/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_rt.py:550: 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 No. of numerical features: 167 No. of categorical features: 7 index: 0 ind: 1 Mask count check: True index: 1 ind: 2 Mask count check: True Original Data Counter({0: 409, 1: 3}) Data dim: (412, 174) ------------------------------------------------------------- Successfully split data: REVERSE training imputed values: training set actual values: blind test set Train data size: (412, 174) Test data size: (119, 174) y_train numbers: Counter({0: 409, 1: 3}) y_train ratio: 136.33333333333334 y_test_numbers: Counter({0: 76, 1: 43}) y_test ratio: 1.7674418604651163 ------------------------------------------------------------- Simple Random OverSampling Counter({0: 409, 1: 409}) (818, 174) Simple Random UnderSampling Counter({0: 3, 1: 3}) (6, 174) Simple Combined Over and UnderSampling Counter({0: 409, 1: 409}) (818, 174) SMOTE_NC OverSampling Counter({0: 409, 1: 409}) (818, 174) ##################################################################### Running ML analysis: REVERSE training Gene name: gid Drug name: streptomycin Output directory: /home/tanu/git/Data/streptomycin/output/ml/tts_rt/ Sanity checks: Total input features: 174 Training data size: (412, 174) Test data size: (119, 174) Target feature numbers (training data): Counter({0: 409, 1: 3}) Target features ratio (training data: 136.33333333333334 Target feature numbers (test data): Counter({0: 76, 1: 43}) Target features ratio (test data): 1.7674418604651163 ##################################################################### ================================================================ Strucutral features (n): 35 These are: Common stablity features: ['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity'] FoldX columns: ['electro_rr', 'electro_mm', 'electro_sm', 'electro_ss', 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss', 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss', 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss', 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss', 'volumetric_rr', 'volumetric_mm', 'volumetric_ss'] Other struc columns: ['rsa', 'kd_values', 'rd_values'] ================================================================ AAindex features (n): 123 These are: ['ALTS910101', 'AZAE970101', 'AZAE970102', 'BASU010101', 'BENS940101', 'BENS940102', 'BENS940103', 'BENS940104', 'BETM990101', 'BLAJ010101', 'BONM030101', 'BONM030102', 'BONM030103', 'BONM030104', 'BONM030105', 'BONM030106', 'BRYS930101', 'CROG050101', 'CSEM940101', 'DAYM780301', 'DAYM780302', 'DOSZ010101', 'DOSZ010102', 'DOSZ010103', 'DOSZ010104', 'FEND850101', 'FITW660101', 'GEOD900101', 'GIAG010101', 'GONG920101', 'GRAR740104', 'HENS920101', 'HENS920102', 'HENS920103', 'HENS920104', 'JOHM930101', 'JOND920103', 'JOND940101', 'KANM000101', 'KAPO950101', 'KESO980101', 'KESO980102', 'KOLA920101', 'KOLA930101', 'KOSJ950100_RSA_SST', 'KOSJ950100_SST', 'KOSJ950110_RSA', 'KOSJ950115', 'LEVJ860101', 'LINK010101', 'LIWA970101', 'LUTR910101', 'LUTR910102', 'LUTR910103', 'LUTR910104', 'LUTR910105', 'LUTR910106', 'LUTR910107', 'LUTR910108', 'LUTR910109', 'MCLA710101', 'MCLA720101', 'MEHP950102', 'MICC010101', 'MIRL960101', 'MIYS850102', 'MIYS850103', 'MIYS930101', 'MIYS960101', 'MIYS960102', 'MIYS960103', 'MIYS990106', 'MIYS990107', 'MIYT790101', 'MOHR870101', 'MOOG990101', 'MUET010101', 'MUET020101', 'MUET020102', 'NAOD960101', 'NGPC000101', 'NIEK910101', 'NIEK910102', 'OGAK980101', 'OVEJ920100_RSA', 'OVEJ920101', 'OVEJ920102', 'OVEJ920103', 'PRLA000101', 'PRLA000102', 'QUIB020101', 'QU_C930101', 'QU_C930102', 'QU_C930103', 'RIER950101', 'RISJ880101', 'RUSR970101', 'RUSR970102', 'RUSR970103', 'SIMK990101', 'SIMK990102', 'SIMK990103', 'SIMK990104', 'SIMK990105', 'SKOJ000101', 'SKOJ000102', 'SKOJ970101', 'TANS760101', 'TANS760102', 'THOP960101', 'TOBD000101', 'TOBD000102', 'TUDE900101', 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'] ================================================================ Evolutionary features (n): 3 These are: ['consurf_score', 'snap2_score', 'provean_score'] ================================================================ Genomic features (n): 6 These are: ['maf', 'logorI'] ['lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'] ================================================================ Categorical features (n): 7 These are: ['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'] ================================================================ Pass: No. of features match ##################################################################### Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) List of models: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5. warnings.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/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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5. warnings.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/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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5. warnings.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/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)) [('Logistic Regression', LogisticRegression(random_state=42)), ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)), ('Gaussian NB', GaussianNB()), ('Naive Bayes', BernoulliNB()), ('K-Nearest Neighbors', KNeighborsClassifier()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.01989913 0.02139759 0.02002597 0.03451586 0.0280056 0.03632236 0.02027631 0.02962422 0.03100157 0.03250861] mean value: 0.027357721328735353 key: score_time value: [0.0128715 0.01213503 0.01457286 0.01133895 0.01129723 0.01150632 0.01147532 0.01419973 0.01247072 0.01238894] mean value: 0.012425661087036133 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [0.99459459 0.99459459 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99460916] mean value: 0.9927194580024769 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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)) [0.32015514 0.29606748 0.34469795 0.32714677 0.5100379 0.35257006 0.34146309 0.32645774 0.33970666 0.30980587] mean value: 0.34681086540222167 key: score_time value: [0.01249838 0.01282454 0.01188016 0.01201582 0.01188278 0.01193213 0.01159549 0.01159835 0.01162362 0.01247168] mean value: 0.012032294273376464 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [0.99459459 0.99459459 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99460916] mean value: 0.9927194580024769 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Gaussian NB Model func: GaussianNB() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01610732 0.01534462 0.01008058 0.01067209 0.01061916 0.01147008 0.01083493 0.01097155 0.01086569 0.01091909] mean value: 0.011788511276245117 key: score_time value: [0.0125947 0.01148891 0.00836134 0.00904775 0.0091002 0.00894094 0.00898957 0.00901532 0.00915742 0.01016784] mean value: 0.00968639850616455 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan 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. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 Accuracy on Blind test: 0.64 Model_name: Naive Bayes Model func: BernoulliNB() List of models: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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)) [('Logistic Regression', LogisticRegression(random_state=42)), ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)), ('Gaussian NB', GaussianNB()), ('Naive Bayes', BernoulliNB()), ('K-Nearest Neighbors', KNeighborsClassifier()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01143622 0.01087689 0.01108122 0.01111841 0.01095366 0.01134276 0.01103449 0.01116729 0.01100588 0.01114798] mean value: 0.011116480827331543 key: score_time value: [0.00971389 0.00984168 0.00832176 0.00884438 0.0087328 0.00878572 0.00879002 0.00871754 0.00878549 0.00984311] mean value: 0.009037637710571289 key: test_mcc value: [-0.02439024 0. nan nan nan nan nan nan nan 0. ] mean value: nan key: train_mcc value: [-0.00543478 -0.00666528 -0.00815217 -0.00815217 -0.0066472 -0.00815217 -0.00815217 -0.00815217 -0.00815217 -0.0066472 ] mean value: -0.007430750394168724 key: test_accuracy value: [0.95238095 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [0.98918919 0.98648649 0.98382749 0.98382749 0.98652291 0.98382749 0.98382749 0.98382749 0.98382749 0.98652291] mean value: 0.9851686457346835 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_roc_auc value: [0.48780488 0.5 nan nan nan nan nan nan nan 0.5 ] mean value: nan key: train_roc_auc value: [0.49728261 0.49592391 0.49592391 0.49592391 0.49728261 0.49592391 0.49592391 0.49592391 0.49592391 0.49593496] mean value: 0.4961967568045246 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: -0.07 Accuracy on Blind test: 0.63 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01051664 0.0111444 0.01057911 0.01073694 0.01086092 0.01030827 0.01071453 0.01029611 0.01004815 0.00915384] mean value: 0.010435891151428223 key: score_time value: [0.08815837 0.01870036 0.01156998 0.01183891 0.01103091 0.01112247 0.01270485 0.01529336 0.01064134 0.01371527] mean value: 0.02047758102416992 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [0.99459459 0.99459459 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99460916] mean value: 0.9927194580024769 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: SVM Model func: SVC(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.01050568 0.01019382 0.01052141 0.01041746 0.01076007 0.01149678 0.01037335 0.01086855 0.01035285 0.01040316] mean value: 0.010589313507080079 key: score_time value: [0.00910854 0.00907183 0.00823593 0.00835204 0.00827742 0.00894523 0.00806546 0.00815535 0.00801182 0.0088501 ] mean value: 0.008507370948791504 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [0.99459459 0.99459459 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99460916] mean value: 0.9927194580024769 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) List of models: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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)) [('Logistic Regression', LogisticRegression(random_state=42)), ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)), ('Gaussian NB', GaussianNB()), ('Naive Bayes', BernoulliNB()), ('K-Nearest Neighbors', KNeighborsClassifier()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.07598901 0.08371949 0.0635035 0.06438303 0.06469774 0.06213498 0.06427479 0.06431317 0.06999135 0.07021499] mean value: 0.06832220554351806 key: score_time value: [0.01199293 0.01225996 0.0116899 0.01158714 0.01154232 0.01144671 0.01235509 0.01183105 0.01171947 0.0125463 ] mean value: 0.011897087097167969 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [0.99459459 0.99459459 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99460916] mean value: 0.9927194580024769 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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)) [0.01785231 0.01330876 0.01328421 0.01276445 0.0128417 0.01568699 0.01443124 0.01690936 0.01593995 0.01157117] mean value: 0.014459013938903809 key: score_time value: [0.01500154 0.00937605 0.00824142 0.00831437 0.00889111 0.00872159 0.00888252 0.00857997 0.00813174 0.00908661] mean value: 0.009322690963745116 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan 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. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 Accuracy on Blind test: 0.65 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.08993912 0.08948255 0.0866437 0.08457899 0.08424997 0.08384418 0.08424282 0.08384633 0.08527565 0.08414817] mean value: 0.08562514781951905 key: score_time value: [0.01871276 0.0188036 0.01643777 0.02006221 0.01625156 0.0163331 0.0162921 0.01657963 0.01643276 0.01721597] mean value: 0.017312145233154295 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan 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. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) List of models: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( [('Logistic Regression', LogisticRegression(random_state=42)), ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)), ('Gaussian NB', GaussianNB()), ('Naive Bayes', BernoulliNB()), ('K-Nearest Neighbors', KNeighborsClassifier()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01054931 0.00981259 0.00974107 0.00977802 0.00951934 0.00969315 0.01019955 0.00981641 0.00991702 0.00960732] mean value: 0.00986337661743164 key: score_time value: [0.00950408 0.00891519 0.00787663 0.0080781 0.00800753 0.00796795 0.00864196 0.00863695 0.00802159 0.00875759] mean value: 0.008440756797790527 key: test_mcc value: [-0.03492151 0. nan nan nan nan nan nan nan -0.025 ] mean value: nan key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.92857143 0.97619048 nan nan nan nan nan nan nan 0.95121951] mean value: nan key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.47560976 0.5 nan nan nan nan nan nan nan 0.4875 ] mean value: nan 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. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.07 Accuracy on Blind test: 0.63 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep 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(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.04075599 1.02801013 1.07141137 1.08398533 1.13270688 1.07735205 1.07241106 1.07471728 1.07248068 1.03925109] mean value: 1.0693081855773925 key: score_time value: [0.09357929 0.09518886 0.09432316 0.09397125 0.08665419 0.08706093 0.08933854 0.08832955 0.08816171 0.09343171] mean value: 0.09100391864776611 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan 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. