24347 lines
823 KiB
Text
24347 lines
823 KiB
Text
/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_7030.py:464: SettingWithCopyWarning:
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A value is trying to be set on a copy of a slice from a DataFrame
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See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
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mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True)
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/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.
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from pandas import MultiIndex, Int64Index
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1.22.4
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1.4.1
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aaindex_df contains non-numerical data
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Total no. of non-numerial columns: 2
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Selecting numerical data only
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PASS: successfully selected numerical columns only for aaindex_df
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Now checking for NA in the remaining aaindex_cols
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Counting aaindex_df cols with NA
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ncols with NA: 4 columns
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Dropping these...
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Original ncols: 127
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Revised df ncols: 123
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Checking NA in revised df...
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PASS: cols with NA successfully dropped from aaindex_df
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Proceeding with combining aa_df with other features_df
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PASS: ncols match
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Expected ncols: 123
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Got: 123
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Total no. of columns in clean aa_df: 123
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Proceeding to merge, expected nrows in merged_df: 531
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PASS: my_features_df and aa_df successfully combined
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nrows: 531
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ncols: 286
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count of NULL values before imputation
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or_mychisq 263
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log10_or_mychisq 263
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dtype: int64
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count of NULL values AFTER imputation
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mutationinformation 0
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or_rawI 0
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logorI 0
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dtype: int64
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PASS: OR values imputed, data ready for ML
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Total no. of features for aaindex: 123
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PASS: x_features has no target variable
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No. of columns for x_features: 173
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PASS: ML data with input features, training and test generated...
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Total no. of input features: 173
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--------No. of numerical features: 167
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--------No. of categorical features: 6
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Total no. of evolutionary features: 3
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Total no. of stability features: 28
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--------Common stabilty cols: 5
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--------Foldx cols: 23
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Total no. of affinity features: 4
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--------Common affinity cols: 3
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--------Gene specific affinity cols: 1
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Total no. of residue level features: 132
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--------AA index cols: 123
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--------Residue Prop cols: 3
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--------AA change Prop cols: 6
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Total no. of genomic features: 6
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--------MAF+OR cols: 2
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--------Lineage cols: 4
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--------Other cols: 0
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-------------------------------------------------------------
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Successfully split data: ALL features
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actual values: training set
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imputed values: blind test set
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Total data size: 119
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Train data size: (79, 173)
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y_train numbers: Counter({0: 50, 1: 29})
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Test data size: (40, 173)
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y_test_numbers: Counter({0: 26, 1: 14})
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y_train ratio: 1.7241379310344827
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y_test ratio: 1.8571428571428572
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-------------------------------------------------------------
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index: 0
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ind: 1
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Mask count check: True
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index: 1
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ind: 2
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Mask count check: True
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Original Data
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Counter({0: 50, 1: 29}) Data dim: (79, 173)
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Simple Random OverSampling
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Counter({1: 50, 0: 50})
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(100, 173)
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Simple Random UnderSampling
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Counter({0: 29, 1: 29})
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(58, 173)
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Simple Combined Over and UnderSampling
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Counter({0: 50, 1: 50})
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(100, 173)
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SMOTE_NC OverSampling
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Counter({1: 50, 0: 50})
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(100, 173)
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#####################################################################
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Running ML analysis: feature groups
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Gene name: gid
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Drug name: streptomycin
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Output directory: /home/tanu/git/Data/streptomycin/output/ml/tts_7030/
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==============================================================
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Running several classification models (n): 24
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List of models:
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('Logistic Regression', LogisticRegression(random_state=42))
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('Logistic RegressionCV', LogisticRegressionCV(random_state=42))
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('Gaussian NB', GaussianNB())
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('Naive Bayes', BernoulliNB())
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('K-Nearest Neighbors', KNeighborsClassifier())
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('SVC', SVC(random_state=42))
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('MLP', MLPClassifier(max_iter=500, random_state=42))
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('Decision Tree', DecisionTreeClassifier(random_state=42))
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('Extra Trees', ExtraTreesClassifier(random_state=42))
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('Extra Tree', ExtraTreeClassifier(random_state=42))
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('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42))
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('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5,
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n_estimators=1000, n_jobs=10, oob_score=True,
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random_state=42))
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('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
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colsample_bynode=None, colsample_bytree=None,
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enable_categorical=False, gamma=None, gpu_id=None,
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importance_type=None, interaction_constraints=None,
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learning_rate=None, max_delta_step=None, max_depth=None,
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min_child_weight=None, missing=nan, monotone_constraints=None,
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n_estimators=100, n_jobs=None, num_parallel_tree=None,
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predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
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scale_pos_weight=None, subsample=None, tree_method=None,
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use_label_encoder=False, validate_parameters=None, verbosity=0))
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('LDA', LinearDiscriminantAnalysis())
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('Multinomial', MultinomialNB())
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('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42))
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('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42))
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('AdaBoost Classifier', AdaBoostClassifier(random_state=42))
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('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))
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('Gaussian Process', GaussianProcessClassifier(random_state=42))
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('Gradient Boosting', GradientBoostingClassifier(random_state=42))
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('QDA', QuadraticDiscriminantAnalysis())
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('Ridge Classifier', RidgeClassifier(random_state=42))
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('Ridge ClassifierCV', RidgeClassifierCV(cv=10))
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================================================================
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Running classifier: 1
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Model_name: Logistic Regression
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Model func: LogisticRegression(random_state=42)
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Running model pipeline: Pipeline(steps=[('prep',
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ColumnTransformer(remainder='passthrough',
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transformers=[('num', MinMaxScaler(),
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Index(['consurf_score', 'snap2_score', 'provean_score',
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'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
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'contacts', 'electro_rr', 'electro_mm',
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...
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'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
