0.57142857 0.83333333 0.8 0.8 0.5 ] mean value: 0.7971428571428572 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.97826087] mean value: 0.9978260869565216 key: test_recall value: [1. 1. 0.8 1. 1. 0.8 1. 0.8 0.8 0.6] mean value: 0.8800000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.9 0.8 0.9 1. 0.6 0.9 0.8 0.8 0.5] /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:432: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_CV['Data_source'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:433: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_CV['Resampling'] = rs_rouC /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:438: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_BT['Data_source'] = 'BT' /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:439: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy rouC_BT['Resampling'] = rs_rouC mean value: 0.82 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.98888889] mean value: 0.9988888888888889 key: test_roc_auc value: [1. 0.9 0.8 0.9 1. 0.6 0.9 0.8 0.8 0.5] mean value: 0.82 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.98888889] mean value: 0.9988888888888889 key: test_jcc value: [1. 0.83333333 0.66666667 0.83333333 1. 0.5 0.83333333 0.66666667 0.66666667 0.375 ] mean value: 0.7375 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.97826087] mean value: 0.9978260869565216 key: TN value: 38 mean value: 38.0 key: FP value: 6 mean value: 6.0 key: FN value: 12 mean value: 12.0 key: TP value: 44 mean value: 44.0 key: trainingY_neg value: 50 mean value: 50.0 key: trainingY_pos value: 50 mean value: 50.0 key: blindY_neg value: 26 mean value: 26.0 key: blindY_pos value: 14 mean value: 14.0 MCC on Blind test: -0.03 Accuracy on Blind test: 0.52 PASS: sorting df by score that is mapped onto the order I want Concatenating dfs with different resampling methods [WF]: 70/30 No. of dfs combining: 10 The sampling methods are: Resampling Logistic Regression none Logistic Regression smnc Logistic Regression ros Logistic Regression rus Logistic Regression rouC PASS: 10 dfs successfully combined nrows in combined_df_wf: 240 ncols in combined_df_wf: 9 Concatenating dfs with different resampling methods: 70/30 No. of dfs combining: 5 The sampling methods are: Resampling training_size 0 none 79 24 smnc 100 48 ros 100 72 rus 58 96 rouC 100 PASS: 5 dfs successfully combined nrows in combined_df: 120 ncols in combined_df: 32 File successfully written: /home/tanu/git/Data/streptomycin/output/ml/tts_7030/gid_baselineC_7030.csv File successfully written: /home/tanu/git/Data/streptomycin/output/ml/tts_7030/gid_baselineC_ext_7030.csv