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 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) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000...05', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', '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))]) /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/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this 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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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)) key: fit_time value: [1.84526539 0.94256401 0.87784743 0.87131786 0.87920451 0.88232422 0.87456799 0.91457176 0.89835405 0.87264204] mean value: 0.9858659267425537 key: score_time value: [0.2224977 0.1898675 0.21597195 0.26002526 0.21300793 0.2356112 0.20910764 0.22935915 0.21669412 0.21950126] mean value: 0.22116436958312988 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [0.99459459 0.99459459 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99460916] mean value: 0.9927194580024769 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Naive Bayes Model func: BernoulliNB() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.02475333 0.0101583 0.01128387 0.01099753 0.01108003 0.01017022 0.0107851 0.00999808 0.01097155 0.01101136] mean value: 0.012120938301086426 key: score_time value: [0.00987434 0.00949407 0.00875926 0.00866365 0.00858641 0.00872087 0.00876784 0.00811195 0.00882792 0.00923276] mean value: 0.008903908729553222 key: test_mcc value: [-0.02439024 0. nan nan nan nan nan nan nan 0. ] mean value: nan key: train_mcc value: [-0.00543478 -0.00666528 -0.00815217 -0.00815217 -0.0066472 -0.00815217 -0.00815217 -0.00815217 -0.00815217 -0.0066472 ] mean value: -0.007430750394168724 key: test_accuracy value: [0.95238095 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [0.98918919 0.98648649 0.98382749 0.98382749 0.98652291 0.98382749 0.98382749 0.98382749 0.98382749 0.98652291] mean value: 0.9851686457346835 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_roc_auc value: [0.48780488 0.5 nan nan nan nan nan nan nan 0.5 ] mean value: nan key: train_roc_auc value: [0.49728261 0.49592391 0.49592391 0.49592391 0.49728261 0.49592391 0.49592391 0.49592391 0.49592391 0.49593496] mean value: 0.4961967568045246 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: -0.07 Accuracy on Blind test: 0.63 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) List of models: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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)) [('Logistic Regression', LogisticRegression(random_state=42)), ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)), ('Gaussian NB', GaussianNB()), ('Naive Bayes', BernoulliNB()), ('K-Nearest Neighbors', KNeighborsClassifier()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000... 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: [0.15044928 0.04897809 0.05639958 0.06074142 0.08801484 0.05432439 0.05890536 0.06162691 0.06117535 0.05631852] mean value: 0.06969337463378907 key: score_time value: [0.01093197 0.01076651 0.00965357 0.00998425 0.01001692 0.00999784 0.01029325 0.01003051 0.00980496 0.011235 ] mean value: 0.010271477699279784 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [0.70614799 0.70614799 0.81538947 0.81538947 0.81538947 1. 0.81538947 0.81538947 0.81538947 1. ] mean value: 0.8304632782242238 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [0.9972973 0.9972973 0.99730458 0.99730458 0.99730458 1. 0.99730458 0.99730458 0.99730458 1. ] mean value: 0.997842208785605 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [0.66666667 0.66666667 0.8 0.8 0.8 1. 0.8 0.8 0.8 1. ] mean value: 0.8133333333333334 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [0.5 0.5 0.66666667 0.66666667 0.66666667 1. 0.66666667 0.66666667 0.66666667 1. ] mean value: 0.7 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan key: train_roc_auc value: [0.75 0.75 0.83333333 0.83333333 0.83333333 1. 0.83333333 0.83333333 0.83333333 1. ] mean value: 0.85 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [0.5 0.5 0.66666667 0.66666667 0.66666667 1. 0.66666667 0.66666667 0.66666667 1. ] mean value: 0.7 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: LDA Model func: LinearDiscriminantAnalysis() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.04444814 0.05598116 0.06279087 0.03509378 0.04921556 0.07959771 0.06728888 0.06242537 0.07381535 0.03813124] mean value: 0.05687880516052246 key: score_time value: [0.01246095 0.02204037 0.02578568 0.01153469 0.01158452 0.02235889 0.02250266 0.02209353 0.02209496 0.0125103 ] mean value: 0.01849665641784668 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [1. 1. 0.86484794 0.86484794 0.86484794 0.86484794 1. 0.86484794 1. 0.81538947] mean value: 0.9139629158315105 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [1. 1. 0.99730458 0.99730458 0.99730458 0.99730458 1. 0.99730458 1. 0.99730458] mean value: 0.9983827493261456 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [1. 1. 0.85714286 0.85714286 0.85714286 0.85714286 1. 0.85714286 1. 0.8 ] mean value: 0.9085714285714286 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [1. 1. 0.75 0.75 0.75 0.75 1. 0.75 1. 0.66666667] mean value: 0.8416666666666667 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan key: train_roc_auc value: [1. 1. 0.9986413 0.9986413 0.9986413 0.9986413 1. 0.9986413 1. 0.99864499] mean value: 0.9991851508188996 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [1. 1. 0.75 0.75 0.75 0.75 1. 0.75 1. 0.66666667] mean value: 0.8416666666666667 MCC on Blind test: 0.1 Accuracy on Blind test: 0.65 Model_name: Multinomial Model func: MultinomialNB() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01979399 0.01152015 0.01133204 0.01063061 0.01144385 0.01150703 0.01080608 0.01222038 0.01035595 0.01057076] mean value: 0.012018084526062012 key: score_time value: [0.01043391 0.0104506 0.00911331 0.00911403 0.00901508 0.00888371 0.00888872 0.00870442 0.00886321 0.01030731] mean value: 0.009377431869506837 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0.57578775 0. ] mean value: 0.05757877486813449 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [0.99459459 0.99459459 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99460916 0.99460916] mean value: 0.9929889997814526 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0.5 0. ] mean value: 0.05 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.] mean value: 0.1 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0.33333333 0. ] mean value: 0.03333333333333333 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.66666667 0.5 ] mean value: 0.5166666666666666 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc 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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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. 0. 0. 0. 0. 0. 0. 0. 0.33333333 0. ] mean value: 0.03333333333333333 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01436138 0.01534724 0.01509571 0.01730561 0.01619768 0.01917768 0.01716328 0.02114773 0.01738644 0.0191133 ] mean value: 0.017229604721069335 key: score_time value: [0.00999451 0.01163411 0.01093435 0.01128221 0.01092696 0.0112462 0.0111506 0.01127481 0.0135498 0.01213241] mean value: 0.011412596702575684 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [1. 0.70614799 0.81538947 1. 1. 0.57578775 1. 0.81538947 0.81538947 1. ] mean value: 0.8728104139802464 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [1. 0.9972973 0.99730458 1. 1. 0.99460916 1. 0.99730458 0.99730458 1. ] mean value: 0.9983820208348511 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [1. 0.66666667 0.8 1. 1. 0.5 1. 0.8 0.8 1. ] mean value: 0.8566666666666667 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [1. 0.5 0.66666667 1. 1. 0.33333333 1. 0.66666667 0.66666667 1. ] mean value: 0.7833333333333333 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan key: train_roc_auc value: [1. 0.75 0.83333333 1. 1. 0.66666667 1. 0.83333333 0.83333333 1. ] mean value: 0.8916666666666666 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [1. 0.5 0.66666667 1. 1. 0.33333333 1. 0.66666667 0.66666667 1. ] mean value: 0.7833333333333333 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01883221 0.01770854 0.01782966 0.01542687 0.01819515 0.01625967 0.01974487 0.01578569 0.01943088 0.01426744] mean value: 0.017348098754882812 key: score_time value: [0.01210117 0.0125072 0.01142359 0.01086974 0.01098657 0.01108551 0.01127458 0.011127 0.01064634 0.01213956] mean value: 0.011416125297546386 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [0. 0. 1. 1. 1. 1. 0.57578775 0.57578775 1. 1. ] mean value: 0.715157549736269 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [0.99459459 0.99459459 1. 1. 1. 1. 0.99460916 0.99460916 1. 1. ] mean value: 0.997840751803016 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [0. 0. 1. 1. 1. 1. 0.5 0.5 1. 1. ] mean value: 0.7 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [0. 0. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 0.8 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [0. 0. 1. 1. 1. 1. 0.33333333 0.33333333 1. 1. ] mean value: 0.6666666666666666 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan key: train_roc_auc value: [0.5 0.5 1. 1. 1. 1. 0.66666667 0.66666667 1. 1. ] mean value: 0.8333333333333334 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [0. 0. 1. 1. 1. 1. 0.33333333 0.33333333 1. 1. ] mean value: 0.6666666666666666 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.16987848 0.15895891 0.14807296 0.15208459 0.15941596 0.15608859 0.14817023 0.15044117 0.14882445 0.15114546] mean value: 0.15430808067321777 key: score_time value: [0.01673746 0.01536226 0.01484203 0.01564717 0.01588035 0.01446342 0.01494956 0.01494145 0.01467395 0.01594186] mean value: 0.015343952178955077 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan 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. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 Accuracy on Blind test: 0.65 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) List of models: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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)) [('Logistic Regression', LogisticRegression(random_state=42)), ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)), ('Gaussian NB', GaussianNB()), ('Naive Bayes', BernoulliNB()), ('K-Nearest Neighbors', KNeighborsClassifier()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.05544281 0.06112909 0.0427177 0.06485868 0.04066658 0.05549884 0.05135918 0.06852889 0.06360698 0.06171417] mean value: 0.05655229091644287 key: score_time value: [0.02798557 0.02249169 0.0232358 0.01667023 0.02339268 0.02715111 0.03077722 0.03415179 0.03425574 0.04068136] mean value: 0.02807931900024414 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [1. 1. 0.57578775 0.57578775 0.57578775 1. 0.81538947 0.57578775 1. 0. ] mean value: 0.711854046113585 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [1. 1. 0.99460916 0.99460916 0.99460916 1. 0.99730458 0.99460916 1. 0.99460916] mean value: 0.9970350404312669 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [1. 1. 0.5 0.5 0.5 1. 0.8 0.5 1. 0. ] mean value: 0.68 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.] mean value: 0.9 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [1. 1. 0.33333333 0.33333333 0.33333333 1. 0.66666667 0.33333333 1. 0. ] mean value: 0.6 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan key: train_roc_auc value: [1. 1. 0.66666667 0.66666667 0.66666667 1. 0.83333333 0.66666667 1. 0.5 ] mean value: 0.7999999999999999 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [1. 1. 0.33333333 0.33333333 0.33333333 1. 0.66666667 0.33333333 1. 0. ] mean value: 0.6 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.09493756 0.12387276 0.14413285 0.13103056 0.12700105 0.14302993 0.10722637 0.10728407 0.13245082 0.1236937 ] mean value: 0.12346596717834472 key: score_time value: [0.02551651 0.02611494 0.02803993 0.02623105 0.02616787 0.02645826 0.02657771 0.0264411 0.02686644 0.02554941] mean value: 0.02639632225036621 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [0.70614799 0.70614799 0.81538947 0.81538947 0.81538947 0.81538947 0.81538947 0.81538947 0.81538947 1. ] mean value: 0.812002224865271 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [0.9972973 0.9972973 0.99730458 0.99730458 0.99730458 0.99730458 0.99730458 0.99730458 0.99730458 1. ] mean value: 0.9975726670066293 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [0.66666667 0.66666667 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1. ] mean value: 0.7933333333333333 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [0.5 0.5 0.66666667 0.66666667 0.66666667 0.66666667 0.66666667 0.66666667 0.66666667 1. ] mean value: 0.6666666666666666 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan key: train_roc_auc value: [0.75 0.75 0.83333333 0.83333333 0.83333333 0.83333333 0.83333333 0.83333333 0.83333333 1. ] mean value: 0.8333333333333333 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [0.5 0.5 0.66666667 0.66666667 0.66666667 0.66666667 0.66666667 0.66666667 0.66666667 1. ] mean value: 0.6666666666666666 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.37525511 0.36695719 0.36911511 0.36837983 0.36991143 0.51728988 0.37010503 0.52289772 0.52321172 0.20602083] mean value: 0.39891438484191893 key: score_time value: [0.00933504 0.00948024 0.00845313 0.00824428 0.00835323 0.00815487 0.00842738 0.00815463 0.00849557 0.009197 ] mean value: 0.008629536628723145 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan 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. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 Accuracy on Blind test: 0.65 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() List of models: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/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/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/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/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:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/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)) [('Logistic Regression', LogisticRegression(random_state=42)), ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)), ('Gaussian NB', GaussianNB()), ('Naive Bayes', BernoulliNB()), ('K-Nearest Neighbors', KNeighborsClassifier()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02627063 0.0297873 0.07492399 0.05087018 0.038692 0.03735232 0.03435063 0.03712821 0.02941561 0.02809024] mean value: 0.03868811130523682 key: score_time value: [0.01187539 0.03454638 0.0263474 0.01137161 0.02450061 0.0197978 0.02197385 0.01114798 0.01123142 0.01212859] mean value: 0.01849210262298584 key: test_mcc value: [-0.02439024 -0.03492151 nan nan nan nan nan nan nan -0.025 ] mean value: nan key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.95238095 0.92857143 nan nan nan nan nan nan nan 0.95121951] mean value: nan key: train_accuracy value: [0.99459459 0.99459459 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99460916] mean value: 0.9927194580024769 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_roc_auc value: [0.48780488 0.47560976 nan nan nan nan nan nan nan 0.4875 ] mean value: nan key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.04 Accuracy on Blind test: 0.64 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time /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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=10. warnings.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/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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=10. warnings.