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'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
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'lineage_count_unique'],
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dtype='object', length=167)),
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('cat', OneHotEncoder(),
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Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
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'polarity_change', 'water_change', 'active_site'],
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dtype='object'))])),
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('model', LogisticRegression(random_state=42))])
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
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STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
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Increase the number of iterations (max_iter) or scale the data as shown in:
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https://scikit-learn.org/stable/modules/preprocessing.html
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Please also refer to the documentation for alternative solver options:
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https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
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n_iter_i = _check_optimize_result(
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
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STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
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|
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Increase the number of iterations (max_iter) or scale the data as shown in:
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https://scikit-learn.org/stable/modules/preprocessing.html
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Please also refer to the documentation for alternative solver options:
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https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
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n_iter_i = _check_optimize_result(
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
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STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
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Increase the number of iterations (max_iter) or scale the data as shown in:
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https://scikit-learn.org/stable/modules/preprocessing.html
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Please also refer to the documentation for alternative solver options:
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https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
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n_iter_i = _check_optimize_result(
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
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STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
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|
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Increase the number of iterations (max_iter) or scale the data as shown in:
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https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
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https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
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n_iter_i = _check_optimize_result(
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
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STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
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|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
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https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
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n_iter_i = _check_optimize_result(
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
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_warn_prf(average, modifier, msg_start, len(result))
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
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_warn_prf(average, modifier, msg_start, len(result))
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
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STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
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https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
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https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
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n_iter_i = _check_optimize_result(
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
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STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
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STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
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n_iter_i = _check_optimize_result(
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
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STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
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STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
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STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
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STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
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/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
|
|
key: fit_time
|
|
value: [0.0242238 0.03297615 0.02694082 0.02446318 0.02704215 0.04720712
|
|
0.03565717 0.02282596 0.04037237 0.05070901]
|
|
|
|
mean value: 0.033241772651672365
|
|
|
|
key: score_time
|
|
value: [0.0121789 0.01169276 0.01170754 0.011518 0.01153922 0.0120914
|
|
0.01173162 0.01155663 0.01343775 0.0119257 ]
|
|
|
|
mean value: 0.011937952041625977
|
|
|
|
key: test_mcc
|
|
value: [ 0.74535599 0.46666667 0.74535599 -0.06666667 0.46666667 0.25819889
|
|
0.48795004 -0.29277002 0.48795004 0.3 ]
|
|
|
|
mean value: 0.35987075924776607
|
|
|
|
key: train_mcc
|
|
value: [0.94010481 0.91067388 0.94010481 0.94010481 0.90865445 0.96986363
|
|
0.91067388 0.90865445 0.96986363 0.97058178]
|
|
|
|
mean value: 0.9369280134082192
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.66666667 0.8 0.33333333 0.66666667 0.57142857
|
|
0.5 0. 0.5 0.5 ]
|
|
|
|
mean value: 0.5338095238095238
|
|
|
|
key: train_fscore
|
|
value: [0.96 0.93877551 0.96 0.96 0.94117647 0.98039216
|
|
0.93877551 0.94117647 0.98039216 0.98113208]
|
|
|
|
mean value: 0.9581820350781823
|
|
|
|
key: test_precision
|
|
value: [1. 0.66666667 1. 0.33333333 0.66666667 0.5
|
|
1. 0. 1. 0.5 ]
|
|
|
|
mean value: 0.6666666666666666
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 0.96 1. 1. 0.96 1. 1. ]
|
|
|
|
mean value: 0.992
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.66666667 0.66666667 0.33333333 0.66666667 0.66666667
|
|
0.33333333 0. 0.33333333 0.5 ]
|
|
|
|
mean value: 0.4833333333333333
|
|
|
|
key: train_recall
|
|
value: [0.92307692 0.88461538 0.92307692 0.92307692 0.92307692 0.96153846
|
|
0.88461538 0.92307692 0.96153846 0.96296296]
|
|
|
|
mean value: 0.9270655270655273
|
|
|
|
key: test_accuracy
|
|
value: [0.875 0.75 0.875 0.5 0.75 0.625
|
|
0.75 0.5 0.75 0.71428571]
|
|
|
|
mean value: 0.7089285714285715
|
|
|
|
key: train_accuracy
|
|
value: [0.97183099 0.95774648 0.97183099 0.97183099 0.95774648 0.98591549
|
|
0.95774648 0.95774648 0.98591549 0.98611111]
|
|
|
|
mean value: 0.9704420970266041
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 0.73333333 0.83333333 0.46666667 0.73333333 0.63333333
|
|
0.66666667 0.4 0.66666667 0.65 ]
|
|
|
|
mean value: 0.6616666666666667
|
|
|
|
key: train_roc_auc
|
|
value: [0.96153846 0.94230769 0.96153846 0.96153846 0.95042735 0.98076923
|
|
0.94230769 0.95042735 0.98076923 0.98148148]
|
|
|
|
mean value: 0.9613105413105412
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.5 0.66666667 0.2 0.5 0.4
|
|
0.33333333 0. 0.33333333 0.33333333]
|
|
|
|
mean value: 0.39333333333333337
|
|
|
|
key: train_jcc
|
|
value: [0.92307692 0.88461538 0.92307692 0.92307692 0.88888889 0.96153846
|
|
0.88461538 0.88888889 0.96153846 0.96296296]
|
|
|
|
mean value: 0.9202279202279204
|
|
|
|
key: TN
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: FP
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.36
|
|
|
|
Accuracy on Blind test: 0.72
|
|
|
|
Running classifier: 2
|
|
Model_name: Logistic RegressionCV
|
|
Model func: LogisticRegressionCV(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegressionCV(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.92556214 0.62680626 0.74799728 0.69733357 0.53420568 0.79944897
|
|
0.80795002 0.70474362 0.61180806 0.56007266]
|
|
|
|
mean value: 0.7015928268432617
|
|
|
|
key: score_time
|
|
value: [0.01263237 0.01342344 0.01227355 0.01217771 0.01460266 0.01221347
|
|
0.01453018 0.01216531 0.01223826 0.01225662]
|
|
|
|
mean value: 0.012851357460021973
|
|
|
|
key: test_mcc
|
|
value: [ 0. 0.46666667 0.48795004 -0.06666667 0.46666667 0.6
|
|
1. 0. 0.1490712 0. ]
|
|
|
|
mean value: 0.3103687901640919
|
|
|
|
key: train_mcc
|
|
value: [0. 1. 0.79523358 0.72919664 1. 1.
|
|
1. 0.72919664 0.96986363 0. ]
|
|
|
|
mean value: 0.7223490489333553
|
|
|
|
key: test_fscore
|
|
value: [0. 0.66666667 0.5 0.33333333 0.66666667 0.75
|
|
1. 0. 0.4 0. ]
|
|
|
|
mean value: 0.43166666666666664
|
|
|
|
key: train_fscore
|
|
value: [0. 1. 0.84444444 0.8 1. 1.
|
|
1. 0.8 0.98039216 0. ]
|
|
|
|
mean value: 0.742483660130719
|
|
|
|
key: test_precision
|
|
value: [0. 0.66666667 1. 0.33333333 0.66666667 0.6
|
|
1. 0. 0.5 0. ]
|
|
|
|
mean value: 0.4766666666666667
|
|
|
|
key: train_precision
|
|
value: [0. 1. 1. 0.94736842 1. 1.
|
|
1. 0.94736842 1. 0. ]
|
|
|
|
mean value: 0.7894736842105263
|
|
|
|
key: test_recall
|
|
value: [0. 0.66666667 0.33333333 0.33333333 0.66666667 1.
|
|
1. 0. 0.33333333 0. ]
|
|
|
|
mean value: 0.4333333333333333
|
|
|
|
key: train_recall
|
|
value: [0. 1. 0.73076923 0.69230769 1. 1.
|
|
1. 0.69230769 0.96153846 0. ]
|
|
|
|
mean value: 0.7076923076923076
|
|
|
|
key: test_accuracy
|
|
value: [0.625 0.75 0.75 0.5 0.75 0.75
|
|
1. 0.625 0.625 0.71428571]
|
|
|
|
mean value: 0.7089285714285715
|
|
|
|
key: train_accuracy
|
|
value: [0.63380282 1. 0.90140845 0.87323944 1. 1.
|
|
1. 0.87323944 0.98591549 0.625 ]
|
|
|
|
mean value: 0.8892605633802816
|
|
|
|
key: test_roc_auc
|
|
value: [0.5 0.73333333 0.66666667 0.46666667 0.73333333 0.8
|
|
1. 0.5 0.56666667 0.5 ]
|
|
|
|
mean value: 0.6466666666666667
|
|
|
|
key: train_roc_auc
|
|
value: [0.5 1. 0.86538462 0.83504274 1. 1.
|
|
1. 0.83504274 0.98076923 0.5 ]
|
|
|
|
mean value: 0.8516239316239316
|
|
|
|
key: test_jcc
|
|
value: [0. 0.5 0.33333333 0.2 0.5 0.6
|
|
1. 0. 0.25 0. ]
|
|
|
|
mean value: 0.3383333333333333
|
|
|
|
key: train_jcc
|
|
value: [0. 1. 0.73076923 0.66666667 1. 1.
|
|
1. 0.66666667 0.96153846 0. ]
|
|
|
|
mean value: 0.7025641025641025
|
|
|
|
key: TN
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: FP
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.39
|
|
|
|
Accuracy on Blind test: 0.72
|
|
|
|
Running classifier: 3
|
|
Model_name: Gaussian NB
|
|
Model func: GaussianNB()
|
|
|
|
Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianNB())])
|
|
|
|
key: fit_time
|
|
value: [0.01347423 0.01884365 0.00855184 0.00828695 0.00806451 0.00827241
|
|
0.00818229 0.00826859 0.00821042 0.00816202]
|
|
|
|
mean value: 0.009831690788269043
|
|
|
|
key: score_time
|
|
value: [0.01358652 0.00921679 0.00873518 0.00868773 0.00849938 0.00838518
|
|
0.00836921 0.00838017 0.00841665 0.00839853]
|
|
|
|
mean value: 0.009067535400390625
|
|
|
|
key: test_mcc
|
|
value: [-0.1490712 0. -0.1490712 -0.46666667 0. 0.6
|
|
0.06666667 -0.06666667 -0.46666667 0.25819889]
|
|
|
|
mean value: -0.03732768405861442
|
|
|
|
key: train_mcc
|
|
value: [0.53350008 0.46369578 0.44297451 0.61021596 0.39343135 0.5954372
|
|
0.40122444 0.55355298 0.55355298 0.54074074]
|
|
|
|
mean value: 0.5088326024907828
|
|
|
|
key: test_fscore
|
|
value: [0.44444444 0.54545455 0.44444444 0.25 0.54545455 0.75
|
|
0.5 0.33333333 0.25 0.5 ]
|
|
|
|
mean value: 0.45631313131313134
|
|
|
|
key: train_fscore
|
|
value: [0.71428571 0.67567568 0.65822785 0.75757576 0.64102564 0.74285714
|
|
0.64864865 0.72463768 0.72463768 0.72222222]
|
|
|
|
mean value: 0.7009794012710908
|
|
|
|
key: test_precision
|
|
value: [0.33333333 0.375 0.33333333 0.2 0.375 0.6
|
|
0.4 0.33333333 0.2 0.33333333]
|
|
|
|
mean value: 0.3483333333333334
|
|
|
|
key: train_precision
|
|
value: [0.66666667 0.52083333 0.49056604 0.625 0.48076923 0.59090909
|
|
0.5 0.58139535 0.58139535 0.57777778]
|
|
|
|
mean value: 0.5615312834866366
|
|
|
|
key: test_recall
|
|
value: [0.66666667 1. 0.66666667 0.33333333 1. 1.
|
|
0.66666667 0.33333333 0.33333333 1. ]
|
|
|
|
mean value: 0.7
|
|
|
|
key: train_recall
|
|
value: [0.76923077 0.96153846 1. 0.96153846 0.96153846 1.
|
|
0.92307692 0.96153846 0.96153846 0.96296296]
|
|
|
|
mean value: 0.9462962962962964
|
|
|
|
key: test_accuracy
|
|
value: [0.375 0.375 0.375 0.25 0.375 0.75
|
|
0.5 0.5 0.25 0.42857143]
|
|
|
|
mean value: 0.41785714285714287
|
|
|
|
key: train_accuracy
|
|
value: [0.77464789 0.66197183 0.61971831 0.77464789 0.6056338 0.74647887
|
|
0.63380282 0.73239437 0.73239437 0.72222222]
|
|
|
|
mean value: 0.7003912363067293
|
|
|
|
key: test_roc_auc
|
|
value: [0.43333333 0.5 0.43333333 0.26666667 0.5 0.8
|
|
0.53333333 0.46666667 0.26666667 0.6 ]
|
|
|
|
mean value: 0.47999999999999987
|
|
|
|
key: train_roc_auc
|
|
value: [0.77350427 0.72521368 0.7 0.81410256 0.68076923 0.8
|
|
0.69487179 0.78076923 0.78076923 0.77037037]
|
|
|
|
mean value: 0.7520370370370371
|
|
|
|
key: test_jcc
|
|
value: [0.28571429 0.375 0.28571429 0.14285714 0.375 0.6
|
|
0.33333333 0.2 0.14285714 0.33333333]
|
|
|
|
mean value: 0.30738095238095237
|
|
|
|
key: train_jcc
|
|
value: [0.55555556 0.51020408 0.49056604 0.6097561 0.47169811 0.59090909
|
|
0.48 0.56818182 0.56818182 0.56521739]
|
|
|
|
mean value: 0.5410270004269655
|
|
|
|
key: TN
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: FP
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: FN
|
|
value: 37
|
|
|
|
mean value: 37.0
|
|
|
|
key: TP
|
|
value: 20
|
|
|
|
mean value: 20.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.27
|
|
|
|
Accuracy on Blind test: 0.52
|
|
|
|
Running classifier: 4
|
|
Model_name: Naive Bayes
|
|
Model func: BernoulliNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', BernoulliNB())])
|
|
|
|
key: fit_time
|
|
value: [0.0091784 0.00914526 0.00893593 0.00925088 0.00869727 0.00930667
|
|
0.00948238 0.00864649 0.00869966 0.00828242]
|
|
|
|
mean value: 0.008962535858154297
|
|
|
|
key: score_time
|
|
value: [0.00917268 0.00923753 0.00889874 0.00905013 0.00859499 0.00902128
|
|
0.00948811 0.00878263 0.00849462 0.00845385]
|
|
|
|
mean value: 0.008919453620910645
|
|
|
|
key: test_mcc
|
|
value: [-0.29277002 -0.06666667 0.48795004 0. 0.46666667 -0.06666667
|
|
-0.29277002 -0.29277002 -0.29277002 -0.25819889]
|
|
|
|
mean value: -0.060799560747780104
|
|
|
|
key: train_mcc
|
|
value: [0.40170738 0.51530373 0.43729399 0.35928426 0.40170738 0.50503962
|
|
0.48250404 0.50503962 0.39606306 0.4233902 ]
|
|
|
|
mean value: 0.44273332643919316
|
|
|
|
key: test_fscore
|
|
value: [0. 0.33333333 0.5 0. 0.66666667 0.33333333
|
|
0. 0. 0. 0. ]
|
|
|
|
mean value: 0.18333333333333332
|
|
|
|
key: train_fscore
|
|
value: [0.48648649 0.57894737 0.52631579 0.47368421 0.48648649 0.6
|
|
0.54054054 0.6 0.51282051 0.51282051]
|
|
|
|
mean value: 0.5318101907575592
|
|
|
|
key: test_precision
|
|
value: [0. 0.33333333 1. 0. 0.66666667 0.33333333
|
|
0. 0. 0. 0. ]
|
|
|
|
mean value: 0.2333333333333333
|
|
|
|
key: train_precision
|
|
value: [0.81818182 0.91666667 0.83333333 0.75 0.81818182 0.85714286
|
|
0.90909091 0.85714286 0.76923077 0.83333333]
|
|
|
|
mean value: 0.8362304362304362
|
|
|
|
key: test_recall
|
|
value: [0. 0.33333333 0.33333333 0. 0.66666667 0.33333333
|
|
0. 0. 0. 0. ]
|
|
|
|
mean value: 0.16666666666666666
|
|
|
|
key: train_recall
|
|
value: [0.34615385 0.42307692 0.38461538 0.34615385 0.34615385 0.46153846
|
|
0.38461538 0.46153846 0.38461538 0.37037037]
|
|
|
|
mean value: 0.3908831908831909
|
|
|
|
key: test_accuracy
|
|
value: [0.5 0.5 0.75 0.625 0.75 0.5
|
|
0.5 0.5 0.5 0.57142857]
|
|
|
|
mean value: 0.5696428571428571
|
|
|
|
key: train_accuracy
|
|
value: [0.73239437 0.77464789 0.74647887 0.71830986 0.73239437 0.77464789
|
|
0.76056338 0.77464789 0.73239437 0.73611111]
|
|
|
|
mean value: 0.7482589984350548
|
|
|
|
key: test_roc_auc
|
|
value: [0.4 0.46666667 0.66666667 0.5 0.73333333 0.46666667
|
|
0.4 0.4 0.4 0.4 ]
|
|
|
|
mean value: 0.4833333333333334
|
|
|
|
key: train_roc_auc
|
|
value: [0.6508547 0.70042735 0.67008547 0.63974359 0.6508547 0.70854701
|
|
0.68119658 0.70854701 0.65897436 0.66296296]
|
|
|
|
mean value: 0.6732193732193733
|
|
|
|
key: test_jcc
|
|
value: [0. 0.2 0.33333333 0. 0.5 0.2
|
|
0. 0. 0. 0. ]
|
|
|
|
mean value: 0.12333333333333334
|
|
|
|
key: train_jcc
|
|
value: [0.32142857 0.40740741 0.35714286 0.31034483 0.32142857 0.42857143
|
|
0.37037037 0.42857143 0.34482759 0.34482759]
|
|
|
|
mean value: 0.36349206349206353
|
|
|
|
key: TN
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: FP
|
|
value: 24
|
|
|
|
mean value: 24.0
|
|
|
|
key: FN
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: TP
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.16
|
|
|
|
Accuracy on Blind test: 0.55
|
|
|
|
Running classifier: 5 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
|
|
Model_name: K-Nearest Neighbors
|
|
Model func: KNeighborsClassifier()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', KNeighborsClassifier())])
|
|
|
|
key: fit_time
|
|
value: [0.00837946 0.01102352 0.00812745 0.00867653 0.00784159 0.00877905
|
|
0.00860858 0.00786567 0.0082624 0.00899577]
|
|
|
|
mean value: 0.008656001091003418
|
|
|
|
key: score_time
|
|
value: [0.04542685 0.03425503 0.00936675 0.00973725 0.00988531 0.00930572
|
|
0.01529074 0.01369047 0.01387525 0.01388526]
|
|
|
|
mean value: 0.017471861839294434
|
|
|
|
key: test_mcc
|
|
value: [ 0.48795004 -0.06666667 0.25819889 0.1490712 0.46666667 0.25819889
|
|
0.48795004 0. -0.06666667 1. ]
|
|
|
|
mean value: 0.2974702384276175
|
|
|
|
key: train_mcc
|
|
value: [0.532629 0.49965897 0.332975 0.49787306 0.46412056 0.4660252
|
|
0.46504888 0.43897987 0.53764379 0.48034053]
|
|
|
|
mean value: 0.4715294855036312
|
|
|
|
key: test_fscore
|
|
value: [0.5 0.33333333 0.57142857 0.4 0.66666667 0.57142857
|
|
0.5 0. 0.33333333 1. ]
|
|
|
|
mean value: 0.4876190476190477
|
|
|
|
key: train_fscore
|
|
value: [0.65116279 0.61904762 0.53333333 0.63636364 0.60465116 0.58536585
|
|
0.62222222 0.625 0.63414634 0.62222222]
|
|
|
|
mean value: 0.6133515181799357
|
|
|
|
key: test_precision
|
|
value: [1. 0.33333333 0.5 0.5 0.66666667 0.5
|
|
1. 0. 0.33333333 1. ]
|
|
|
|
mean value: 0.5833333333333333
|
|
|
|
key: train_precision
|
|
value: [0.82352941 0.8125 0.63157895 0.77777778 0.76470588 0.8
|
|
0.73684211 0.68181818 0.86666667 0.77777778]
|
|
|
|
mean value: 0.7673196750789629
|
|
|
|
key: test_recall
|
|
value: [0.33333333 0.33333333 0.66666667 0.33333333 0.66666667 0.66666667
|
|
0.33333333 0. 0.33333333 1. ]
|
|
|
|
mean value: 0.4666666666666666
|
|
|
|
key: train_recall
|
|
value: [0.53846154 0.5 0.46153846 0.53846154 0.5 0.46153846
|
|
0.53846154 0.57692308 0.5 0.51851852]
|
|
|
|
mean value: 0.5133903133903133
|
|
|
|
key: test_accuracy
|
|
value: [0.75 0.5 0.625 0.625 0.75 0.625 0.75 0.625 0.5 1. ]
|
|
|
|
mean value: 0.675
|
|
|
|
key: train_accuracy
|
|
value: [0.78873239 0.77464789 0.70422535 0.77464789 0.76056338 0.76056338
|
|
0.76056338 0.74647887 0.78873239 0.76388889]
|
|
|
|
mean value: 0.7623043818466353
|
|
|
|