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/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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1592: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, "true nor predicted", "F-score is", len(true_sum)) /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: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true 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/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 767, in _score scores = scorer(estimator, X_test, y_test) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 106, in __call__ score = scorer._score(cached_call, estimator, *args, **kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_scorer.py", line 267, in _score return self._sign * self._score_func(y_true, y_pred, **self._kwargs) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 569, in roc_auc_score return _average_binary_score( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_base.py", line 75, in _average_binary_score return binary_metric(y_true, y_score, sample_weight=sample_weight) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_ranking.py", line 338, in _binary_roc_auc_score raise ValueError( ValueError: Only one class present in y_true. ROC AUC score is not defined in that case. warnings.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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=10. warnings.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/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/model_selection/_split.py:680: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=10. warnings.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/git/LSHTM_analysis/scripts/ml/./gid_rt.py:114: 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 baseline_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True) /home/tanu/git/LSHTM_analysis/scripts/ml/./gid_rt.py:117: 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 baseline_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True) value: [0.02520037 0.03303194 0.03606892 0.03627133 0.03032136 0.03611326 0.03608656 0.03004909 0.03638005 0.03393817] mean value: 0.03334610462188721 key: score_time value: [0.02206588 0.02527237 0.02517176 0.02519727 0.02531409 0.02258182 0.02516508 0.02220869 0.02191806 0.02567792] mean value: 0.024057292938232423 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [0.99459459 0.99459459 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99460916] mean value: 0.9927194580024769 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.24358463 0.24829793 0.2510066 0.25444627 0.25330687 0.26447606 0.27128458 0.30404878 0.25829268 0.25916886] mean value: 0.26079132556915285 key: score_time value: [0.02582049 0.02625751 0.02527237 0.02232337 0.02252817 0.0247674 0.02487659 0.02488422 0.02405882 0.02176929] mean value: 0.024255824089050294 key: test_mcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.97619048 0.97619048 nan nan nan nan nan nan nan 0.97560976] mean value: nan key: train_accuracy value: [0.99459459 0.99459459 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99191375 0.99460916] mean value: 0.9927194580024769 key: test_fscore value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_roc_auc value: [0.5 0.5 nan nan nan nan nan nan nan 0.5] mean value: nan key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [ 0. 0. nan nan nan nan nan nan nan 0.] mean value: nan key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02864671 0.02664042 0.02762151 0.0318346 0.02810979 0.03002357 0.02838254 0.0265522 0.02744389 0.02837777] mean value: 0.028363299369812012 key: score_time value: [0.01195574 0.01175928 0.01182771 0.01192236 0.01198316 0.0118866 0.01181173 0.01239872 0.01185322 0.01191878] mean value: 0.011931729316711426 key: test_mcc value: [1. 1. 0.97590007 1. 0.97590007 1. 1. 1. 0.97560976 1. ] mean value: 0.9927409901994627 key: train_mcc value: [0.99728629 0.99728629 0.99728629 0.99728629 1. 0.99728629 0.99728629 0.99728629 0.99728997 0.99728995] mean value: 0.9975583961405413 key: test_accuracy value: [1. 1. 0.98780488 1. 0.98780488 1. 1. 1. 0.98765432 1. ] mean value: 0.9963264077085215 key: train_accuracy value: [0.9986413 0.9986413 0.9986413 0.9986413 1. 0.9986413 0.9986413 0.9986413 0.99864315 0.99864315] mean value: 0.9987775426228541 key: test_fscore value: [1. 1. 0.98795181 1. 0.98765432 1. 1. 1. 0.98765432 1. ] mean value: 0.9963260449204224 key: train_fscore value: [0.99863946 0.99863946 0.99863946 0.99863946 1. 0.99863946 0.99863946 0.99863946 0.99864315 0.99863946] mean value: 0.9987758794155382 key: test_precision value: [1. 1. 0.97619048 1. 1. 1. 1. 1. 0.97560976 1. ] mean value: 0.9951800232288037 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [0.99728261 0.99728261 0.99728261 0.99728261 1. 0.99728261 0.99728261 0.99728261 0.99728997 0.99728261] mean value: 0.9975550842464946 key: test_roc_auc value: [1. 1. 0.98780488 1. 0.98780488 1. 1. 1. 0.98780488 1. ] mean value: 0.9963414634146341 key: train_roc_auc value: [0.9986413 0.9986413 0.9986413 0.9986413 1. 0.9986413 0.9986413 0.9986413 0.99864499 0.9986413 ] mean value: 0.9987775421232474 key: test_jcc value: [1. 1. 0.97619048 1. 0.97560976 1. 1. 1. 0.97560976 1. ] mean value: 0.9927409988385598 key: train_jcc value: [0.99728261 0.99728261 0.99728261 0.99728261 1. 0.99728261 0.99728261 0.99728261 0.99728997 0.99728261] mean value: 0.9975550842464946 MCC on Blind test: 0.04 Accuracy on Blind test: 0.64 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 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( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.70233035 0.524055 0.48847866 0.48064637 0.50587416 0.51395321 0.51212144 0.46806359 0.46835923 0.67188501] mean value: 0.5335767030715942 key: score_time value: [0.01204348 0.01221061 0.01208615 0.01207471 0.01226497 0.01208091 0.01211357 0.01209903 0.01210117 0.01218176] mean value: 0.012125635147094726 key: test_mcc value: [1. 1. 1. 1. 0.97590007 1. 0.97590007 0.97590007 0.97560976 1. ] mean value: 0.9903309974943161 key: train_mcc value: [0.99728629 0.99728629 0.99728629 0.99728629 1. 1. 1. 0.99456522 0.99728997 0.99728995] mean value: 0.9978290306424038 key: test_accuracy value: [1. 1. 1. 1. 0.98780488 1. 0.98780488 0.98780488 0.98765432 1. ] mean value: 0.9951068955133996 key: train_accuracy value: [0.9986413 0.9986413 0.9986413 0.9986413 1. 1. 1. 0.99728261 0.99864315 0.99864315] mean value: 0.9989134121880715 key: test_fscore value: [1. 1. 1. 1. 0.98765432 1. 0.98795181 0.98795181 0.98765432 1. ] mean value: 0.995121225643314 key: train_fscore value: [0.99863946 0.99863946 0.99863946 0.99863946 1. 1. 1. 0.99728261 0.99864315 0.99863946] mean value: 0.9989123035504096 key: test_precision value: [1. 1. 1. 1. 1. 1. 0.97619048 0.97619048 0.97560976 1. ] mean value: 0.9927990708478514 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 0.99728261 1. 1. ] mean value: 0.9997282608695652 key: test_recall value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [0.99728261 0.99728261 0.99728261 0.99728261 1. 1. 1. 0.99728261 0.99728997 0.99728261] mean value: 0.9980985625073642 key: test_roc_auc value: [1. 1. 1. 1. 0.98780488 1. 0.98780488 0.98780488 0.98780488 1. ] mean value: 0.9951219512195122 key: train_roc_auc value: [0.9986413 0.9986413 0.9986413 0.9986413 1. 1. 1. 0.99728261 0.99864499 0.9986413 ] mean value: 0.9989134116884648 key: test_jcc value: [1. 1. 1. 1. 0.97560976 1. 0.97619048 0.97619048 0.97560976 1. ] mean value: 0.9903600464576074 key: train_jcc value: [0.99728261 0.99728261 0.99728261 0.99728261 1. 1. 1. 0.99457995 0.99728997 0.99728261] mean value: 0.9978282962177448 MCC on Blind test: 0.1 Accuracy on Blind test: 0.65 Model_name: Gaussian NB Model func: GaussianNB() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01617408 0.01320672 0.01180935 0.01129079 0.01151109 0.0114429 0.01146865 0.0116272 0.01405287 0.01234508] mean value: 0.012492871284484864 key: score_time value: [0.01227593 0.00933051 0.00936937 0.00899673 0.00907826 0.00901532 0.00900316 0.00899124 0.00994086 0.00929475] mean value: 0.009529614448547363 key: test_mcc value: [1. 1. 1. 1. 0.97590007 1. 1. 1. 1. 1. ] mean value: 0.9975900072948534 key: train_mcc value: [0.99728629 0.99728629 0.99728629 0.99728629 1. 0.99728629 0.99728629 0.99728629 0.99728997 0.99728995] mean value: 0.9975583961405413 key: test_accuracy value: [1. 1. 1. 1. 0.98780488 1. 1. 1. 1. 1. ] mean value: 0.998780487804878 key: train_accuracy value: [0.9986413 0.9986413 0.9986413 0.9986413 1. 0.9986413 0.9986413 0.9986413 0.99864315 0.99864315] mean value: 0.9987775426228541 key: test_fscore value: [1. 1. 1. 1. 0.98765432 1. 1. 1. 1. 1. ] mean value: 0.9987654320987654 key: train_fscore value: [0.99863946 0.99863946 0.99863946 0.99863946 1. 0.99863946 0.99863946 0.99863946 0.99864315 0.99863946] mean value: 0.9987758794155382 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. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [0.99728261 0.99728261 0.99728261 0.99728261 1. 0.99728261 0.99728261 0.99728261 0.99728997 0.99728261] mean value: 0.9975550842464946 key: test_roc_auc value: [1. 1. 1. 1. 0.98780488 1. 1. 1. 1. 1. ] mean value: 0.998780487804878 key: train_roc_auc value: [0.9986413 0.9986413 0.9986413 0.9986413 1. 0.9986413 0.9986413 0.9986413 0.99864499 0.9986413 ] mean value: 0.9987775421232474 key: test_jcc value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_jcc value: [0.99728261 0.99728261 0.99728261 0.99728261 1. 0.99728261 0.99728261 0.99728261 0.99728997 0.99728261] mean value: 0.9975550842464946 MCC on Blind test: -0.07 Accuracy on Blind test: 0.63 Model_name: Naive Bayes Model func: BernoulliNB() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01663947 0.01513195 0.01523376 0.01532173 0.01524663 0.01522851 0.01521087 0.01525855 0.01529098 0.02192926] mean value: 0.01604917049407959 key: score_time value: [0.01222825 0.01201463 0.01202512 0.01207089 0.01205087 0.01207089 0.01207471 0.01207829 0.01202965 0.01265216] mean value: 0.012129545211791992 key: test_mcc value: [1. 0.92932038 0.97590007 0.97590007 0.95121951 0.95235327 0.95235327 0.95235327 1. 1. ] mean value: 0.9689399834833974 key: train_mcc value: [0.97039895 0.97572001 0.96510517 0.97305604 0.97318546 0.97039895 0.97039895 0.97039895 0.96779073 0.96515328] mean value: 0.9701606488894258 key: test_accuracy value: [1. 0.96341463 0.98780488 0.98780488 0.97560976 0.97560976 0.97560976 0.97560976 1. 1. ] mean value: 0.9841463414634146 key: train_accuracy value: [0.98505435 0.98777174 0.98233696 0.98641304 0.98641304 0.98505435 0.98505435 0.98505435 0.98371777 0.98236092] mean value: 0.9849230871335024 key: test_fscore value: [1. 0.96470588 0.98795181 0.98795181 0.97560976 0.97619048 0.97619048 0.97619048 1. 1. ] mean value: 0.9844790681479763 key: train_fscore value: [0.9852349 0.98788694 0.98259705 0.98655914 0.98659517 0.9852349 0.9852349 0.9852349 0.98395722 0.98259705] mean value: 0.9851132185205181 key: test_precision value: [1. 0.93181818 0.97619048 0.97619048 0.97560976 0.95348837 0.95348837 0.95348837 1. 1. ] mean value: 0.9720274006575765 key: train_precision value: [0.9734748 0.97866667 0.96833773 0.97606383 0.97354497 0.9734748 0.9734748 0.9734748 0.97097625 0.96833773] mean value: 0.9729826389282483 key: test_recall value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [0.99728261 0.99728261 0.99728261 0.99728261 1. 0.99728261 0.99728261 0.99728261 0.99728997 0.99728261] mean value: 0.9975550842464946 key: test_roc_auc value: [1. 0.96341463 0.98780488 0.98780488 0.97560976 0.97560976 0.97560976 0.97560976 1. 1. ] mean value: 0.9841463414634146 key: train_roc_auc value: [0.98505435 0.98777174 0.98233696 0.98641304 0.98641304 0.98505435 0.98505435 0.98505435 0.98369933 0.98238114] mean value: 0.9849232649935196 key: test_jcc value: [1. 0.93181818 0.97619048 0.97619048 0.95238095 0.95348837 0.95348837 0.95348837 1. 1. ] mean value: 0.9697045202859156 key: train_jcc value: [0.97089947 0.97606383 0.96578947 0.9734748 0.97354497 0.97089947 0.97089947 0.97089947 0.96842105 0.96578947] mean value: 0.9706681487991099 MCC on Blind test: 0.07 Accuracy on Blind test: 0.64 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01525855 0.01103139 0.01229334 0.01178408 0.01259732 0.01231575 0.01306176 0.01331615 0.01320744 0.01262689] mean value: 0.012749266624450684 key: score_time value: [0.0398953 0.01294327 0.01847672 0.01840377 0.01431727 0.01472712 0.0146594 0.01454973 0.01474571 0.01453471] mean value: 0.017725300788879395 key: test_mcc value: [1. 0.97590007 0.97590007 1. 0.97590007 1. 0.97590007 0.9067647 1. 0.97559506] mean value: 0.9785960048885964 key: train_mcc value: [0.98644582 0.98644582 0.98644582 0.98644582 0.9865041 0.98644582 0.98914504 0.98914504 0.99186249 0.98915981] mean value: 0.9878045610087458 key: test_accuracy value: [1. 0.98780488 0.98780488 1. 0.98780488 1. 0.98780488 0.95121951 1. 0.98765432] mean value: 0.9890093345377898 key: train_accuracy value: [0.99320652 0.99320652 0.99320652 0.99320652 0.99320652 0.99320652 0.99456522 0.99456522 0.99592944 0.99457259] mean value: 0.9938871600495547 key: test_fscore value: [1. 0.98795181 0.98795181 1. 0.98765432 1. 0.98795181 0.95348837 1. 0.98795181] mean value: 0.989294992199634 key: train_fscore value: [0.9932341 0.9932341 0.9932341 0.9932341 0.99325236 0.9932341 0.99457995 0.99457995 0.99594046 0.99457995] mean value: 0.993910315982957 key: test_precision value: [1. 0.97619048 0.97619048 1. 1. 1. 0.97619048 0.91111111 1. 0.97619048] mean value: 0.9815873015873016 key: train_precision value: [0.98921833 0.98921833 0.98921833 0.98921833 0.98659517 0.98921833 0.99189189 0.99189189 0.99459459 0.99189189] mean value: 0.9902957088737857 key: test_recall value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [0.99728261 0.99728261 0.99728261 0.99728261 1. 0.99728261 0.99728261 0.99728261 0.99728997 0.99728261] mean value: 0.9975550842464946 key: test_roc_auc value: [1. 0.98780488 0.98780488 1. 0.98780488 1. 0.98780488 0.95121951 1. 0.9875 ] mean value: 0.9889939024390244 key: train_roc_auc value: [0.99320652 0.99320652 0.99320652 0.99320652 0.99320652 0.99320652 0.99456522 0.99456522 0.9959276 0.99457626] mean value: 0.9938873424060328 key: test_jcc value: [1. 0.97619048 0.97619048 1. 0.97560976 1. 0.97619048 0.91111111 1. 0.97619048] mean value: 0.9791482771970577 key: train_jcc value: [0.98655914 0.98655914 0.98655914 0.98655914 0.98659517 0.98655914 0.98921833 0.98921833 0.99191375 0.98921833] mean value: 0.9878959606341104 MCC on Blind test: -0.04 Accuracy on Blind test: 0.62 Model_name: SVM Model func: SVC(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.01619244 0.01553178 0.01565218 0.01461983 0.01578021 0.01494431 0.01576471 0.01583004 0.01758289 0.01549101] mean value: 0.015738940238952635 key: score_time value: [0.01130342 0.01001787 0.01011443 0.01000953 0.01020145 0.01054621 0.01036334 0.01090264 0.01029825 0.01063371] mean value: 0.010439085960388183 key: test_mcc value: [1. 1. 1. 1. 0.97590007 1. 1. 1. 1. 1. ] mean value: 0.9975900072948534 key: train_mcc value: [0.99728629 0.99728629 0.99728629 0.99728629 1. 0.99728629 0.99728629 0.99728629 0.99728997 0.99728995] mean value: 0.9975583961405413 key: test_accuracy value: [1. 1. 1. 1. 0.98780488 1. 1. 1. 1. 1. ] mean value: 0.998780487804878 key: train_accuracy value: [0.9986413 0.9986413 0.9986413 0.9986413 1. 0.9986413 0.9986413 0.9986413 0.99864315 0.99864315] mean value: 0.9987775426228541 key: test_fscore value: [1. 1. 1. 1. 0.98765432 1. 