key: test_roc_auc
|
|
value: [0.66666667 0.46666667 0.63333333 0.56666667 0.73333333 0.63333333
|
|
0.66666667 0.5 0.46666667 1. ]
|
|
|
|
mean value: 0.6333333333333333
|
|
|
|
key: train_roc_auc
|
|
value: [0.73589744 0.71666667 0.65299145 0.72478632 0.70555556 0.6974359
|
|
0.71367521 0.71068376 0.72777778 0.71481481]
|
|
|
|
mean value: 0.7100284900284901
|
|
|
|
key: test_jcc
|
|
value: [0.33333333 0.2 0.4 0.25 0.5 0.4
|
|
0.33333333 0. 0.2 1. ]
|
|
|
|
mean value: 0.3616666666666667
|
|
|
|
key: train_jcc
|
|
value: [0.48275862 0.44827586 0.36363636 0.46666667 0.43333333 0.4137931
|
|
0.4516129 0.45454545 0.46428571 0.4516129 ]
|
|
|
|
mean value: 0.4430520925126042
|
|
|
|
key: TN
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: FP
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: FN
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: TP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.16
|
|
|
|
Accuracy on Blind test: 0.55
|
|
|
|
Running classifier: 6
|
|
Model_name: SVC
|
|
Model func: SVC(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SVC(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00934434 0.00849128 0.00862622 0.00854397 0.00879931 0.00910807
|
|
0.00843096 0.00899863 0.00908327 0.00870323]
|
|
|
|
mean value: 0.008812928199768066
|
|
|
|
key: score_time
|
|
value: [0.00875854 0.00884771 0.00850749 0.00841236 0.00861263 0.00835824
|
|
0.0086937 0.00896144 0.00847411 0.00863171]
|
|
|
|
mean value: 0.00862579345703125
|
|
|
|
key: test_mcc
|
|
value: [ 0. -0.4472136 0. 0. 0. 0.48795004
|
|
0. 0. 0. 0. ]
|
|
|
|
mean value: 0.004073644097430868
|
|
|
|
key: train_mcc
|
|
value: [0.46880723 0.53266562 0.46880723 0.53266562 0.43508951 0.50123916
|
|
0.50123916 0.56330071 0.46880723 0.51847585]
|
|
|
|
mean value: 0.49910973205557047
|
|
|
|
key: test_fscore
|
|
value: [0. 0. 0. 0. 0. 0.5 0. 0. 0. 0. ]
|
|
|
|
mean value: 0.05
|
|
|
|
key: train_fscore
|
|
value: [0.47058824 0.55555556 0.47058824 0.55555556 0.42424242 0.51428571
|
|
0.51428571 0.59459459 0.47058824 0.54054054]
|
|
|
|
mean value: 0.5110824804942451
|
|
|
|
key: test_precision
|
|
value: [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
|
|
|
|
mean value: 0.1
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0. 0. 0. 0. 0. 0.33333333
|
|
0. 0. 0. 0. ]
|
|
|
|
mean value: 0.03333333333333333
|
|
|
|
key: train_recall
|
|
value: [0.30769231 0.38461538 0.30769231 0.38461538 0.26923077 0.34615385
|
|
0.34615385 0.42307692 0.30769231 0.37037037]
|
|
|
|
mean value: 0.3447293447293447
|
|
|
|
key: test_accuracy
|
|
value: [0.625 0.375 0.625 0.625 0.625 0.75
|
|
0.625 0.625 0.625 0.71428571]
|
|
|
|
mean value: 0.6214285714285714
|
|
|
|
key: train_accuracy
|
|
value: [0.74647887 0.77464789 0.74647887 0.77464789 0.73239437 0.76056338
|
|
0.76056338 0.78873239 0.74647887 0.76388889]
|
|
|
|
mean value: 0.7594874804381847
|
|
|
|
key: test_roc_auc
|
|
value: [0.5 0.3 0.5 0.5 0.5 0.66666667
|
|
0.5 0.5 0.5 0.5 ]
|
|
|
|
mean value: 0.4966666666666667
|
|
|
|
key: train_roc_auc
|
|
value: [0.65384615 0.69230769 0.65384615 0.69230769 0.63461538 0.67307692
|
|
0.67307692 0.71153846 0.65384615 0.68518519]
|
|
|
|
mean value: 0.6723646723646725
|
|
|
|
key: test_jcc
|
|
value: [0. 0. 0. 0. 0. 0.33333333
|
|
0. 0. 0. 0. ]
|
|
|
|
mean value: 0.03333333333333333
|
|
|
|
key: train_jcc
|
|
value: [0.30769231 0.38461538 0.30769231 0.38461538 0.26923077 0.34615385
|
|
0.34615385 0.42307692 0.30769231 0.37037037]
|
|
|
|
mean value: 0.3447293447293447
|
|
|
|
key: TN
|
|
value: 48
|
|
|
|
mean value: 48.0
|
|
|
|
key: FP
|
|
value: 28
|
|
|
|
mean value: 28.0
|
|
|
|
key: FN
|
|
value: 2
|
|
|
|
mean value: 2.0
|
|
|
|
key: TP
|
|
value: 1
|
|
|
|
mean value: 1.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.22
|
|
|
|
Accuracy on Blind test: 0.68
|
|
|
|
Running classifier: 7
|
|
Model_name: MLP
|
|
Model func: MLPClassifier(max_iter=500, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MLPClassifier(max_iter=500, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.4739027 0.3656857 0.50512409 0.53201818 0.40765285 0.37933254
|
|
0.71146369 1.25982904 0.67421913 0.39366579]
|
|
|
|
mean value: 0.5702893733978271
|
|
|
|
key: score_time
|
|
value: [0.01205778 0.01223612 0.01199913 0.01212883 0.01202846 0.01200676
|
|
0.012532 0.01227117 0.01231146 0.01202655]
|
|
|
|
mean value: 0.01215982437133789
|
|
|
|
key: test_mcc
|
|
value: [ 0.74535599 0.46666667 0.74535599 0.06666667 0.46666667 0.6
|
|
0.1490712 -0.29277002 -0.06666667 0.3 ]
|
|
|
|
mean value: 0.3180346494948619
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.66666667 0.8 0.5 0.66666667 0.75
|
|
0.4 0. 0.33333333 0.5 ]
|
|
|
|
mean value: 0.5416666666666666
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.66666667 1. 0.4 0.66666667 0.6
|
|
0.5 0. 0.33333333 0.5 ]
|
|
|
|
mean value: 0.5666666666666667
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.66666667 0.66666667 0.66666667 0.66666667 1.
|
|
0.33333333 0. 0.33333333 0.5 ]
|
|
|
|
mean value: 0.5499999999999999
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.875 0.75 0.875 0.5 0.75 0.75
|
|
0.625 0.5 0.5 0.71428571]
|
|
|
|
mean value: 0.6839285714285714
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 0.73333333 0.83333333 0.53333333 0.73333333 0.8
|
|
0.56666667 0.4 0.46666667 0.65 ]
|
|
|
|
mean value: 0.655
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.5 0.66666667 0.33333333 0.5 0.6
|
|
0.25 0. 0.2 0.33333333]
|
|
|
|
mean value: 0.40499999999999997
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 38
|
|
|
|
mean value: 38.0
|
|
|
|
key: FP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: FN
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: TP
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.16
|
|
|
|
Accuracy on Blind test: 0.62
|
|
|
|
Running classifier: 8
|
|
Model_name: Decision Tree
|
|
Model func: DecisionTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', DecisionTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01303053 0.01276302 0.00972843 0.00934529 0.00922871 0.00918818
|
|
0.00910521 0.0092113 0.00886583 0.00934553]
|
|
|
|
mean value: 0.009981203079223632
|
|
|
|
key: score_time
|
|
value: [0.01174879 0.01063251 0.00858688 0.00828457 0.00867963 0.00826287
|
|
0.00824618 0.00817752 0.00813031 0.00817704]
|
|
|
|
mean value: 0.008892631530761719
|
|
|
|
key: test_mcc
|
|
value: [0.74535599 0.74535599 0.77459667 0.46666667 1. 0.6
|
|
0.46666667 1. 0.74535599 0.54772256]
|
|
|
|
mean value: 0.7091720537579772
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.8 0.85714286 0.66666667 1. 0.75
|
|
0.66666667 1. 0.8 0.66666667]
|
|
|
|
mean value: 0.8007142857142856
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.75 0.66666667 1. 0.6
|
|
0.66666667 1. 1. 0.5 ]
|
|
|
|
mean value: 0.8183333333333334
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.66666667 1. 0.66666667 1. 1.
|
|
0.66666667 1. 0.66666667 1. ]
|
|
|
|
mean value: 0.8333333333333333
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.875 0.875 0.875 0.75 1. 0.75
|
|
0.75 1. 0.875 0.71428571]
|
|
|
|
mean value: 0.8464285714285713
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 0.83333333 0.9 0.73333333 1. 0.8
|
|
0.73333333 1. 0.83333333 0.8 ]
|
|
|
|
mean value: 0.8466666666666667
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.66666667 0.75 0.5 1. 0.6
|
|
0.5 1. 0.66666667 0.5 ]
|
|
|
|
mean value: 0.685
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: FP
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 24
|
|
|
|
mean value: 24.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.89
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 9
|
|
Model_name: Extra Trees
|
|
Model func: ExtraTreesClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreesClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.0797112 0.08011031 0.0798316 0.07908225 0.07965994 0.07991409
|
|
0.08132887 0.07901454 0.07934308 0.07978773]
|
|
|
|
mean value: 0.07977836132049561
|
|
|
|
key: score_time
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
[0.01661205 0.01663494 0.01658773 0.01665282 0.01672363 0.01666379
|
|
0.01657057 0.01656389 0.01663399 0.0166142 ]
|
|
|
|
mean value: 0.01662576198577881
|
|
|
|
key: test_mcc
|
|
value: [ 0.48795004 -0.06666667 0.74535599 -0.06666667 0.1490712 0.25819889
|
|
0.1490712 -0.29277002 0.1490712 0.73029674]
|
|
|
|
mean value: 0.22429119023436436
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.5 0.33333333 0.8 0.33333333 0.4 0.57142857
|
|
0.4 0. 0.4 0.8 ]
|
|
|
|
mean value: 0.4538095238095238
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.33333333 1. 0.33333333 0.5 0.5
|
|
0.5 0. 0.5 0.66666667]
|
|
|
|
mean value: 0.5333333333333333
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.33333333 0.33333333 0.66666667 0.33333333 0.33333333 0.66666667
|
|
0.33333333 0. 0.33333333 1. ]
|
|
|
|
mean value: 0.4333333333333334
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.75 0.5 0.875 0.5 0.625 0.625
|
|
0.625 0.5 0.625 0.85714286]
|
|
|
|
mean value: 0.6482142857142856
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.66666667 0.46666667 0.83333333 0.46666667 0.56666667 0.63333333
|
|
0.56666667 0.4 0.56666667 0.9 ]
|
|
|
|
mean value: 0.6066666666666667
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.33333333 0.2 0.66666667 0.2 0.25 0.4
|
|
0.25 0. 0.25 0.66666667]
|
|
|
|
mean value: 0.32166666666666666
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 39
|
|
|
|
mean value: 39.0
|
|
|
|
key: FP
|
|
value: 17
|
|
|
|
mean value: 17.0
|
|
|
|
key: FN
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: TP
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.28
|
|
|
|
Accuracy on Blind test: 0.7
|
|
|
|
Running classifier: 10
|
|
Model_name: Extra Tree
|
|
Model func: ExtraTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00881672 0.00906944 0.00841808 0.00802994 0.00884914 0.00797772
|
|
0.0080471 0.00805855 0.00790024 0.00817227]
|
|
|
|
mean value: 0.008333921432495117
|
|
|
|
key: score_time
|
|
value: [0.00838375 0.0091083 0.0088253 0.00814772 0.00830793 0.008255
|
|
0.00831819 0.00809455 0.00816226 0.00925803]
|
|
|
|
mean value: 0.008486104011535645
|
|
|
|
key: test_mcc
|
|
value: [ 0. -0.06666667 0.74535599 -0.6 0.48795004 0.4472136
|
|
0.1490712 -0.06666667 0.25819889 -0.09128709]
|
|
|
|
mean value: 0.12631692864704402
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0. 0.33333333 0.8 0. 0.5 0.66666667
|
|
0.4 0.33333333 0.57142857 0.33333333]
|
|
|
|
mean value: 0.39380952380952383
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0. 0.33333333 1. 0. 1. 0.5
|
|
0.5 0.33333333 0.5 0.25 ]
|
|
|
|
mean value: 0.4416666666666666
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0. 0.33333333 0.66666667 0. 0.33333333 1.
|
|
0.33333333 0.33333333 0.66666667 0.5 ]
|
|
|
|
mean value: 0.41666666666666663
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.625 0.5 0.875 0.25 0.75 0.625
|
|
0.625 0.5 0.625 0.42857143]
|
|
|
|
mean value: 0.5803571428571429
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.5 0.46666667 0.83333333 0.2 0.66666667 0.7
|
|
0.56666667 0.46666667 0.63333333 0.45 ]
|
|
|
|
mean value: 0.5483333333333333
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0. 0.2 0.66666667 0. 0.33333333 0.5
|
|
0.25 0.2 0.4 0.2 ]
|
|
|
|
mean value: 0.275
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 34
|
|
|
|
mean value: 34.0
|
|
|
|
key: FP
|
|
value: 17
|
|
|
|
mean value: 17.0
|
|
|
|
key: FN
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: TP
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.08
|
|
|
|
Accuracy on Blind test: 0.57
|
|
|
|
Running classifier: 11
|
|
Model_name: Random Forest
|
|
Model func: RandomForestClassifier(n_estimators=1000, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(n_estimators=1000, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.98950601 0.99312663 0.9990921 1.0047307 0.99263144 0.98464394
|
|
0.99300337 0.98910856 0.98675776 0.99708605]
|
|
|
|
mean value: 0.9929686546325683
|
|
|
|
key: score_time
|
|
value: [0.08685112 0.08616352 0.0863297 0.09210467 0.08822775 0.08625507
|
|
0.08599234 0.08730912 0.0862937 0.08622384]
|
|
|
|
mean value: 0.08717508316040039
|
|
|
|
key: test_mcc
|
|
value: [0.74535599 0.46666667 0.74535599 0.25819889 0.48795004 0.74535599
|
|
0.74535599 0. 0.1490712 0.73029674]
|
|
|
|
mean value: 0.5073607504728022
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.66666667 0.8 0.57142857 0.5 0.8
|
|
0.8 0. 0.4 0.8 ]
|
|
|
|
mean value: 0.6138095238095238
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.66666667 1. 0.5 1. 1.
|
|
1. 0. 0.5 0.66666667]
|
|
|
|
mean value: 0.7333333333333333
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.66666667 0.66666667 0.66666667 0.33333333 0.66666667
|
|
0.66666667 0. 0.33333333 1. ]
|
|
|
|
mean value: 0.5666666666666667
|
|
|
|
key: train_recall
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.875 0.75 0.875 0.625 0.75 0.875
|
|
0.875 0.625 0.625 0.85714286]
|
|
|
|
mean value: 0.7732142857142856
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 0.73333333 0.83333333 0.63333333 0.66666667 0.83333333
|
|
0.83333333 0.5 0.56666667 0.9 ]
|
|
|
|
mean value: 0.7333333333333333
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.5 0.66666667 0.4 0.33333333 0.66666667
|
|
0.66666667 0. 0.25 0.66666667]
|
|
|
|
mean value: 0.48166666666666674
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 45
|
|
|
|
mean value: 45.0
|
|
|
|
key: FP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: FN
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: TP
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.57
|
|
|
|
Accuracy on Blind test: 0.8
|
|
|
|
Running classifier: 12
|
|
Model_name: Random Forest2
|
|
Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...age_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10,
|
|
oob_score=True, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.82440186 0.80704331 0.8464756 0.91650128 0.80716181 0.7972815
|
|
0.81476235 0.90086365 0.79608941 0.80462122]
|
|
|
|
mean value: 0.8315201997756958
|
|
|
|
key: score_time
|
|
value: [0.19199228 0.18694234 0.19604993 0.18045735 0.1642139 0.16165352
|
|
0.17684007 0.18403292 0.14751863 0.19382191]
|
|
|
|
mean value: 0.1783522844314575
|
|
|
|
key: test_mcc
|
|
value: [0.74535599 0.48795004 0.46666667 0.48795004 0.48795004 0.74535599
|
|
0.74535599 0. 0. 0.73029674]
|
|
|
|
mean value: 0.48968814969294777
|
|
|
|
key: train_mcc
|
|
value: [0.88152145 0.91067388 0.94010481 0.94010481 0.94010481 0.96986363
|
|
0.91067388 0.90865445 0.94010481 0.94155447]
|
|
|
|
mean value: 0.9283361007134785
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.5 0.66666667 0.5 0.5 0.8
|
|
0.8 0. 0. 0.8 ]
|
|
|
|
mean value: 0.5366666666666666
|
|
|
|
key: train_fscore
|
|
value: [0.91666667 0.93877551 0.96 0.96 0.96 0.98039216
|
|
0.93877551 0.94117647 0.96 0.96153846]
|
|
|
|
mean value: 0.9517324776064273
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.66666667 1. 1. 1.
|
|
1. 0. 0. 0.66666667]
|
|
|
|
mean value: 0.7333333333333333
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 0.96 1. 1. ]
|
|
|
|
mean value: 0.9960000000000001
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.33333333 0.66666667 0.33333333 0.33333333 0.66666667
|
|
0.66666667 0. 0. 1. ]
|
|
|
|
mean value: 0.4666666666666666
|
|
|
|
key: train_recall
|
|
value: [0.84615385 0.88461538 0.92307692 0.92307692 0.92307692 0.96153846
|
|
0.88461538 0.92307692 0.92307692 0.92592593]
|
|
|
|
mean value: 0.9118233618233619
|
|
|
|
key: test_accuracy
|
|
value: [0.875 0.75 0.75 0.75 0.75 0.875
|
|
0.875 0.625 0.625 0.85714286]
|
|
|
|
mean value: 0.7732142857142856
|
|
|
|
key: train_accuracy
|
|
value: [0.94366197 0.95774648 0.97183099 0.97183099 0.97183099 0.98591549
|
|
0.95774648 0.95774648 0.97183099 0.97222222]
|
|
|
|
mean value: 0.9662363067292643
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 0.66666667 0.73333333 0.66666667 0.66666667 0.83333333
|
|
0.83333333 0.5 0.5 0.9 ]
|
|
|
|
mean value: 0.7133333333333333
|
|
|
|
key: train_roc_auc
|
|
value: [0.92307692 0.94230769 0.96153846 0.96153846 0.96153846 0.98076923
|
|
0.94230769 0.95042735 0.96153846 0.96296296]
|
|
|
|
mean value: 0.9548005698005699
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.33333333 0.5 0.33333333 0.33333333 0.66666667
|
|
0.66666667 0. 0. 0.66666667]
|
|
|
|
mean value: 0.4166666666666667
|
|
|
|
key: train_jcc
|
|
value: [0.84615385 0.88461538 0.92307692 0.92307692 0.92307692 0.96153846
|
|
0.88461538 0.88888889 0.92307692 0.92592593]
|
|
|
|
mean value: 0.9084045584045584
|
|
|
|
key: TN
|
|
value: 48
|
|
|
|
mean value: 48.0
|
|
|
|
key: FP
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: FN
|
|
value: 2
|
|
|
|
mean value: 2.0
|
|
|
|
key: TP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.63
|
|
|
|
Accuracy on Blind test: 0.82
|
|
|
|
Running classifier: 13
|
|
Model_name: XGBoost
|
|
Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...