1. 1. 1. 1. ] mean value: 0.9987654320987654 key: train_fscore value: [0.99863946 0.99863946 0.99863946 0.99863946 1. 0.99863946 0.99863946 0.99863946 0.99864315 0.99863946] mean value: 0.9987758794155382 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. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [0.99728261 0.99728261 0.99728261 0.99728261 1. 0.99728261 0.99728261 0.99728261 0.99728997 0.99728261] mean value: 0.9975550842464946 key: test_roc_auc value: [1. 1. 1. 1. 0.98780488 1. 1. 1. 1. 1. ] mean value: 0.998780487804878 key: train_roc_auc value: [0.9986413 0.9986413 0.9986413 0.9986413 1. 0.9986413 0.9986413 0.9986413 0.99864499 0.9986413 ] mean value: 0.9987775421232474 key: test_jcc value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_jcc value: [0.99728261 0.99728261 0.99728261 0.99728261 1. 0.99728261 0.99728261 0.99728261 0.99728997 0.99728261] mean value: 0.9975550842464946 MCC on Blind test: 0.04 Accuracy on Blind test: 0.64 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.71033812 0.85398245 0.76243162 0.64253044 0.71102619 0.74322653 0.67659807 0.86070871 0.6764524 0.71408868] mean value: 0.7351383209228516 key: score_time value: [0.01244736 0.01238942 0.01265264 0.0124588 0.01265335 0.01254511 0.01257324 0.01247334 0.0125246 0.0124898 ] mean value: 0.012520766258239746 key: test_mcc value: [1. 1. 1. 1. 0.97590007 1. 0.97590007 1. 0.97560976 1. ] mean value: 0.9927409901994627 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 0.98780488 1. 0.98780488 1. 0.98765432 1. ] mean value: 0.9963264077085215 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 0.98765432 1. 0.98795181 1. 0.98765432 1. ] mean value: 0.9963260449204224 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. 0.97619048 1. 0.97560976 1. ] mean value: 0.9951800232288037 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 0.98780488 1. 0.98780488 1. 0.98780488 1. ] mean value: 0.9963414634146341 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 0.97560976 1. 0.97619048 1. 0.97560976 1. ] mean value: 0.9927409988385598 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.01 Accuracy on Blind test: 0.63 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.03180194 0.02575827 0.02321696 0.02543497 0.02257943 0.02369952 0.02363586 0.02361107 0.02385974 0.02565098] mean value: 0.024924874305725098 key: score_time value: [0.01251912 0.00972176 0.00946927 0.01002908 0.00947881 0.00931859 0.00975347 0.00951219 0.00998378 0.00985932] mean value: 0.009964537620544434 key: test_mcc value: [0.97590007 1. 0.97590007 1. 0.92710507 1. 0.97590007 1. 1. 1. ] mean value: 0.9854805288146706 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.98780488 1. 0.98780488 1. 0.96341463 1. 0.98780488 1. 1. 1. ] mean value: 0.9926829268292683 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.98765432 1. 0.98795181 1. 0.96385542 1. 0.98795181 1. 1. 1. ] mean value: 0.9927413357132232 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.97619048 1. 0.95238095 1. 0.97619048 1. 1. 1. ] mean value: 0.9904761904761905 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.97560976 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9951219512195122 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.98780488 1. 0.98780488 1. 0.96341463 1. 0.98780488 1. 1. 1. ] mean value: 0.9926829268292683 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.97560976 1. 0.97619048 1. 0.93023256 1. 0.97619048 1. 1. 1. ] mean value: 0.9858223266618048 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 Accuracy on Blind test: 0.64 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.11132574 0.10388851 0.11121058 0.10519838 0.10347652 0.10427356 0.1079793 0.10282946 0.10855246 0.10818768] mean value: 0.10669221878051757 key: score_time value: [0.01775813 0.01951098 0.01936197 0.01826262 0.01819253 0.01921201 0.01875758 0.01807761 0.01975298 0.01824927] mean value: 0.01871356964111328 key: test_mcc value: [1. 1. 1. 1. 0.97590007 0.97590007 1. 1. 1. 1. ] mean value: 0.9951800145897066 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 0.98780488 0.98780488 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 0.98765432 0.98765432 1. 1. 1. 1. ] mean value: 0.9975308641975309 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. 1. 1. 1. 0.97560976 0.97560976 1. 1. 1. 1. ] mean value: 0.9951219512195122 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 0.98780488 0.98780488 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 0.97560976 0.97560976 1. 1. 1. 1. ] mean value: 0.9951219512195122 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.07 Accuracy on Blind test: 0.63 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01084042 0.01063967 0.0104816 0.01065373 0.01052141 0.01053262 0.01071382 0.01055121 0.01071 0.01063275] mean value: 0.01062772274017334 key: score_time value: [0.00893998 0.00872803 0.00880671 0.00882053 0.00877452 0.00883174 0.00872397 0.00879312 0.00882244 0.00878716] mean value: 0.00880281925201416 key: test_mcc value: [0.97590007 1. 1. 1. 0.95121951 1. 1. 1. 1. 0.97559506] mean value: 0.9902714641653124 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.98780488 1. 1. 1. 0.97560976 1. 1. 1. 1. 0.98765432] mean value: 0.9951068955133996 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.98795181 1. 1. 1. 0.97560976 1. 1. 1. 1. 0.98795181] mean value: 0.9951513370555393 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.97619048 1. 1. 1. 0.97560976 1. 1. 1. 1. 0.97619048] mean value: 0.9927990708478514 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.98780488 1. 1. 1. 0.97560976 1. 1. 1. 1. 0.9875 ] mean value: 0.9950914634146342 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.97619048 1. 1. 1. 0.95238095 1. 1. 1. 1. 0.97619048] mean value: 0.9904761904761905 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.07 Accuracy on Blind test: 0.61 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.67246389 1.67752552 1.6656456 1.67518735 1.64327574 1.6754415 1.67777348 1.66476154 1.69954181 1.70067692] mean value: 1.6752293348312377 key: score_time value: [0.14544225 0.09017467 0.09018755 0.09005737 0.09022903 0.09050894 0.08992743 0.09079027 0.09556842 0.09634709] mean value: 0.09692330360412597 key: test_mcc value: [1. 1. 1. 1. 0.97590007 0.97590007 1. 1. 1. 1. ] mean value: 0.9951800145897066 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 0.98780488 0.98780488 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 0.98765432 0.98765432 1. 1. 1. 1. ] mean value: 0.9975308641975309 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. 1. 1. 1. 0.97560976 0.97560976 1. 1. 1. 1. ] mean value: 0.9951219512195122 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 0.98780488 0.98780488 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 0.97560976 0.97560976 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)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this 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)) mean value: 0.9951219512195122 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 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) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000...05', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', '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: [0.99092054 1.16540051 1.00584269 1.03182769 1.01105213 1.06261158 1.01615024 1.07495379 1.00184035 1.06114531] mean value: 1.042174482345581 key: score_time value: [0.22117782 0.23626304 0.23236489 0.18774223 0.22354317 0.23702693 0.22307229 0.25480652 0.24092126 0.26768947] mean value: 0.23246076107025146 key: test_mcc value: [1. 1. 1. 1. 0.97590007 0.97590007 1. 1. 1. 1. ] mean value: 0.9951800145897066 key: train_mcc value: [0.99457991 0.99457991 0.99457991 0.99457991 0.99728629 0.99728629 0.99457991 0.99457991 0.99458727 0.99458719] mean value: 0.9951226477721911 key: test_accuracy value: [1. 1. 1. 1. 0.98780488 0.98780488 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_accuracy value: [0.99728261 0.99728261 0.99728261 0.99728261 0.9986413 0.9986413 0.99728261 0.99728261 0.9972863 0.9972863 ] mean value: 0.9975550852457082 key: test_fscore value: [1. 1. 1. 1. 0.98765432 0.98765432 1. 1. 1. 1. ] mean value: 0.9975308641975309 key: train_fscore value: [0.9972752 0.9972752 0.9972752 0.9972752 0.99863946 0.99863946 0.9972752 0.9972752 0.99728261 0.9972752 ] mean value: 0.9975487950777989 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. 1. 1. 1. 0.97560976 0.97560976 1. 1. 1. 1. ] mean value: 0.9951219512195122 key: train_recall value: [0.99456522 0.99456522 0.99456522 0.99456522 0.99728261 0.99728261 0.99456522 0.99456522 0.99457995 0.99456522] mean value: 0.9951101684929893 key: test_roc_auc value: [1. 1. 1. 1. 0.98780488 0.98780488 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_roc_auc value: [0.99728261 0.99728261 0.99728261 0.99728261 0.9986413 0.9986413 0.99728261 0.99728261 0.99728997 0.99728261] mean value: 0.9975550842464946 key: test_jcc value: [1. 1. 1. 1. 0.97560976 0.97560976 1. 1. 1. 1. ] mean value: 0.9951219512195122 key: train_jcc value: [0.99456522 0.99456522 0.99456522 0.99456522 0.99728261 0.99728261 0.99456522 0.99456522 0.99457995 0.99456522] mean value: 0.9951101684929893 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Naive Bayes Model func: BernoulliNB() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.02661252 0.01637578 0.01948047 0.02643633 0.01708198 0.01650357 0.01662159 0.02180552 0.01668048 0.01667333] mean value: 0.019427156448364256 key: score_time value: [0.01385212 0.0119977 0.01221657 0.01216435 0.01208377 0.01212525 0.01216173 0.01243138 0.01215315 0.01216531] mean value: 0.01233513355255127 key: test_mcc value: [1. 0.92932038 0.97590007 0.97590007 0.95121951 0.95235327 0.95235327 0.95235327 1. 1. ] mean value: 0.9689399834833974 key: train_mcc value: [0.97039895 0.97572001 0.96510517 0.97305604 0.97318546 0.97039895 0.97039895 0.97039895 0.96779073 0.96515328] mean value: 0.9701606488894258 key: test_accuracy value: [1. 0.96341463 0.98780488 0.98780488 0.97560976 0.97560976 0.97560976 0.97560976 1. 1. ] mean value: 0.9841463414634146 key: train_accuracy value: [0.98505435 0.98777174 0.98233696 0.98641304 0.98641304 0.98505435 0.98505435 0.98505435 0.98371777 0.98236092] mean value: 0.9849230871335024 key: test_fscore value: [1. 0.96470588 0.98795181 0.98795181 0.97560976 0.97619048 0.97619048 0.97619048 1. 1. ] mean value: 0.9844790681479763 key: train_fscore value: [0.9852349 0.98788694 0.98259705 0.98655914 0.98659517 0.9852349 0.9852349 0.9852349 0.98395722 0.98259705] mean value: 0.9851132185205181 key: test_precision value: [1. 0.93181818 0.97619048 0.97619048 0.97560976 0.95348837 0.95348837 0.95348837 1. 1. ] mean value: 0.9720274006575765 key: train_precision value: [0.9734748 0.97866667 0.96833773 0.97606383 0.97354497 0.9734748 0.9734748 0.9734748 0.97097625 0.96833773] mean value: 0.9729826389282483 key: test_recall value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [0.99728261 0.99728261 0.99728261 0.99728261 1. 0.99728261 0.99728261 0.99728261 0.99728997 0.99728261] mean value: 0.9975550842464946 key: test_roc_auc value: [1. 0.96341463 0.98780488 0.98780488 0.97560976 0.97560976 0.97560976 0.97560976 1. 1. ] mean value: 0.9841463414634146 key: train_roc_auc value: [0.98505435 0.98777174 0.98233696 0.98641304 0.98641304 0.98505435 0.98505435 0.98505435 0.98369933 0.98238114] mean value: 0.9849232649935196 key: test_jcc value: [1. 0.93181818 0.97619048 0.97619048 0.95238095 0.95348837 0.95348837 0.95348837 1. 1. ] mean value: 0.9697045202859156 key: train_jcc value: [0.97089947 0.97606383 0.96578947 0.9734748 0.97354497 0.97089947 0.97089947 0.97089947 0.96842105 0.96578947] mean value: 0.9706681487991099 MCC on Blind test: 0.07 Accuracy on Blind test: 0.64 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) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000... 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: [0.20946097 0.30711532 0.08906627 0.08612251 0.10769844 0.08769441 0.09436107 0.2328229 0.15788984 0.26959753] mean value: 0.1641829252243042 key: score_time value: [0.01183629 0.011976 0.01166344 0.01103258 0.011132 0.01190448 0.01189089 0.01126862 0.01215935 0.01104069] mean value: 0.01159043312072754 key: test_mcc value: [1. 1. 0.97590007 1. 0.95121951 0.97590007 1. 1. 1. 1. ] mean value: 0.9903019658092188 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 0.98780488 1. 0.97560976 0.98780488 1. 1. 1. 1. ] mean value: 0.9951219512195122 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 0.98795181 1. 0.97560976 0.98765432 1. 1. 1. 1. ] mean value: 0.9951215884314131 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.97619048 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9951800232288037 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 0.97560976 0.97560976 1. 1. 1. 1. ] mean value: 0.9951219512195122 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.98780488 1. 0.97560976 0.98780488 1. 1. 1. 1. ] mean value: 0.9951219512195122 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. 0.97619048 1. 0.95238095 0.97560976 1. 1. 1. 1. ] mean value: 0.990418118466899 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.07 Accuracy on Blind test: 0.63 Model_name: LDA Model func: LinearDiscriminantAnalysis() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.0522449 0.07058001 0.05262351 0.1267302 0.08981466 0.09062672 0.10652876 0.05881453 0.10278082 0.07908916] mean value: 0.08298332691192627 key: score_time value: [0.0192585 0.0122261 0.01217866 0.02735615 0.0230546 0.01231122 0.01231313 0.0156126 0.0154686 0.01230407] mean value: 0.016208362579345704 key: test_mcc value: [1. 1. 1. 0.97590007 0.97590007 1. 0.97590007 0.97590007 0.97560976 0.95174259] mean value: 0.9830952635721183 key: train_mcc value: [0.99728629 0.99456522 0.99728629 0.99728629 1. 0.99456522 0.99728629 0.99728629 0.99728997 0.99457258] mean value: 0.9967424443232185 key: test_accuracy value: [1. 1. 1. 0.98780488 0.98780488 1. 0.98780488 0.98780488 0.98765432 0.97530864] mean value: 0.9914182475158084 key: train_accuracy value: [0.9986413 0.99728261 0.9986413 0.9986413 1. 0.99728261 0.9986413 0.9986413 0.99864315 0.9972863 ] mean value: 0.9983701182821072 key: test_fscore value: [1. 1. 1. 0.98795181 0.98765432 1. 0.98795181 0.98795181 0.98765432 0.97619048] mean value: 0.9915354539852532 key: train_fscore value: [0.99863946 0.99728261 0.99863946 0.99863946 1. 0.99728261 0.99863946 0.99863946 0.99864315 0.99728261] mean value: 0.9983688252895401 key: test_precision value: [1. 1. 1. 0.97619048 1. 1. 0.97619048 0.97619048 0.97560976 0.95348837] mean value: 0.9857669556762013 key: train_precision value: [1. 0.99728261 1. 1. 1. 0.99728261 1. 1. 1. 0.99728261] mean value: 0.9991847826086957 key: test_recall value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [0.99728261 0.99728261 0.99728261 0.99728261 1. 0.99728261 0.99728261 0.99728261 0.99728997 0.99728261] mean value: 0.9975550842464946 key: test_roc_auc value: [1. 1. 1. 0.98780488 0.98780488 1. 0.98780488 0.98780488 0.98780488 0.975 ] mean value: 0.9914024390243903 key: train_roc_auc value: [0.9986413 0.99728261 0.9986413 0.9986413 1. 0.99728261 0.9986413 0.9986413 0.99864499 0.99728629] mean value: 0.998370301637799 key: test_jcc value: [1. 1. 1. 0.97619048 0.97560976 1. 0.97619048 0.97619048 0.97560976 0.95348837] mean value: 0.9833279312859574 key: train_jcc value: [0.99728261 0.99457995 0.99728261 0.99728261 1. 0.99457995 0.99728261 0.99728261 0.99728997 0.99457995] mean value: 0.9967442853776364 MCC on Blind test: 0.05 Accuracy on Blind test: 0.64 Model_name: Multinomial Model func: MultinomialNB() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.02811432 0.01485062 0.01452565 0.01507163 0.01482677 0.01482296 0.01491022 0.01570797 0.01581764 0.01586032] mean value: 0.01645081043243408 key: score_time value: [0.01208687 0.0120523 0.0120635 0.0120616 0.0118196 0.0120852 0.01258612 0.01281214 0.01249075 0.01210594] mean value: 0.012216401100158692 key: test_mcc value: [1. 0.97590007 0.95235327 1. 0.92710507 1. 1. 0.88465174 0.97560976 0.97559506] mean value: 0.9691214958271785 key: train_mcc value: [0.96774867 0.97039895 0.97039895 0.97039895 0.97849211 0.96510517 0.96774867 0.98106882 0.97043771 0.97043965] mean value: 0.9712237636315258 key: test_accuracy value: [1. 0.98780488 0.97560976 1. 0.96341463 1. 1. 0.93902439 0.98765432 0.98765432] mean value: 0.9841162300511894 key: train_accuracy value: [0.98369565 0.98505435 0.98505435 0.98505435 0.98913043 0.98233696 0.98369565 0.99048913 0.98507463 0.98507463] mean value: 0.985466012329656 key: test_fscore value: [1. 0.98795181 0.97619048 1. 0.96385542 1. 1. 0.94252874 0.98765432 0.98795181] mean value: 0.9846132568954893 key: train_fscore value: [0.98391421 0.9852349 0.9852349 0.9852349 0.98924731 0.98259705 0.98391421 0.99055331 0.98527443 0.9852349 ] mean value: 0.9856440119660511 key: test_precision value: [1. 0.97619048 0.95348837 1. 0.95238095 1. 1. 0.89130435 0.97560976 0.97619048] mean value: 0.9725164380778576 key: train_precision value: [0.97089947 0.9734748 0.9734748 0.9734748 0.9787234 0.96833773 0.97089947 0.98391421 0.97354497 0.9734748 ] mean value: 0.974021846382926 key: test_recall value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [0.99728261 0.99728261 0.99728261 0.99728261 1. 0.99728261 0.99728261 0.99728261 0.99728997 0.99728261] mean value: 0.9975550842464946 key: test_roc_auc value: [1. 0.98780488 0.97560976 1. 0.96341463 1. 1. 0.93902439 0.98780488 0.9875 ] mean value: 0.9841158536585366 key: train_roc_auc value: [0.98369565 0.98505435 0.98505435 0.98505435 0.98913043 0.98233696 0.98369565 0.99048913 0.98505803 0.98509117] mean value: 0.9854660068339814 key: test_jcc value: [1. 0.97619048 0.95348837 1. 0.93023256 1. 1. 0.89130435 0.97560976 0.97619048] mean value: 0.9703015986537158 key: train_jcc value: [0.96833773 0.97089947 0.97089947 0.97089947 0.9787234 0.96578947 0.96833773 0.98128342 0.97097625 0.97089947] mean value: 0.9717045899036885 MCC on Blind test: 0.03 Accuracy on Blind test: 0.63 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.02199936 0.01965737 0.01917267 0.01896453 0.01923943 0.02606606 0.01948166 0.02033186 0.0212853 0.01985145] mean value: 0.020604968070983887 key: score_time value: [0.01192355 0.01183677 0.01220274 0.01208115 0.01211715 0.01216769 0.01209044 0.01894236 0.012146 0.01208568] mean value: 0.01275935173034668 key: test_mcc value: [1. 1. 1. 1. 0.97590007 1. 0.97590007 1. 0.97560976 1. ] mean value: 0.9927409901994627 key: train_mcc value: [1. 0.99728629 1. 0.99728629 1. 0.99728629 1. 1. 1. 0.99728995] mean value: 0.9989148825342111 key: test_accuracy value: [1. 1. 1. 1. 0.98780488 1. 0.98780488 1. 0.98765432 1. ] mean value: 0.9963264077085215 key: train_accuracy value: [1. 0.9986413 1. 0.9986413 1. 0.9986413 1. 1. 1. 0.99864315] mean value: 0.9994567060940357 key: test_fscore value: [1. 1. 1. 1. 0.98765432 1. 0.98795181 1. 0.98765432 1. ] mean value: 0.9963260449204224 key: train_fscore value: [1. 0.99863946 1. 0.99863946 1. 0.99863946 1. 1. 1. 0.99863946] mean value: 0.9994557823129252 key: test_precision value: [1. 1. 1. 1. 1. 1. 0.97619048 1. 0.97560976 1. ] mean value: 0.9951800232288037 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [1. 0.99728261 1. 0.99728261 1. 0.99728261 1. 1. 1. 0.99728261] mean value: 0.9989130434782609 key: test_roc_auc value: [1. 1. 1. 1. 0.98780488 1. 0.98780488 1. 0.98780488 1. ] mean value: 0.9963414634146341 key: train_roc_auc value: [1. 0.9986413 1. 0.9986413 1. 0.9986413 1. 1. 1. 0.9986413] mean value: 0.9994565217391305 key: test_jcc value: [1. 1. 1. 1. 0.97560976 1. 0.97619048 1. 0.97560976 1. ] mean value: 0.9927409988385598 key: train_jcc value: [1. 0.99728261 1. 0.99728261 1. 0.99728261 1. 1. 1. 0.99728261] mean value: 0.9989130434782609 MCC on Blind test: 0.04 Accuracy on Blind test: 0.64 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) List of models: /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)) [('Logistic Regression', LogisticRegression(random_state=42)), ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)), ('Gaussian NB', GaussianNB()), ('Naive Bayes', BernoulliNB()), ('K-Nearest Neighbors', KNeighborsClassifier()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.02702713 0.01727796 0.0181396 0.01810908 0.016891 0.01763678 0.01881504 0.01880169 0.03385401 0.01738429] mean value: 0.02039365768432617 key: score_time value: [0.01218867 0.01202273 0.01220393 0.01227856 0.01208973 0.01210833 0.01978302 0.01922965 0.02226758 0.01182604] mean value: 0.014599823951721191 key: test_mcc value: [1. 1. 1. 1. 0.97590007 1. 0.97590007 1. 0.97560976 1. ] mean value: 0.9927409901994627 key: train_mcc value: [1. 1. 1. 0.99728629 1. 1. 1. 1. 1. 0.99728995] mean value: 0.9994576243760325 key: test_accuracy value: [1. 1. 1. 1. 0.98780488 1. 0.98780488 1. 0.98765432 1. ] mean value: 0.9963264077085215 key: train_accuracy value: [1. 1. 1. 0.9986413 1. 1. 1. 1. 1. 0.99864315] mean value: 0.9997284452244706 key: test_fscore value: [1. 1. 1. 1. 0.98765432 1. 0.98795181 1. 0.98765432 1. ] mean value: 0.9963260449204224 key: train_fscore value: [1. 1. 1. 0.99863946 1. 1. 1. 1. 1. 0.99863946] mean value: 0.9997278911564625 key: test_precision value: [1. 1. 1. 1. 1. 1. 0.97619048 1. 0.97560976 1. ] mean value: 0.9951800232288037 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [1. 1. 1. 0.99728261 1. 1. 1. 1. 1. 0.99728261] mean value: 0.9994565217391305 key: test_roc_auc value: [1. 1. 1. 1. 0.98780488 1. 0.98780488 1. 0.98780488 1. ] mean value: 0.9963414634146341 key: train_roc_auc value: [1. 1. 1. 0.9986413 1. 1. 1. 1. 1. 0.9986413] mean value: 0.9997282608695652 key: test_jcc value: [1. 1. 1. 1. 0.97560976 1. 0.97619048 1. 0.97560976 1. ] mean value: 0.9927409988385598 key: train_jcc value: [1. 1. 1. 0.99728261 1. 1. 1. 1. 1. 0.99728261] mean value: 0.9994565217391305 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.35029912 0.34669185 0.34488606 0.34064388 0.32821965 0.32856464 0.33788705 0.32813716 0.32518172 0.32532883] mean value: 0.3355839967727661 key: score_time value: [0.01660323 0.01597118 0.01555681 0.01566911 0.01627326 0.01621413 0.01663399 0.01586509 0.01574016 0.01567173] mean value: 0.016019868850708007 key: test_mcc value: [1. 1. 1. 1. 0.97590007 0.97590007 1. 1. 1. 1. ] mean value: 0.9951800145897066 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 0.98780488 0.98780488 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 0.98765432 0.98765432 1. 1. 1. 1. ] mean value: 0.9975308641975309 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. 1. 1. 1. 0.97560976 0.97560976 1. 1. 1. 1. ] mean value: 0.9951219512195122 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 0.98780488 0.98780488 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 0.97560976 0.97560976 1. 1. 1. 1. ] mean value: 0.9951219512195122 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 Accuracy on Blind test: 0.65 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.16502285 0.18274188 0.16214156 0.16709042 0.15880084 0.15820289 0.16567898 0.18430805 0.16625166 0.08271599] mean value: 0.15929551124572755 key: score_time value: [0.0390625 0.03055167 0.02326536 0.02653861 0.03228641 0.02673721 0.03486085 0.03969193 0.04029655 0.03138304] mean value: 0.0324674129486084 key: test_mcc value: [1. 1. 0.97590007 1. 0.92710507 0.97590007 1. 1. 1. 1. ] mean value: 0.9878905215198173 key: train_mcc value: [0.99728629 1. 1. 1. 1. 1. 1. 1. 1. 0.99728997] mean value: 0.9994576263690622 key: test_accuracy value: [1. 1. 0.98780488 1. 0.96341463 0.98780488 1. 1. 1. 1. ] mean value: 0.9939024390243902 key: train_accuracy value: [0.9986413 1. 1. 1. 1. 1. 1. 1. 1. 0.99864315] mean value: 0.9997284452244706 key: test_fscore value: [1. 1. 0.98795181 1. 0.96385542 0.98765432 1. 1. 1. 1. ] mean value: 0.9939461549903317 key: train_fscore value: [0.99863946 1. 1. 1. 1. 1. 1. 1. 1. 0.99864315] mean value: 0.9997282603679192 key: test_precision value: [1. 1. 0.97619048 1. 0.95238095 1. 1. 1. 1. 1. ] mean value: 0.9928571428571429 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.99728997] mean value: 0.9997289972899729 key: test_recall value: [1. 1. 1. 1. 0.97560976 0.97560976 1. 1. 1. 1. ] mean value: 0.9951219512195122 key: train_recall value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /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)) [0.99728261 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9997282608695652 key: test_roc_auc value: [1. 1. 0.98780488 1. 0.96341463 0.98780488 1. 1. 1. 1. ] mean value: 0.9939024390243902 key: train_roc_auc value: [0.9986413 1. 1. 1. 1. 1. 1. 1. 1. 0.99864499] mean value: 0.9997286290797691 key: test_jcc value: [1. 1. 0.97619048 1. 0.93023256 0.97560976 1. 1. 1. 1. ] mean value: 0.9882032790427572 key: train_jcc value: [0.99728261 1. 1. 1. 1. 1. 1. 1. 1. 0.99728997] mean value: 0.9994572581595381 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.39215183 0.51867986 0.51008296 0.37460756 0.48715901 0.49438095 0.48513198 0.41793799 0.39081693 0.55535507] mean value: 0.4626304149627686 key: score_time value: [0.01875997 0.03882957 0.03196502 0.03138757 0.03683138 0.03812099 0.03802204 0.03779054 0.03119278 0.03178477] mean value: 0.03346846103668213 key: test_mcc value: [1. 0.97590007 0.97590007 1. 0.97590007 1. 1. 0.88465174 1. 1. ] mean value: 0.9812351955774983 key: train_mcc value: [0.99728629 0.99728629 0.99728629 0.99728629 0.99728629 0.99457991 0.99728629 0.99728629 1. 0.99728997] mean value: 0.9972873914320465 key: test_accuracy value: [1. 0.98780488 0.98780488 1. 0.98780488 1. 1. 0.93902439 1. 1. ] mean value: 0.9902439024390244 key: train_accuracy value: [0.9986413 0.9986413 0.9986413 0.9986413 0.9986413 0.99728261 0.9986413 0.9986413 1. 0.99864315] mean value: 0.9986414887027314 key: test_fscore value: [1. 0.98795181 0.98795181 1. 0.98765432 1. 1. 0.94252874 1. 1. ] mean value: 0.990608667107767 key: train_fscore value: [0.99864315 0.99864315 0.99864315 0.99864315 0.99864315 0.99728997 0.99864315 0.99864315 1. 0.99864315] mean value: 0.9986435156074762 key: test_precision value: [1. 0.97619048 0.97619048 1. 1. 1. 1. 0.89130435 1. 1. ] mean value: 0.9843685300207039 key: train_precision value: [0.99728997 0.99728997 0.99728997 0.99728997 0.99728997 0.99459459 0.99728997 0.99728997 1. 0.99728997] mean value: 0.9972914377792427 key: test_recall value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.98780488 0.98780488 1. 0.98780488 1. 1. 0.93902439 1. 1. ] mean value: 0.9902439024390244 key: train_roc_auc value: [0.9986413 0.9986413 0.9986413 0.9986413 0.9986413 0.99728261 0.9986413 0.9986413 1. 0.99864499] mean value: 0.9986416725580299 key: test_jcc value: [1. 0.97619048 0.97619048 1. 0.97560976 1. 1. 0.89130435 1. 1. ] mean value: 0.98192950563046 key: train_jcc value: [0.99728997 0.99728997 0.99728997 0.99728997 0.99728997 0.99459459 0.99728997 0.99728997 1. 0.99728997] mean value: 0.9972914377792427 MCC on Blind test: -0.04 Accuracy on Blind test: 0.62 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) List of models: /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)) [('Logistic Regression', LogisticRegression(random_state=42)), ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)), ('Gaussian NB', GaussianNB()), ('Naive Bayes', BernoulliNB()), ('K-Nearest Neighbors', KNeighborsClassifier()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [1.11634278 1.22586918 1.08421278 1.11571884 0.93970394 1.09515667 1.21813774 1.20429325 1.23972106 1.22192454] mean value: 1.146108078956604 key: score_time value: [0.00973034 0.00927925 0.00935864 0.00923634 0.00941062 0.00923848 0.00920224 0.00918889 0.00931549 0.00929356] mean value: 0.009325385093688965 key: test_mcc value: [0.97590007 1. 0.97590007 1. 0.95121951 0.97590007 1. 1. 1. 1. ] mean value: 0.9878919731040722 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.98780488 1. 0.98780488 1. 0.97560976 0.98780488 1. 1. 1. 1. ] mean value: 0.9939024390243902 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.98765432 1. 0.98795181 1. 0.97560976 0.98765432 1. 1. 1. 1. ] mean value: 0.9938870205301785 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.97619048 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9951800232288037 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.97560976 1. 1. 1. 0.97560976 0.97560976 1. 1. 1. 1. ] mean value: 0.9926829268292683 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.98780488 1. 0.98780488 1. 0.97560976 0.98780488 1. 1. 1. 1. ] mean value: 0.9939024390243902 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.97560976 1. 0.97619048 1. 0.95238095 0.97560976 1. 1. 1. 1. ] mean value: 0.9879790940766551 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 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/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(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.03594899 0.03618169 0.03547835 0.03739643 0.0375247 0.03539371 0.03769898 0.0356102 0.03827429 0.03624153] mean value: 0.03657488822937012 key: score_time value: [0.01257873 0.01270938 0.01267934 0.017627 0.01313949 0.01514435 0.02065754 0.01593971 0.01886082 0.02063227] mean value: 0.015996861457824706 key: test_mcc value: [1. 1. 1. 1. 0.97590007 1. 1. 1. 1. 1. ] mean value: 0.9975900072948534 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 0.98780488 1. 1. 1. 1. 1. ] mean value: 0.998780487804878 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 0.98765432 1. 1. 1. 1. 1. ] mean value: 0.9987654320987654 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. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 0.98780488 1. 1. 1. 1. 1. ] mean value: 0.998780487804878 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02517939 0.04055691 0.0403533 0.04039001 0.03876543 0.04039931 0.04014945 0.04073524 0.05057812 0.03798723] mean value: 0.03950943946838379 key: score_time value: [0.01878047 0.01876593 0.01878381 0.01876879 0.01878834 0.01874089 0.01880455 0.01884627 0.0188272 0.01871467] mean value: 0.01878209114074707 key: test_mcc value: [1. 