|
|
interaction_constraints=None, learning_rate=None,
|
|
max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan,
|
|
monotone_constraints=None, n_estimators=100,
|
|
n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None,
|
|
reg_lambda=None, scale_pos_weight=None,
|
|
subsample=None, tree_method=None,
|
|
use_label_encoder=False,
|
|
validate_parameters=None, verbosity=0))])
|
|
|
|
key: fit_time
|
|
value: [0.25267649 0.03277755 0.03268933 0.0326879 0.03371954 0.0320909
|
|
0.03316617 0.03263474 0.03360939 0.03158092]
|
|
|
|
mean value: 0.054763293266296385
|
|
|
|
key: score_time
|
|
value: [0.01154733 0.01026154 0.01063204 0.01008058 0.010144 0.00997472
|
|
0.01005268 0.01010799 0.01003933 0.01033354]
|
|
|
|
mean value: 0.010317373275756835
|
|
|
|
key: test_mcc
|
|
value: [1. 0.74535599 0.46666667 1. 1. 0.6
|
|
1. 1. 0.74535599 0.73029674]
|
|
|
|
mean value: 0.8287675395006747
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.8 0.66666667 1. 1. 0.75
|
|
1. 1. 0.8 0.8 ]
|
|
|
|
mean value: 0.8816666666666668
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.66666667 1. 1. 0.6
|
|
1. 1. 1. 0.66666667]
|
|
|
|
mean value: 0.8933333333333332
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.66666667 0.66666667 1. 1. 1.
|
|
1. 1. 0.66666667 1. ]
|
|
|
|
mean value: 0.9
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.875 0.75 1. 1. 0.75
|
|
1. 1. 0.875 0.85714286]
|
|
|
|
mean value: 0.9107142857142858
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.83333333 0.73333333 1. 1. 0.8
|
|
1. 1. 0.83333333 0.9 ]
|
|
|
|
mean value: 0.9099999999999999
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.66666667 0.5 1. 1. 0.6
|
|
1. 1. 0.66666667 0.66666667]
|
|
|
|
mean value: 0.8099999999999999
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 46
|
|
|
|
mean value: 46.0
|
|
|
|
key: FP
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: FN
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: TP
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.95
|
|
|
|
Accuracy on Blind test: 0.98
|
|
|
|
Running classifier: 14
|
|
Model_name: LDA
|
|
Model func: LinearDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LinearDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.02152681 0.03794527 0.03800631 0.03840089 0.03812551 0.03813028
|
|
0.038131 0.03790116 0.04037714 0.04405189]
|
|
|
|
mean value: 0.03725962638854981
|
|
|
|
key: score_time
|
|
value: [0.02359128 0.01147771 0.02012229 0.02056122 0.01671767 0.02125883
|
|
0.02173471 0.02235389 0.02048898 0.01903772]
|
|
|
|
mean value: 0.01973443031311035
|
|
|
|
key: test_mcc
|
|
value: [ 0.46666667 0.46666667 1. -0.46666667 0.46666667 0.46666667
|
|
0.74535599 0.25819889 0.06666667 -0.09128709]
|
|
|
|
mean value: 0.33789344559962303
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.66666667 0.66666667 1. 0.25 0.66666667 0.66666667
|
|
0.8 0.57142857 0.5 0.33333333]
|
|
|
|
mean value: 0.6121428571428571
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.66666667 0.66666667 1. 0.2 0.66666667 0.66666667
|
|
1. 0.5 0.4 0.25 ]
|
|
|
|
mean value: 0.6016666666666667
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.66666667 1. 0.33333333 0.66666667 0.66666667
|
|
0.66666667 0.66666667 0.66666667 0.5 ]
|
|
|
|
mean value: 0.65
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.75 0.75 1. 0.25 0.75 0.75
|
|
0.875 0.625 0.5 0.42857143]
|
|
|
|
mean value: 0.6678571428571429
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.73333333 0.73333333 1. 0.26666667 0.73333333 0.73333333
|
|
0.83333333 0.63333333 0.53333333 0.45 ]
|
|
|
|
mean value: 0.6649999999999999
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.5 0.5 1. 0.14285714 0.5 0.5
|
|
0.66666667 0.4 0.33333333 0.2 ]
|
|
|
|
mean value: 0.4742857142857142
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 34
|
|
|
|
mean value: 34.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: TP
|
|
value: 19
|
|
|
|
mean value: 19.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.04
|
|
|
|
Accuracy on Blind test: 0.52
|
|
|
|
Running classifier: 15
|
|
Model_name: Multinomial
|
|
Model func: MultinomialNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MultinomialNB())])
|
|
|
|
key: fit_time
|
|
value: [0.01962996 0.00853753 0.00832033 0.00821114 0.00803685 0.00808215
|
|
0.00803947 0.00806713 0.00811481 0.00799322]
|
|
|
|
mean value: 0.00930325984954834
|
|
|
|
key: score_time
|
|
value: [0.00881076 0.00864339 0.00856471 0.00838065 0.00816441 0.00830078
|
|
0.00829577 0.00821209 0.00824976 0.00826263]
|
|
|
|
mean value: 0.008388495445251465
|
|
|
|
key: test_mcc
|
|
value: [-0.06666667 -0.06666667 0.1490712 -0.06666667 0.48795004 0.48795004
|
|
0. -0.29277002 0.1490712 -0.4 ]
|
|
|
|
mean value: 0.038127244806394525
|
|
|
|
key: train_mcc
|
|
value: [0.35808137 0.39561212 0.39440661 0.39440661 0.43729399 0.36890287
|
|
0.39561212 0.39901194 0.39901194 0.41403934]
|
|
|
|
mean value: 0.3956378889330924
|
|
|
|
key: test_fscore
|
|
value: [0.33333333 0.33333333 0.4 0.33333333 0.5 0.5
|
|
0. 0. 0.4 0. ]
|
|
|
|
mean value: 0.27999999999999997
|
|
|
|
key: train_fscore
|
|
value: [0.5 0.55813953 0.53658537 0.53658537 0.52631579 0.56521739
|
|
0.55813953 0.57777778 0.57777778 0.55813953]
|
|
|
|
mean value: 0.5494678072692067
|
|
|
|
key: test_precision
|
|
value: [0.33333333 0.33333333 0.5 0.33333333 1. 1.