0.97590007 1. 0.97590007 0.97590007 1. 0.95235327 1. 0.97560976 0.92840283] mean value: 0.978406607327605 key: train_mcc value: [0.99456522 0.99456522 0.99456522 0.99456522 0.99728629 0.99456522 0.99728629 0.99456522 0.99457258 0.99457258] mean value: 0.9951109049120375 key: test_accuracy value: [1. 0.98780488 1. 0.98780488 0.98780488 1. 0.97560976 1. 0.98765432 0.96296296] mean value: 0.9889641674194519 key: train_accuracy value: [0.99728261 0.99728261 0.99728261 0.99728261 0.9986413 0.99728261 0.9986413 0.99728261 0.9972863 0.9972863 ] mean value: 0.9975550852457082 key: test_fscore value: [1. 0.98795181 1. 0.98795181 0.98765432 1. 0.97619048 1. 0.98765432 0.96470588] mean value: 0.9892108614976557 key: train_fscore value: [0.99728261 0.99728261 0.99728261 0.99728261 0.99864315 0.99728261 0.99863946 0.99728261 0.99728997 0.99728261] mean value: 0.9975550837448487 key: test_precision value: [1. 0.97619048 1. 0.97619048 1. 1. 0.95348837 1. 0.97560976 0.93181818] mean value: 0.9813297262389719 key: train_precision value: [0.99728261 0.99728261 0.99728261 0.99728261 0.99728997 0.99728261 1. 0.99728261 0.99728997 0.99728261] mean value: 0.9975558206669024 key: test_recall value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [0.99728261 0.99728261 0.99728261 0.99728261 1. 0.99728261 0.99728261 0.99728261 0.99728997 0.99728261] mean value: 0.9975550842464946 key: test_roc_auc value: /home/tanu/git/LSHTM_analysis/scripts/ml/./gid_rt.py:135: 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 smnc_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True) /home/tanu/git/LSHTM_analysis/scripts/ml/./gid_rt.py:138: 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 smnc_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True) [1. 0.98780488 1. 0.98780488 0.98780488 1. 0.97560976 1. 0.98780488 0.9625 ] mean value: 0.9889329268292683 key: train_roc_auc value: [0.99728261 0.99728261 0.99728261 0.99728261 0.9986413 0.99728261 0.9986413 0.99728261 0.99728629 0.99728629] mean value: 0.9975550842464946 key: test_jcc value: [1. 0.97619048 1. 0.97619048 0.97560976 1. 0.95348837 1. 0.97560976 0.93181818] mean value: 0.978890701848728 key: train_jcc value: [0.99457995 0.99457995 0.99457995 0.99457995 0.99728997 0.99457995 0.99728261 0.99457995 0.99459459 0.99457995] mean value: 0.9951226796786181 MCC on Blind test: 0.1 Accuracy on Blind test: 0.65 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.31079054 0.31409287 0.31281781 0.31339955 0.31390643 0.43118978 0.30926585 0.42711043 0.48189974 0.38591003] mean value: 0.3600383043289185 key: score_time value: [0.01892018 0.01887846 0.01886964 0.01884007 0.01896596 0.02387404 0.01898599 0.02272606 0.01887226 0.01887417] mean value: 0.019780683517456054 key: test_mcc value: [0.97590007 1. 1. 1. 0.97590007 1. 0.95235327 1. 0.97560976 0.95174259] mean value: 0.983150575630985 key: train_mcc value: [0.99185149 0.99185149 0.99185149 0.99185149 0.99188079 0.99185149 0.99185149 0.99185149 0.99186249 0.99457258] mean value: 0.9921276278816327 key: test_accuracy value: [0.98780488 1. 1. 1. 0.98780488 1. 0.97560976 1. 0.98765432 0.97530864] mean value: 0.9914182475158084 key: train_accuracy value: [0.99592391 0.99592391 0.99592391 0.99592391 0.99592391 0.99592391 0.99592391 0.99592391 0.99592944 0.9972863 ] mean value: 0.9960607043832223 key: test_fscore value: [0.98795181 1. 1. 1. 0.98765432 1. 0.97619048 1. 0.98765432 0.97619048] mean value: 0.9915641401585177 key: train_fscore value: [0.99592944 0.99592944 0.99592944 0.99592944 0.99594046 0.99592944 0.99592944 0.99592944 0.99594046 0.99728261] mean value: 0.9960669634692498 key: test_precision value: [0.97619048 1. 1. 1. 1. 1. 0.95348837 1. 0.97560976 0.95348837] mean value: 0.9858776976474084 key: train_precision value: [0.99457995 0.99457995 0.99457995 0.99457995 0.99191375 0.99457995 0.99457995 0.99457995 0.99459459 0.99728261] mean value: 0.9945850570517181 key: test_recall value: [1. 1. 1. 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9975609756097561 key: train_recall value: [0.99728261 0.99728261 0.99728261 0.99728261 1. 0.99728261 0.99728261 0.99728261 0.99728997 0.99728261] mean value: 0.9975550842464946 key: test_roc_auc value: [0.98780488 1. 1. 1. 0.98780488 1. 0.97560976 1. 0.98780488 0.975 ] mean value: 0.9914024390243903 key: train_roc_auc value: [0.99592391 0.99592391 0.99592391 0.99592391 0.99592391 0.99592391 0.99592391 0.99592391 0.9959276 0.99728629] mean value: 0.9960605190291033 key: test_jcc value: [0.97619048 1. 1. 1. 0.97560976 1. 0.95348837 1. 0.97560976 0.95348837] mean value: 0.9834386732571645 key: train_jcc value: [0.99189189 0.99189189 0.99189189 0.99189189 0.99191375 0.99189189 0.99189189 0.99189189 0.99191375 0.99457995] mean value: 0.9921650682304156 MCC on Blind test: 0.1 Accuracy on Blind test: 0.65 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.0329392 0.03201771 0.02906203 0.03408504 0.03161645 0.03559852 0.03268695 0.03535366 0.03019762 0.03441501] mean value: 0.03279721736907959 key: score_time value: [0.01205301 0.01190281 0.01184726 0.01198125 0.01185298 0.01194119 0.01186681 0.01191092 0.01191902 0.0118854 ] mean value: 0.011916065216064453 key: test_mcc value: [1. 1. 1. 1. 0.97590007 1. 0.97590007 1. 0.97560976 0.97559506] mean value: 0.9903004958504097 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 0.98780488 1. 0.98780488 1. 0.98765432 0.98765432] mean value: 0.995091839807287 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 0.98795181 1. 0.98795181 1. 0.98765432 0.98795181] mean value: 0.9951509742674401 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. 0.97619048 1. 0.97619048 1. 0.97560976 0.97619048] mean value: 0.990418118466899 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 0.98780488 1. 0.98780488 1. 0.98780488 0.9875 ] mean value: 0.9950914634146342 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 0.97619048 1. 0.97619048 1. 0.97560976 0.97619048] mean value: 0.990418118466899 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 Accuracy on Blind test: 0.64 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 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( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(random_state=42))]) key: fit_time value: [0.69424486 0.52966881 0.6177032 0.60444522 0.52784967 0.58575439 0.54582286 0.50463963 0.53821635 0.62744951] mean value: 0.5775794506072998 key: score_time value: [0.01205277 0.01202464 0.0120728 0.01207852 0.01209378 0.01279664 0.0121026 0.01207995 0.01209998 0.01939416] mean value: 0.012879586219787598 key: test_mcc value: [1. 1. 1. 1. 0.97590007 1. 0.97590007 1. 0.97560976 0.97559506] mean value: 0.9903004958504097 key: train_mcc value: [0.99728629 1. 1. 1. 1. 1. 1. 1. 0.99458719 1. ] mean value: 0.9991873481454057 key: test_accuracy value: [1. 1. 1. 1. 0.98780488 1. 0.98780488 1. 0.98765432 0.98765432] mean value: 0.995091839807287 key: train_accuracy value: [0.9986413 1. 1. 1. 1. 1. 1. 1. 0.9972863 1. ] mean value: 0.9995927600141584 key: test_fscore value: [1. 1. 1. 1. 0.98795181 1. 0.98795181 1. 0.98765432 0.98795181] mean value: 0.9951509742674401 key: train_fscore value: [0.99864315 1. 1. 1. 1. 1. 1. 1. 0.9972973 1. ] mean value: 0.9995940445194176 key: test_precision value: [1. 1. 1. 1. 0.97619048 1. 0.97619048 1. 0.97560976 0.97619048] mean value: 0.990418118466899 key: train_precision value: [0.99728997 1. 1. 1. 1. 1. 1. 1. 0.99460916 1. ] mean value: 0.9991899137320214 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 0.98780488 1. 0.98780488 1. 0.98780488 0.9875 ] mean value: 0.9950914634146342 key: train_roc_auc value: [0.9986413 1. 1. 1. 1. 1. 1. 1. 0.99728261 1. ] mean value: 0.9995923913043478 key: test_jcc value: [1. 1. 1. 1. 0.97619048 1. 0.97619048 1. 0.97560976 0.97619048] mean value: 0.990418118466899 key: train_jcc value: [0.99728997 1. 1. 1. 1. 1. 1. 1. 0.99460916 1. ] mean value: 0.9991899137320214 MCC on Blind test: 0.04 Accuracy on Blind test: 0.64 Model_name: Gaussian NB Model func: GaussianNB() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01523733 0.01386714 0.01118112 0.01092887 0.01209068 0.01093578 0.01087809 0.01114058 0.01089859 0.0110786 ] mean value: 0.011823678016662597 key: score_time value: [0.01218796 0.00947809 0.00929165 0.00916791 0.00912809 0.00901389 0.00895309 0.00893259 0.00899363 0.00910449] mean value: 0.009425139427185059 key: test_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 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. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 Accuracy on Blind test: 0.64 Model_name: Naive Bayes Model func: BernoulliNB() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01114893 0.01527357 0.01541328 0.01537609 0.01549268 0.01553464 0.01534081 0.01535797 0.01547241 0.01550865] mean value: 0.01499190330505371 key: score_time value: [0.01206446 0.01204586 0.01218605 0.01213098 0.01215172 0.01219511 0.01214266 0.0121603 0.01214004 0.01216531] mean value: 0.0121382474899292 key: test_mcc value: [0.97590007 0.88465174 1. 0.92932038 0.95235327 0.86294893 0.92932038 0.9067647 0.97560976 1. ] mean value: 0.9416869214875435 key: train_mcc value: [0.93933644 0.94447385 0.93422349 0.94963613 0.93933644 0.95222673 0.94190206 0.94963613 0.93685466 0.93431576] mean value: 0.9421941684418391 key: test_accuracy value: [0.98780488 0.93902439 1. 0.96341463 0.97560976 0.92682927 0.96341463 0.95121951 0.98765432 1. ] mean value: 0.9694971394158386 key: train_accuracy value: [0.96875 0.97146739 0.96603261 0.97418478 0.96875 0.97554348 0.9701087 0.97418478 0.96743555 0.9660787 ] mean value: 0.9702535986077517 key: test_fscore value: [0.98795181 0.94252874 1. 0.96470588 0.97619048 0.93181818 0.96470588 0.95348837 0.98765432 1. ] mean value: 0.9709043658656318 key: train_fscore value: [0.96969697 0.97225892 0.96714849 0.97483444 0.96969697 0.97612732 0.97097625 0.97483444 0.96850394 0.96714849] mean value: 0.9711226219264782 key: test_precision value: [0.97619048 0.89130435 1. 0.93181818 0.95348837 0.87234043 0.93181818 0.91111111 0.97560976 1. ] mean value: 0.9443680852486537 key: train_precision value: [0.94117647 0.94601542 0.93638677 0.95090439 0.94117647 0.95336788 0.94358974 0.95090439 0.9389313 0.93638677] mean value: 0.943883960471372 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.98780488 0.93902439 1. 0.96341463 0.97560976 0.92682927 0.96341463 0.95121951 0.98780488 1. ] mean value: 0.9695121951219512 key: train_roc_auc value: [0.96875 0.97146739 0.96603261 0.97418478 0.96875 0.97554348 0.9701087 0.97418478 0.9673913 0.96612466] mean value: 0.9702537704724874 key: test_jcc value: [0.97619048 0.89130435 1. 0.93181818 0.95348837 0.87234043 0.93181818 0.91111111 0.97560976 1. ] mean value: 0.9443680852486537 key: train_jcc value: [0.94117647 0.94601542 0.93638677 0.95090439 0.94117647 0.95336788 0.94358974 0.95090439 0.9389313 0.93638677] mean value: 0.943883960471372 MCC on Blind test: -0.02 Accuracy on Blind test: 0.61 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.02226114 0.01112175 0.01045632 0.01116967 0.01087809 0.01001072 0.01123738 0.01138449 0.0113101 0.01054335] mean value: 0.012037301063537597 key: score_time value: [0.03276396 0.01347852 0.01324391 0.01388717 0.01340795 0.0133779 0.01389813 0.01390147 0.01307583 0.01834106] mean value: 0.01593759059906006 key: test_mcc value: [1. 1. 0.97590007 1. 1. 1. 0.97590007 0.92932038 1. 0.97559506] mean value: 0.9856715579691121 key: train_mcc value: [0.99457991 0.99188079 0.99457991 0.99188079 0.98918887 0.98918887 0.99457991 0.99188079 0.99728995 1. ] mean value: 0.9935049771214682 key: test_accuracy value: [1. 1. 0.98780488 1. 1. 1. 0.98780488 0.96341463 1. 0.98765432] mean value: 0.9926678711231557 key: train_accuracy value: [0.99728261 0.99592391 0.99728261 0.99592391 0.99456522 0.99456522 0.99728261 0.99592391 0.99864315 1. ] mean value: 0.9967393147896879 key: test_fscore value: [1. 1. 0.98795181 1. 1. 1. 0.98795181 0.96470588 1. 0.98795181] mean value: 0.9928561304039688 key: train_fscore value: [0.99728997 0.99594046 0.99728997 0.99594046 0.99459459 0.99459459 0.99728997 0.99594046 0.99864682 1. ] mean value: 0.9967527308159012 key: test_precision value: [1. 1. 0.97619048 1. 1. 1. 0.97619048 0.93181818 1. 0.97619048] mean value: 0.986038961038961 key: train_precision value: [0.99459459 0.99191375 0.99459459 0.99191375 0.98924731 0.98924731 0.99459459 0.99191375 0.9972973 1. ] mean value: 0.9935316944629179 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.98780488 1. 1. 1. 0.98780488 0.96341463 1. 0.9875 ] mean value: 0.9926524390243903 key: train_roc_auc value: [0.99728261 0.99592391 0.99728261 0.99592391 0.99456522 0.99456522 0.99728261 0.99592391 0.9986413 1. ] mean value: 0.9967391304347826 key: test_jcc value: [1. 1. 0.97619048 1. 1. 1. 0.97619048 0.93181818 1. 0.97619048] mean value: 0.986038961038961 key: train_jcc value: [0.99459459 0.99191375 0.99459459 0.99191375 0.98924731 0.98924731 0.99459459 0.99191375 0.9972973 1. ] mean value: 0.9935316944629179 MCC on Blind test: -0.04 Accuracy on Blind test: 0.62 Model_name: SVM Model func: SVC(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.01453543 0.01398969 0.01407623 0.01436734 0.01439548 0.01450872 0.01414347 0.01427746 0.01414585 0.01460624] mean value: 0.014304590225219727 key: score_time value: [0.00989437 0.00991225 0.00994253 0.01007223 0.00993395 0.00997353 0.01007318 0.01002455 0.01004696 0.01010442] mean value: 0.009997797012329102 key: test_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 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. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 Accuracy on Blind test: 0.64 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.63227844 0.68658686 0.69631076 0.60605907 0.56783509 0.74490881 0.64789128 0.58463478 0.59095526 0.75249314] mean value: 0.6509953498840332 key: score_time value: [0.01274347 0.01283765 0.01293373 0.01239657 0.01241398 0.0129447 0.01250648 0.01243639 0.01243687 0.012429 ] mean value: 0.012607884407043458 key: test_mcc value: [1. 1. 1. 1. 0.97590007 1. 0.97590007 1. 0.97560976 0.97559506] mean value: 0.9903004958504097 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 0.98780488 1. 0.98780488 1. 0.98765432 0.98765432] mean value: 0.995091839807287 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 0.98795181 1. 0.98795181 1. 0.98765432 0.98795181] mean value: 0.9951509742674401 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. 0.97619048 1. 0.97619048 1. 0.97560976 0.97619048] mean value: 0.990418118466899 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 0.98780488 1. 0.98780488 1. 0.98780488 0.9875 ] mean value: 0.9950914634146342 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 0.97619048 1. 0.97619048 1. 0.97560976 0.97619048] mean value: 0.990418118466899 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.01 Accuracy on Blind test: 0.63 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 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(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02050948 0.01547027 0.01380348 0.01392651 0.0137167 0.01387787 0.01399446 0.01395702 0.01403189 0.01473165] mean value: 0.014801931381225587 key: score_time value: [0.01198721 0.00945163 0.00899053 0.00889421 0.0090096 0.0093956 0.00908327 0.00903606 0.00903368 0.00904965] mean value: 0.009393143653869628 key: test_mcc value: [1. 1. 0.97590007 1. 0.95235327 1. 1. 1. 1. 1. ] mean value: 0.9928253339434266 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 0.98780488 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9963414634146341 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 0.98795181 1. 0.97619048 1. 1. 1. 1. 1. ] mean value: 0.9964142283419392 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.97619048 1. 0.95348837 1. 1. 1. 1. 1. ] mean value: 0.99296788482835 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.98780488 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9963414634146341 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. 0.97619048 1. 0.95348837 1. 1. 1. 1. 1. ] mean value: 0.99296788482835 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.07 Accuracy on Blind test: 0.63 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.09934592 0.10024786 0.09979486 0.10048461 0.09923458 0.10020065 0.10119796 0.1001811 0.09983778 0.10054326] mean value: 0.10010685920715331 key: score_time value: [0.01766944 0.01795363 0.01804757 0.01796603 0.01820993 0.01779008 0.01808906 0.01779771 0.01781273 0.01796579] mean value: 0.017930197715759277 key: test_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 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. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01134396 0.01072454 0.01176214 0.01080966 0.01096106 0.01094961 0.0109365 0.01086211 0.01096129 0.01087928] mean value: 0.011019015312194824 key: score_time value: [0.00914168 0.00905848 0.00905871 0.00901318 0.00905085 0.00901198 0.00902629 0.00901985 0.00895357 0.00902128] mean value: 0.009035587310791016 key: test_mcc value: [1. 1. 1. 0.97590007 1. 1. 1. 1. 1. 1. ] mean value: 0.9975900072948534 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 0.98780488 1. 1. 1. 1. 1. 1. ] mean value: 0.998780487804878 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 0.98795181 1. 1. 1. 1. 1. 1. ] mean value: 0.9987951807228915 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. 0.97619048 1. 1. 1. 1. 1. 1. ] mean value: 0.9976190476190476 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 0.98780488 1. 1. 1. 1. 1. 1. ] mean value: 0.998780487804878 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 0.97619048 1. 1. 1. 1. 1. 1. ] mean value: 0.9976190476190476 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.17 Accuracy on Blind test: 0.66 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep 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(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, random_state=42))]) key: fit_time value: [1.19025254 1.1910665 1.18577504 1.19194627 1.19458723 1.19427347 1.19646263 1.1843164 1.18629003 1.19279742] mean value: 1.1907767534255982 key: score_time value: [0.09081054 0.09237266 0.09046865 0.09148121 0.09427381 0.09148955 0.09354448 0.0937984 0.09317827 0.09427381] mean value: 0.09256913661956787 key: test_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 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. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 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) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000...05', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', '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: [0.96961617 0.99249053 0.95078969 0.99913931 0.96326542 0.99250197 0.92212391 1.00464892 1.02766109 0.97350574] mean value: 0.9795742750167846 key: score_time value: [0.25640821 0.23247099 0.21121407 0.2550602 0.13452554 0.13529539 0.26445699 0.17845917 0.20959902 0.25679469] mean value: 0.21342842578887938 key: test_mcc value: [1. 1. 0.97590007 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9975900072948534 key: train_mcc value: [0.99728629 0.99728629 1. 0.99728629 0.99728629 0.99728629 0.99728629 0.99728629 0.99728995 0.99728997] mean value: 0.9975583961405413 key: test_accuracy value: [1. 1. 0.98780488 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.998780487804878 key: train_accuracy value: [0.9986413 0.9986413 1. 0.9986413 0.9986413 0.9986413 0.9986413 0.9986413 0.99864315 0.99864315] mean value: 0.9987775426228541 key: test_fscore value: [1. 1. 0.98795181 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9987951807228915 key: train_fscore value: [0.99864315 0.99864315 1. 0.99864315 0.99864315 0.99864315 0.99864315 0.99864315 0.99864682 0.99864315] mean value: 0.9987792003202096 key: test_precision value: [1. 1. 0.97619048 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9976190476190476 key: train_precision value: [0.99728997 0.99728997 1. 0.99728997 0.99728997 0.99728997 0.99728997 0.99728997 0.9972973 0.99728997] mean value: 0.9975617080495129 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.98780488 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.998780487804878 key: train_roc_auc value: [0.9986413 0.9986413 1. 0.9986413 0.9986413 0.9986413 0.9986413 0.9986413 0.9986413 0.99864499] mean value: 0.9987775421232474 key: test_jcc 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)) [1. 1. 0.97619048 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9976190476190476 key: train_jcc value: [0.99728997 0.99728997 1. 0.99728997 0.99728997 0.99728997 0.99728997 0.99728997 0.9972973 0.99728997] mean value: 0.9975617080495129 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Naive Bayes Model func: BernoulliNB() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01321125 0.01969552 0.01535511 0.01532602 0.01544857 0.0153985 0.01541615 0.01543212 0.01560068 0.01556468] mean value: 0.01564486026763916 key: score_time value: [0.01237535 0.01205516 0.01218653 0.01213145 0.01213408 0.01213026 0.01214743 0.0121696 0.01210523 0.01215649] mean value: 0.012159156799316406 key: test_mcc value: [0.97590007 0.88465174 1. 0.92932038 0.95235327 0.86294893 0.92932038 0.9067647 0.97560976 1. ] mean value: 0.9416869214875435 key: train_mcc value: [0.93933644 0.94447385 0.93422349 0.94963613 0.93933644 0.95222673 0.94190206 0.94963613 0.93685466 0.93431576] mean value: 0.9421941684418391 key: test_accuracy value: [0.98780488 0.93902439 1. 0.96341463 0.97560976 0.92682927 0.96341463 0.95121951 0.98765432 1. ] mean value: 0.9694971394158386 key: train_accuracy value: [0.96875 0.97146739 0.96603261 0.97418478 0.96875 0.97554348 0.9701087 0.97418478 0.96743555 0.9660787 ] mean value: 0.9702535986077517 key: test_fscore value: [0.98795181 0.94252874 1. 0.96470588 0.97619048 0.93181818 0.96470588 0.95348837 0.98765432 1. ] mean value: 0.9709043658656318 key: train_fscore value: [0.96969697 0.97225892 0.96714849 0.97483444 0.96969697 0.97612732 0.97097625 0.97483444 0.96850394 0.96714849] mean value: 0.9711226219264782 key: test_precision value: [0.97619048 0.89130435 1. 0.93181818 0.95348837 0.87234043 0.93181818 0.91111111 0.97560976 1. ] mean value: 0.9443680852486537 key: train_precision value: [0.94117647 0.94601542 0.93638677 0.95090439 0.94117647 0.95336788 0.94358974 0.95090439 0.9389313 0.93638677] mean value: 0.943883960471372 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.98780488 0.93902439 1. 0.96341463 0.97560976 0.92682927 0.96341463 0.95121951 0.98780488 1. ] mean value: 0.9695121951219512 key: train_roc_auc value: [0.96875 0.97146739 0.96603261 0.97418478 0.96875 0.97554348 0.9701087 0.97418478 0.9673913 0.96612466] mean value: 0.9702537704724874 key: test_jcc value: [0.97619048 0.89130435 1. 0.93181818 0.95348837 0.87234043 0.93181818 0.91111111 0.97560976 1. ] mean value: 0.9443680852486537 key: train_jcc value: [0.94117647 0.94601542 0.93638677 0.95090439 0.94117647 0.95336788 0.94358974 0.95090439 0.9389313 0.93638677] mean value: 0.943883960471372 MCC on Blind test: -0.02 Accuracy on Blind test: 0.61 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) List of models: /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)) [('Logistic Regression', LogisticRegression(random_state=42)), ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)), ('Gaussian NB', GaussianNB()), ('Naive Bayes', BernoulliNB()), ('K-Nearest Neighbors', KNeighborsClassifier()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000... 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: [0.08123899 0.05527997 0.05502272 0.06461287 0.06690979 0.07206774 0.05670714 0.06016827 0.06766629 0.06786704] mean value: 0.0647540807723999 key: score_time value: [0.01097345 0.01067019 0.01060772 0.01056099 0.01073909 0.01091695 0.01060534 0.01056409 0.0105958 0.01058125] mean value: 0.010681486129760743 key: test_mcc value: [1. 1. 0.97590007 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9975900072948534 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 0.98780488 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.998780487804878 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 0.98795181 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9987951807228915 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.97619048 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9976190476190476 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.98780488 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.998780487804878 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. 0.97619048 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9976190476190476 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: LDA Model func: LinearDiscriminantAnalysis() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.04962325 0.06859589 0.04773283 0.0693655 0.05132008 0.0847261 0.05077052 0.10136676 0.05971193 0.07314157] mean value: 0.06563544273376465 key: score_time value: [0.01876545 0.01226377 0.01248384 0.01230836 0.01890564 0.01234889 0.01252794 0.01917291 0.01230669 0.01629901] mean value: 0.014738249778747558 key: test_mcc value: [1. 0.95235327 1. 0.97590007 1. 0.97590007 0.97590007 0.97590007 0.92852935 0.97559506] mean value: 0.9760077968769325 key: train_mcc value: [0.99728629 0.99457991 0.99728629 0.99728629 0.99728629 0.99728629 1. 0.99728629 0.99728995 0.99458727] mean value: 0.9970174873769332 key: test_accuracy value: [1. 0.97560976 1. 0.98780488 1. 0.98780488 0.98780488 0.98780488 0.96296296 0.98765432] mean value: 0.98774465522433 key: train_accuracy value: [0.9986413 0.99728261 0.9986413 0.9986413 0.9986413 0.9986413 1. 0.9986413 0.99864315 0.9972863 ] mean value: 0.9985059878473246 key: test_fscore value: [1. 0.97619048 1. 0.98795181 1. 0.98795181 0.98795181 0.98795181 0.96385542 0.98795181] mean value: 0.9879804934021801 key: train_fscore value: [0.99864315 0.99728997 0.99864315 0.99864315 0.99864315 0.99864315 1. 0.99864315 0.99864682 0.99728997] mean value: 0.9985085653207797 key: test_precision value: [1. 0.95348837 1. 0.97619048 1. 0.97619048 0.97619048 0.97619048 0.93023256 0.97619048] mean value: 0.9764673311184939 key: train_precision value: [0.99728997 0.99459459 0.99728997 0.99728997 0.99728997 0.99728997 1. 0.99728997 0.9972973 0.99459459] mean value: 0.9970226323884861 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.97560976 1. 0.98780488 1. 0.98780488 0.98780488 0.98780488 0.96341463 0.9875 ] mean value: 0.9877743902439025 key: train_roc_auc value: [0.9986413 0.99728261 0.9986413 0.9986413 0.9986413 0.9986413 1. 0.9986413 0.9986413 0.99728997] mean value: 0.9985061712030164 key: test_jcc value: [1. 0.95348837 1. 0.97619048 1. 0.97619048 0.97619048 0.97619048 0.93023256 0.97619048] mean value: 0.9764673311184939 key: train_jcc value: [0.99728997 0.99459459 0.99728997 0.99728997 0.99728997 0.99728997 1. 0.99728997 0.9972973 0.99459459] mean value: 0.9970226323884861 MCC on Blind test: 0.15 Accuracy on Blind test: 0.66 Model_name: Multinomial Model func: MultinomialNB() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01434016 0.01459646 0.01467919 0.01464367 0.01464581 0.01465583 0.01470542 0.01461792 0.01474738 0.01468301] mean value: 0.01463148593902588 key: score_time value: [0.01197648 0.01193285 0.01184535 0.01183891 0.0118978 0.01192427 0.01189637 0.0118649 0.01187253 0.01188493] mean value: 0.011893439292907714 key: test_mcc value: [0.95235327 0.95235327 0.95235327 0.97590007 0.97590007 0.95235327 0.97590007 0.88465174 0.95180006 0.97559506] mean value: 0.9549160133358575 key: train_mcc value: [0.96003699 0.95742711 0.95742711 0.95482371 0.95482371 0.96003699 0.95742711 0.9626534 0.96008794 0.95229309] mean value: 0.9577037142339239 key: test_accuracy value: [0.97560976 0.97560976 0.97560976 0.98780488 0.98780488 0.97560976 0.98780488 0.93902439 0.97530864 0.98765432] mean value: 0.976784101174345 key: train_accuracy value: [0.97961957 0.97826087 0.97826087 0.97690217 0.97690217 0.97961957 0.97826087 0.98097826 0.97964722 0.97557666] mean value: 0.9784028228423102 key: test_fscore value: [0.97619048 0.97619048 0.97619048 0.98795181 0.98795181 0.97619048 0.98795181 0.94252874 0.97560976 0.98795181] mean value: 0.9774707625407313 key: train_fscore value: [0.98002663 0.9787234 0.9787234 0.97742364 0.97742364 0.98002663 0.9787234 0.98133333 0.98007968 0.97612732] mean value: 0.978861108820245 key: test_precision value: [0.95348837 0.95348837 0.95348837 0.97619048 0.97619048 0.95348837 0.97619048 0.89130435 0.95238095 0.97619048] mean value: 0.9562400693341037 key: train_precision value: [0.96083551 0.95833333 0.95833333 0.95584416 0.95584416 0.96083551 0.95833333 0.96335079 0.9609375 0.95336788] mean value: 0.9586015490953057 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97560976 0.97560976 0.97560976 0.98780488 0.98780488 0.97560976 0.98780488 0.93902439 0.97560976 0.9875 ] mean value: 0.9767987804878049 key: train_roc_auc value: [0.97961957 0.97826087 0.97826087 0.97690217 0.97690217 0.97961957 0.97826087 0.98097826 0.97961957 0.97560976] mean value: 0.9784033669141039 key: test_jcc value: [0.95348837 0.95348837 0.95348837 0.97619048 0.97619048 0.95348837 0.97619048 0.89130435 0.95238095 0.97619048] mean value: 0.9562400693341037 key: train_jcc value: [0.96083551 0.95833333 0.95833333 0.95584416 0.95584416 0.96083551 0.95833333 0.96335079 0.9609375 0.95336788] mean value: 0.9586015490953057 MCC on Blind test: 0.08 Accuracy on Blind test: 0.64 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.01940751 0.02054787 0.02020812 0.02052116 0.01922226 0.0192306 0.01953673 0.01883125 0.01966619 0.01933861] mean value: 0.019651031494140624 key: score_time value: [0.01198006 0.01188064 0.01186681 0.0118916 0.0118022 0.01185632 0.01184344 0.01190782 0.01186085 0.01186776] mean value: 0.011875748634338379 key: test_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 0.97560976 1. ] mean value: 0.9975609756097561 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 0.98765432 1. ] mean value: 0.9987654320987654 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 0.98765432 1. ] mean value: 0.9987654320987654 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. 