|
|
0. 0. 0.5 0. ]
|
|
|
|
mean value: 0.4
|
|
|
|
key: train_precision
|
|
value: [0.71428571 0.70588235 0.73333333 0.73333333 0.83333333 0.65
|
|
0.70588235 0.68421053 0.68421053 0.75 ]
|
|
|
|
mean value: 0.7194471472799646
|
|
|
|
key: test_recall
|
|
value: [0.33333333 0.33333333 0.33333333 0.33333333 0.33333333 0.33333333
|
|
0. 0. 0.33333333 0. ]
|
|
|
|
mean value: 0.23333333333333334
|
|
|
|
key: train_recall
|
|
value: [0.38461538 0.46153846 0.42307692 0.42307692 0.38461538 0.5
|
|
0.46153846 0.5 0.5 0.44444444]
|
|
|
|
mean value: 0.4482905982905983
|
|
|
|
key: test_accuracy
|
|
value: [0.5 0.5 0.625 0.5 0.75 0.75
|
|
0.625 0.5 0.625 0.42857143]
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
|
|
mean value: 0.5803571428571429
|
|
|
|
key: train_accuracy
|
|
value: [0.71830986 0.73239437 0.73239437 0.73239437 0.74647887 0.71830986
|
|
0.73239437 0.73239437 0.73239437 0.73611111]
|
|
|
|
mean value: 0.7313575899843506
|
|
|
|
key: test_roc_auc
|
|
value: [0.46666667 0.46666667 0.56666667 0.46666667 0.66666667 0.66666667
|
|
0.5 0.4 0.56666667 0.3 ]
|
|
|
|
mean value: 0.5066666666666666
|
|
|
|
key: train_roc_auc
|
|
value: [0.64786325 0.67521368 0.66709402 0.66709402 0.67008547 0.67222222
|
|
0.67521368 0.68333333 0.68333333 0.67777778]
|
|
|
|
mean value: 0.6719230769230771
|
|
|
|
key: test_jcc
|
|
value: [0.2 0.2 0.25 0.2 0.33333333 0.33333333
|
|
0. 0. 0.25 0. ]
|
|
|
|
mean value: 0.17666666666666667
|
|
|
|
key: train_jcc
|
|
value: [0.33333333 0.38709677 0.36666667 0.36666667 0.35714286 0.39393939
|
|
0.38709677 0.40625 0.40625 0.38709677]
|
|
|
|
mean value: 0.3791539240329563
|
|
|
|
key: TN
|
|
value: 39
|
|
|
|
mean value: 39.0
|
|
|
|
key: FP
|
|
value: 22
|
|
|
|
mean value: 22.0
|
|
|
|
key: FN
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: TP
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.06
|
|
|
|
Accuracy on Blind test: 0.57
|
|
|
|
Running classifier: 16
|
|
Model_name: Passive Aggresive
|
|
Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
PassiveAggressiveClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01078463 0.01264167 0.01373768 0.01230025 0.01327467 0.01310778
|
|
0.01337218 0.01272774 0.01252699 0.01253462]
|
|
|
|
mean value: 0.012700819969177246
|
|
|
|
key: score_time
|
|
value: [0.00824046 0.01123905 0.01123929 0.01127291 0.01127601 0.0113132
|
|
0.01129079 0.01124072 0.01128697 0.01134467]
|
|
|
|
mean value: 0.010974407196044922
|
|
|
|
key: test_mcc
|
|
value: [ 0.74535599 0.74535599 0.48795004 -0.06666667 0.46666667 0.6
|
|
1. 0.1490712 -0.29277002 0.73029674]
|
|
|
|
mean value: 0.45652599414297745
|
|
|
|
key: train_mcc
|
|
value: [0.88152145 0.88152145 0.91067388 0.79523358 1. 0.9703421
|
|
1. 0.94196687 0.88152145 0.89081333]
|
|
|
|
mean value: 0.9153594107114607
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.8 0.5 0.33333333 0.66666667 0.75
|
|
1. 0.4 0. 0.8 ]
|
|
|
|
mean value: 0.605
|
|
|
|
key: train_fscore
|
|
value: [0.91666667 0.91666667 0.93877551 0.84444444 1. 0.98113208
|
|
1. 0.96296296 0.91666667 0.93103448]
|
|
|
|
mean value: 0.9408349475841808
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 0.33333333 0.66666667 0.6
|
|
1. 0.5 0. 0.66666667]
|
|
|
|
mean value: 0.6766666666666666
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 0.96296296
|
|
1. 0.92857143 1. 0.87096774]
|
|
|
|
mean value: 0.9762502133469877
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.66666667 0.33333333 0.33333333 0.66666667 1.
|
|
1. 0.33333333 0. 1. ]
|
|
|
|
mean value: 0.6
|
|
|
|
key: train_recall
|
|
value: [0.84615385 0.84615385 0.88461538 0.73076923 1. 1.
|
|
1. 1. 0.84615385 1. ]
|
|
|
|
mean value: 0.9153846153846154
|
|
|
|
key: test_accuracy
|
|
value: [0.875 0.875 0.75 0.5 0.75 0.75
|
|
1. 0.625 0.5 0.85714286]
|
|
|
|
mean value: 0.7482142857142857
|
|
|
|
key: train_accuracy
|
|
value: [0.94366197 0.94366197 0.95774648 0.90140845 1. 0.98591549
|
|
1. 0.97183099 0.94366197 0.94444444]
|
|
|
|
mean value: 0.9592331768388107
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 0.83333333 0.66666667 0.46666667 0.73333333 0.8
|
|
1. 0.56666667 0.4 0.9 ]
|
|
|
|
mean value: 0.72
|
|
|
|
key: train_roc_auc
|
|
value: [0.92307692 0.92307692 0.94230769 0.86538462 1. 0.98888889
|
|
1. 0.97777778 0.92307692 0.95555556]
|
|
|
|
mean value: 0.94991452991453
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.66666667 0.33333333 0.2 0.5 0.6
|
|
1. 0.25 0. 0.66666667]
|
|
|
|
mean value: 0.4883333333333334
|
|
|
|
key: train_jcc
|
|
value: [0.84615385 0.84615385 0.88461538 0.73076923 1. 0.96296296
|
|
1. 0.92857143 0.84615385 0.87096774]
|
|
|
|
mean value: 0.8916348287316029
|
|
|
|
key: TN
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: FP
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 17
|
|
|
|
mean value: 17.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.36
|
|
|
|
Accuracy on Blind test: 0.72
|
|
|
|
Running classifier: 17
|
|
Model_name: Stochastic GDescent
|
|
Model func: SGDClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SGDClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00896978 0.01184177 0.0120883 0.01203012 0.01236367 0.01237869
|
|
0.01220155 0.01421261 0.01220298 0.0120194 ]
|
|
|
|
mean value: 0.012030887603759765
|
|
|
|
key: score_time
|
|
value: [0.00857139 0.01130891 0.01136184 0.01203728 0.011446 0.01147342
|
|
0.01199031 0.01191354 0.01148462 0.01145744]
|
|
|
|
mean value: 0.011304473876953125
|
|
|
|
key: test_mcc
|
|
value: [0.46666667 0.48795004 0.48795004 0.4472136 0.46666667 0.25819889
|
|
0. 0. 0.1490712 0.73029674]
|
|
|
|
mean value: 0.3494013833369193
|
|
|
|
key: train_mcc
|
|
value: [0.81567142 0.36210341 0.93931624 0.9703421 1. 1.
|
|
0.68088097 0.65199786 0.88152145 0.97058178]
|
|
|
|
mean value: 0.8272415231599487
|
|
|
|
key: test_fscore
|
|
value: [0.66666667 0.5 0.5 0.66666667 0.66666667 0.57142857
|
|
0. 0. 0.4 0.8 ]
|
|
|
|
mean value: 0.47714285714285715
|
|
|
|
key: train_fscore
|
|
value: [0.88135593 0.32258065 0.96153846 0.98113208 1. 1.
|
|
0.73170732 0.7 0.91666667 0.98113208]
|
|
|
|
mean value: 0.8476113173586375
|
|
|
|
key: test_precision
|
|
value: [0.66666667 1. 1. 0.5 0.66666667 0.5
|
|
0. 0. 0.5 0.66666667]
|
|
|
|
mean value: 0.55
|
|
|
|
key: train_precision
|
|
value: [0.78787879 1. 0.96153846 0.96296296 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9712380212380213
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.33333333 0.33333333 1. 0.66666667 0.66666667
|
|
0. 0. 0.33333333 1. ]
|
|
|
|
mean value: 0.5
|
|
|
|
key: train_recall
|
|
value: [1. 0.19230769 0.96153846 1. 1. 1.
|
|
0.57692308 0.53846154 0.84615385 0.96296296]
|
|
|
|
mean value: 0.8078347578347579
|
|
|
|
key: test_accuracy
|
|
value: [0.75 0.75 0.75 0.625 0.75 0.625
|
|
0.625 0.625 0.625 0.85714286]
|
|
|
|
mean value: 0.6982142857142857
|
|
|
|
key: train_accuracy
|
|
value: [0.90140845 0.70422535 0.97183099 0.98591549 1. 1.
|
|
0.84507042 0.83098592 0.94366197 0.98611111]
|
|
|
|
mean value: 0.9169209702660407
|
|
|
|
key: test_roc_auc
|
|
value: [0.73333333 0.66666667 0.66666667 0.7 0.73333333 0.63333333
|
|
0.5 0.5 0.56666667 0.9 ]
|
|
|
|
mean value: 0.6599999999999999
|
|
|
|
key: train_roc_auc
|
|
value: [0.92222222 0.59615385 0.96965812 0.98888889 1. 1.
|
|
0.78846154 0.76923077 0.92307692 0.98148148]
|
|
|
|
mean value: 0.893917378917379
|
|
|
|
key: test_jcc
|
|
value: [0.5 0.33333333 0.33333333 0.5 0.5 0.4
|
|
0. 0. 0.25 0.66666667]
|
|
|
|
mean value: 0.3483333333333333
|
|
|
|
key: train_jcc
|
|
value: [0.78787879 0.19230769 0.92592593 0.96296296 1. 1.
|
|
0.57692308 0.53846154 0.84615385 0.96296296]
|
|
|
|
mean value: 0.7793576793576793
|
|
|
|
key: TN
|
|
value: 41
|
|
|
|
mean value: 41.0
|
|
|
|
key: FP
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: FN
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: TP
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.06
|
|
|
|
Accuracy on Blind test: 0.5
|
|
|
|
Running classifier: 18
|
|
Model_name: AdaBoost Classifier
|
|
Model func: AdaBoostClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', AdaBoostClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.07852364 0.07593727 0.07648396 0.07617307 0.07601118 0.07563257
|
|
0.07694602 0.07653785 0.07624555 0.07758784]
|
|
|
|
mean value: 0.07660789489746093
|
|
|
|
key: score_time
|
|
value: [0.01418972 0.0142076 0.014153 0.01459336 0.01418948 0.01423931
|
|
0.01444244 0.01423192 0.01443076 0.0149498 ]
|
|
|
|
mean value: 0.014362740516662597
|
|
|
|
key: test_mcc
|
|
value: [0.74535599 0.74535599 0.25819889 1. 1. 0.6
|
|
1. 1. 0.74535599 0.54772256]
|
|
|
|
mean value: 0.7641989424752118
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.8 0.57142857 1. 1. 0.75
|
|
1. 1. 0.8 0.66666667]
|
|
|
|
mean value: 0.8388095238095238
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.5 1. 1. 0.6 1. 1. 1. 0.5]
|
|
|
|
mean value: 0.86
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.66666667 0.66666667 1. 1. 1.