0.97560976 1. ] mean value: 0.9975609756097561 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 0.98780488 1. ] mean value: 0.998780487804878 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 0.97560976 1. ] mean value: 0.9975609756097561 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 Accuracy on Blind test: 0.65 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=10, random_state=42) List of models: /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)) [('Logistic Regression', LogisticRegression(random_state=42)), ('Logistic RegressionCV', LogisticRegressionCV(random_state=42)), ('Gaussian NB', GaussianNB()), ('Naive Bayes', BernoulliNB()), ('K-Nearest Neighbors', KNeighborsClassifier()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=10, random_state=42))]) key: fit_time value: [0.0162499 0.01603103 0.01658034 0.01598787 0.01594448 0.01596522 0.01621485 0.01603603 0.01626325 0.01672888] mean value: 0.01620018482208252 key: score_time value: [0.01212835 0.01185083 0.01184201 0.01177883 0.01184797 0.01181841 0.01181817 0.01187229 0.0117836 0.01182961] mean value: 0.011857008934020996 key: test_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 0.97560976 1. ] mean value: 0.9975609756097561 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 0.98765432 1. ] mean value: 0.9987654320987654 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 0.98765432 1. ] mean value: 0.9987654320987654 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. 0.97560976 1. ] mean value: 0.9975609756097561 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 0.98780488 1. ] mean value: 0.998780487804878 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 0.97560976 1. ] mean value: 0.9975609756097561 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:747: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_bagging.py:753: RuntimeWarning: invalid value encountered in true_divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.1933639 0.17780399 0.17868209 0.17682266 0.17803907 0.17734218 0.18125486 0.1778419 0.1783545 0.17764473] mean value: 0.17971498966217042 key: score_time value: [0.01552391 0.01565957 0.01562071 0.01566958 0.01566577 0.0158267 0.01566458 0.01561618 0.01578569 0.01569843] mean value: 0.015673112869262696 key: test_mcc value: [1. 1. 0.97590007 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9975900072948534 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 0.98780488 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.998780487804878 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 0.98795181 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9987951807228915 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.97619048 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9976190476190476 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.98780488 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.998780487804878 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. 0.97619048 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9976190476190476 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 Accuracy on Blind test: 0.65 Model_name: Bagging Classifier Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))]) key: fit_time value: [0.15212679 0.16374302 0.16546059 0.16883278 0.16681457 0.056422 0.16603708 0.15502191 0.15446663 0.04659557] mean value: 0.13955209255218506 key: score_time value: [0.03932214 0.03972292 0.04102039 0.03985643 0.04061651 0.0379293 0.03316736 0.03227663 0.03358197 0.03551793] mean value: 0.037301158905029295 key: test_mcc value: [1. 1. 0.97590007 1. 0.95235327 1. 1. 1. 0.97560976 1. ] mean value: 0.9903863095531827 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 0.98780488 1. 0.97560976 1. 1. 1. 0.98765432 1. ] mean value: 0.9951068955133996 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 0.98795181 1. 0.97619048 1. 1. 1. 0.98765432 1. ] mean value: 0.9951796604407046 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.97619048 1. 0.95348837 1. 1. 1. 0.97560976 1. ] mean value: 0.9905288604381061 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.98780488 1. 0.97560976 1. 1. 1. 0.98780488 1. ] mean value: 0.9951219512195122 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. 0.97619048 1. 0.95348837 1. 1. 1. 0.97560976 1. ] mean value: 0.9905288604381061 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test:/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)) 0.0 Accuracy on Blind test: 0.64 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.23483968 0.2919898 0.28060555 0.40334725 0.45062828 0.42588902 0.36511922 0.29585052 0.51436138 0.32489538] mean value: 0.35875260829925537 key: score_time value: [0.01855564 0.01846242 0.03192854 0.03131938 0.03152657 0.03708315 0.01892447 0.03142667 0.01882124 0.03217816] mean value: 0.027022624015808107 key: test_mcc value: [1. 0.97590007 1. 1. 1. 1. 1. 0.97590007 1. 1. ] mean value: 0.9951800145897066 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 0.99728629 1. 1. ] mean value: 0.9997286290790893 key: test_accuracy value: [1. 0.98780488 1. 1. 1. 1. 1. 0.98780488 1. 1. ] mean value: 0.9975609756097561 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 0.9986413 1. 1. ] mean value: 0.9998641304347826 key: test_fscore value: [1. 0.98795181 1. 1. 1. 1. 1. 0.98795181 1. 1. ] mean value: 0.9975903614457832 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 0.99864315 1. 1. ] mean value: 0.999864314789688 key: test_precision value: [1. 0.97619048 1. 1. 1. 1. 1. 0.97619048 1. 1. ] mean value: 0.9952380952380953 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 0.99728997 1. 1. ] mean value: 0.9997289972899729 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.98780488 1. 1. 1. 1. 1. 0.98780488 1. 1. ] mean value: 0.9975609756097561 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 0.9986413 1. 1. ] mean value: 0.9998641304347826 key: test_jcc value: [1. 0.97619048 1. 1. 1. 1. 1. 0.97619048 1. 1. ] mean value: 0.9952380952380953 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 0.99728997 1. 1. ] mean value: 0.9997289972899729 MCC on Blind test: -0.01 Accuracy on Blind test: 0.63 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/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)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.37295341 0.58469367 0.35287929 0.59070086 0.34697008 0.57370162 0.57505512 0.58783317 0.58660769 0.58256197] mean value: 0.5153956890106202 key: score_time value: [0.01013303 0.00937486 0.0092721 0.00929713 0.00996041 0.00939012 0.00961304 0.00950384 0.00951195 0.00951266] mean value: 0.009556913375854492 key: test_mcc value: [0.97590007 1. 0.97590007 1. 0.95235327 1. 1. 1. 1. 1. ] mean value: 0.99041534123828 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.98780488 1. 0.98780488 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9951219512195122 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.98795181 1. 0.98795181 1. 0.97619048 1. 1. 1. 1. 1. ] mean value: 0.9952094090648308 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.97619048 1. 0.97619048 1. 0.95348837 1. 1. 1. 1. 1. ] mean value: 0.9905869324473976 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.98780488 1. 0.98780488 1. 0.97560976 1. 1. 1. 1. 1. ] mean value: 0.9951219512195122 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.97619048 1. 0.97619048 1. 0.95348837 1. 1. 1. 1. 1. ] mean value: 0.9905869324473976 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.03872991 0.03791022 0.03865004 0.03827381 0.04180598 0.04633164 0.038692 0.0389986 0.04175949 0.04024053] mean value: 0.04013922214508057 key: score_time value: [0.02073717 0.01299286 0.01280546 0.01337028 0.01272225 0.01557684 0.01273084 0.01278329 0.017133 0.01732206] mean value: 0.014817404747009277 key: test_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 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. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 Accuracy on Blind test: 0.64 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02562404 0.01709557 0.01693654 0.017066 0.03375077 0.0413425 0.04174829 0.04163456 0.0395155 0.04287601] mean value: 0.03175897598266601 key: score_time value: [0.01889253 0.01219988 0.01222539 0.01215148 0.01892686 0.01885581 0.02128983 0.02113318 0.01900244 0.01880312] mean value: 0.017348051071166992 key: test_mcc value: [1. 0.97590007 1. 0.97590007 1. 1. 0.95235327 1. 0.97560976 0.95174259] mean value: 0.983150575630985 key: train_mcc value: [0.99728629 0.99728629 0.99457991 0.99457991 0.99728629 0.99728629 0.99728629 0.99728629 0.99458719 0.99728997] mean value: 0.9964754720077219 key: test_accuracy value: [1. 0.98780488 1. 0.98780488 1. 1. 0.97560976 1. 0.98765432 0.97530864] mean value: 0.9914182475158084 key: train_accuracy value: [0.9986413 0.9986413 0.99728261 0.99728261 0.9986413 0.9986413 0.9986413 0.9986413 0.9972863 0.99864315] mean value: 0.9982342487168898 key: test_fscore value: [1. 0.98795181 1. 0.98795181 1. 1. 0.97619048 1. 0.98765432 0.97619048] mean value: 0.9915938887826438 key: train_fscore value: [0.99864315 0.99864315 0.99728997 0.99728997 0.99864315 0.99864315 0.99864315 0.99864315 0.9972973 0.99864315] mean value: 0.9982379278374909 key: test_precision value: [1. 0.97619048 1. 0.97619048 1. 1. 0.95348837 1. 0.97560976 0.95348837] mean value: 0.983496745266456 key: train_precision value: [0.99728997 0.99728997 0.99459459 0.99459459 0.99728997 0.99728997 0.99728997 0.99728997 0.99460916 0.99728997] mean value: 0.9964828163907777 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.98780488 1. 0.98780488 1. 1. 0.97560976 1. 0.98780488 0.975 ] mean value: 0.9914024390243903 key: train_roc_auc value: [0.9986413 0.9986413 0.99728261 0.99728261 0.9986413 0.9986413 0.9986413 0.9986413 0.99728261 0.99864499] mean value: 0.9982340638623778 key: test_jcc value: [1. 0.97619048 1. 0.97619048 1. 1. 0.95348837 1. 0.97560976 0.95348837] mean value: 0.983496745266456 key: train_jcc value: [0.99728997 0.99728997 0.99459459 0.99459459 0.99728997 0.99728997 0.99728997 0.99728997 0.99460916 0.99728997] mean value: 0.9964828163907777 MCC on Blind test: 0.15 Accuracy on Blind test: 0.66 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=10) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('XGBoost', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=12, num_parallel_tree=1, predictor='auto', random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', use_label_encoder=False, validate_parameters=1, 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 model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/./gid_rt.py:155: 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 ros_CT.sort_values(by = ['test_mcc'], ascending = False, inplace = True) /home/tanu/git/LSHTM_analysis/scripts/ml/./gid_rt.py:158: 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 ros_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=10))]) key: fit_time value: [0.25911665 0.39674997 0.37813807 0.31950808 0.33281422 0.39413953 0.32855153 0.34012485 0.31728721 0.34709024] mean value: 0.3413520336151123 key: score_time value: [0.01918983 0.01939535 0.0202353 0.01941204 0.01946306 0.01990223 0.02010703 0.01912546 0.01892161 0.01906776] mean value: 0.019481968879699708 key: test_mcc value: [1. 0.97590007 1. 1. 1. 1. 0.95235327 1. 0.97560976 0.95174259] mean value: 0.9855605683361317 key: train_mcc value: [0.99457991 0.99728629 0.99457991 0.99457991 0.99457991 0.99457991 0.99728629 0.99457991 0.99458719 0.99728997] mean value: 0.9953929180451583 key: test_accuracy value: [1. 0.98780488 1. 1. 1. 1. 0.97560976 1. 0.98765432 0.97530864] mean value: 0.9926377597109304 key: train_accuracy value: [0.99728261 0.9986413 0.99728261 0.99728261 0.99728261 0.99728261 0.9986413 0.99728261 0.9972863 0.99864315] mean value: 0.9976907704560203 key: test_fscore value: [1. 0.98795181 1. 1. 1. 1. 0.97619048 1. 0.98765432 0.97619048] mean value: 0.9927987080597522 key: train_fscore value: [0.99728997 0.99864315 0.99728997 0.99728997 0.99728997 0.99728997 0.99864315 0.99728997 0.9972973 0.99864315] mean value: 0.9976966578386308 key: test_precision value: [1. 0.97619048 1. 1. 1. 1. 0.95348837 1. 0.97560976 0.95348837] mean value: 0.9858776976474084 key: train_precision value: [0.99459459 0.99728997 0.99459459 0.99459459 0.99459459 0.99459459 0.99728997 0.99459459 0.99460916 0.99728997] mean value: 0.995404665068724 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.98780488 1. 1. 1. 1. 0.97560976 1. 0.98780488 0.975 ] mean value: 0.9926219512195122 key: train_roc_auc value: [0.99728261 0.9986413 0.99728261 0.99728261 0.99728261 0.99728261 0.9986413 0.99728261 0.99728261 0.99864499] mean value: 0.9976905856015081 key: test_jcc value: [1. 0.97619048 1. 1. 1. 1. 0.95348837 1. 0.97560976 0.95348837] mean value: 0.9858776976474084 key: train_jcc value: [0.99459459 0.99728997 0.99459459 0.99459459 0.99459459 0.99459459 0.99728997 0.99459459 0.99460916 0.99728997] mean value: 0.995404665068724 MCC on Blind test: 0.15 Accuracy on Blind test: 0.66 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) 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()), ('SVM', 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)), ('Naive Bayes', BernoulliNB()), ('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 model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['ligand_distance', 'ligand_affinity_change', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'mmcsm_lig', 'contacts', 'mcsm_na_affinity', 'rsa', ... 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'drtype_mode_labels', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) Traceback (most recent call last): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/joblib/parallel.py", line 822, in dispatch_one_batch tasks = self._ready_batches.get(block=False) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/queue.py", line 168, in get raise Empty _queue.Empty During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/tanu/git/LSHTM_analysis/scripts/ml/./gid_rt.py", line 165, in mm_skf_scoresD4 = MultModelsCl(input_df = X_rus File "/home/tanu/git/LSHTM_analysis/scripts/ml/MultModelsCl.py", line 218, in MultModelsCl skf_cv_mod = cross_validate(model_pipeline File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 266, in cross_validate results = parallel( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/joblib/parallel.py", line 1043, in __call__ if self.dispatch_one_batch(iterator): File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/joblib/parallel.py", line 833, in dispatch_one_batch islice = list(itertools.islice(iterator, big_batch_size)) File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_validation.py", line 266, in results = parallel( File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/model_selection/_split.py", line 333, in split raise ValueError( ValueError: Cannot have number of splits n_splits=10 greater than the number of samples: n_samples=6.