|
|
1. 1. 0.66666667 1. ]
|
|
|
|
mean value: 0.8666666666666668
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.875 0.875 0.625 1. 1. 0.75
|
|
1. 1. 0.875 0.71428571]
|
|
|
|
mean value: 0.8714285714285713
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 0.83333333 0.63333333 1. 1. 0.8
|
|
1. 1. 0.83333333 0.8 ]
|
|
|
|
mean value: 0.8733333333333333
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.66666667 0.4 1. 1. 0.6
|
|
1. 1. 0.66666667 0.5 ]
|
|
|
|
mean value: 0.7500000000000001
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 44
|
|
|
|
mean value: 44.0
|
|
|
|
key: FP
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: FN
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: TP
|
|
value: 25
|
|
|
|
mean value: 25.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.72
|
|
|
|
Accuracy on Blind test: 0.88
|
|
|
|
Running classifier: 19
|
|
Model_name: Bagging Classifier
|
|
Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
BaggingClassifier(n_jobs=10, oob_score=True,
|
|
random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02951646 0.03389382 0.02654004 0.02991986 0.03635168 0.03002954
|
|
0.03156233 0.04396296 0.03435445 0.02904296]
|
|
|
|
mean value: 0.032517409324645995
|
|
|
|
key: score_time
|
|
value: [0.02055669 0.02374935 0.01841545 0.01818538 0.01966786 0.01749253
|
|
0.02351284 0.02446222 0.01901722 0.01872277]
|
|
|
|
mean value: 0.0203782320022583
|
|
|
|
key: test_mcc
|
|
value: [0.74535599 0.74535599 0.74535599 1. 1. 0.6
|
|
0.74535599 0.74535599 0.74535599 0.73029674]
|
|
|
|
mean value: 0.7802432698339802
|
|
|
|
key: train_mcc
|
|
value: [0.96986363 0.96986363 1. 1. 0.94010481 1.
|
|
1. 1. 0.96986363 1. ]
|
|
|
|
mean value: 0.9849695685748777
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.8 0.8 1. 1. 0.75 0.8 0.8 0.8 0.8 ]
|
|
|
|
mean value: 0.835
|
|
|
|
key: train_fscore
|
|
value: [0.98039216 0.98039216 1. 1. 0.96 1.
|
|
1. 1. 0.98039216 1. ]
|
|
|
|
mean value: 0.9901176470588234
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 1. 1. 0.6
|
|
1. 1. 1. 0.66666667]
|
|
|
|
mean value: 0.9266666666666665
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.66666667 0.66666667 1. 1. 1.
|
|
0.66666667 0.66666667 0.66666667 1. ]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_recall
|
|
value: [0.96153846 0.96153846 1. 1. 0.92307692 1.
|
|
1. 1. 0.96153846 1. ]
|
|
|
|
mean value: 0.9807692307692308
|
|
|
|
key: test_accuracy
|
|
value: [0.875 0.875 0.875 1. 1. 0.75
|
|
0.875 0.875 0.875 0.85714286]
|
|
|
|
mean value: 0.8857142857142858
|
|
|
|
key: train_accuracy
|
|
value: [0.98591549 0.98591549 1. 1. 0.97183099 1.
|
|
1. 1. 0.98591549 1. ]
|
|
|
|
mean value: 0.9929577464788732
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 0.83333333 0.83333333 1. 1. 0.8
|
|
0.83333333 0.83333333 0.83333333 0.9 ]
|
|
|
|
mean value: 0.8699999999999999
|
|
|
|
key: train_roc_auc
|
|
value: [0.98076923 0.98076923 1. 1. 0.96153846 1.
|
|
1. 1. 0.98076923 1. ]
|
|
|
|
mean value: 0.9903846153846153
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.66666667 0.66666667 1. 1. 0.6
|
|
0.66666667 0.66666667 0.66666667 0.66666667]
|
|
|
|
mean value: 0.7266666666666668
|
|
|
|
key: train_jcc
|
|
value: [0.96153846 0.96153846 1. 1. 0.92307692 1.
|
|
1. 1. 0.96153846 1. ]
|
|
|
|
mean value: 0.9807692307692308
|
|
|
|
key: TN
|
|
value: 47
|
|
|
|
mean value: 47.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: TP
|
|
value: 23
|
|
|
|
mean value: 23.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.95
|
|
|
|
Accuracy on Blind test: 0.98
|
|
|
|
Running classifier: 20
|
|
Model_name: Gaussian Process
|
|
Model func: GaussianProcessClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianProcessClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01245141 0.01464605 0.01453471 0.01492357 0.01498866 0.01470661
|
|
0.01472354 0.01476574 0.01528478 0.01498938]
|
|
|
|
mean value: 0.014601445198059082
|
|
|
|
key: score_time
|
|
value: [0.01121616 0.01147175 0.01149368 0.01152205 0.01179218 0.01165199
|
|
0.01153016 0.01156569 0.01180983 0.01152635]
|
|
|
|
mean value: 0.011557984352111816
|
|
|
|
key: test_mcc
|
|
value: [-0.29277002 -0.06666667 0.74535599 -0.4472136 0.1490712 0.77459667
|
|
0.1490712 -0.29277002 0.48795004 0.73029674]
|
|
|
|
mean value: 0.1936921532620129
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0. 0.33333333 0.8 0. 0.4 0.85714286
|
|
0.4 0. 0.5 0.8 ]
|
|
|
|
mean value: 0.409047619047619
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0. 0.33333333 1. 0. 0.5 0.75
|
|
0.5 0. 1. 0.66666667]
|
|
|
|
mean value: 0.475
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0. 0.33333333 0.66666667 0. 0.33333333 1.
|
|
0.33333333 0. 0.33333333 1. ]
|
|
|
|
mean value: 0.4
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.5 0.5 0.875 0.375 0.625 0.875
|
|
0.625 0.5 0.75 0.85714286]
|
|
|
|
mean value: 0.6482142857142856
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.4 0.46666667 0.83333333 0.3 0.56666667 0.9
|
|
0.56666667 0.4 0.66666667 0.9 ]
|
|
|
|
mean value: 0.6000000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0. 0.2 0.66666667 0. 0.25 0.75
|
|
0.25 0. 0.33333333 0.66666667]
|
|
|
|
mean value: 0.31166666666666665
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: FP
|
|
value: 18
|
|
|
|
mean value: 18.0
|
|
|
|
key: FN
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: TP
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.18
|
|
|
|
Accuracy on Blind test: 0.65
|
|
|
|
Running classifier: 21
|
|
Model_name: Gradient Boosting
|
|
Model func: GradientBoostingClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GradientBoostingClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.17009902 0.14949942 0.16850829 0.15098715 0.15430665 0.15416098
|
|
0.1536386 0.15502644 0.12563252 0.17038202]
|
|
|
|
mean value: 0.15522410869598388
|
|
|
|
key: score_time
|
|
value: [0.00882673 0.00872111 0.00868773 0.00874734 0.00878859 0.0092032
|
|
0.00890565 0.00874686 0.00879812 0.00924945]
|
|
|
|
mean value: 0.008867478370666504
|
|
|
|
key: test_mcc
|
|
value: [0.74535599 0.74535599 0.46666667 0.77459667 1. 0.6
|
|
0.74535599 1. 0.74535599 0.54772256]
|
|
|
|
mean value: 0.7370409863413036
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.8 0.66666667 0.85714286 1. 0.75
|
|
0.8 1. 0.8 0.66666667]
|
|
|
|
mean value: 0.8140476190476191
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.66666667 0.75 1. 0.6
|
|
1. 1. 1. 0.5 ]
|
|
|
|
mean value: 0.8516666666666666
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.66666667 0.66666667 1. 1. 1.
|
|
0.66666667 1. 0.66666667 1. ]
|
|
|
|
mean value: 0.8333333333333333
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.875 0.875 0.75 0.875 1. 0.75
|
|
0.875 1. 0.875 0.71428571]
|
|
|
|
mean value: 0.8589285714285714
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 0.83333333 0.73333333 0.9 1. 0.8
|
|
0.83333333 1. 0.83333333 0.8 ]
|
|
|
|
mean value: 0.8566666666666667
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.66666667 0.5 0.75 1. 0.6
|
|
0.66666667 1. 0.66666667 0.5 ]
|
|
|
|
mean value: 0.7016666666666667
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 44
|
|
|
|
mean value: 44.0
|
|
|
|
key: FP
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: FN
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: TP
|
|
value: 24
|
|
|
|
mean value: 24.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.95
|
|
|
|
Accuracy on Blind test: 0.98
|
|
|
|
Running classifier: 22
|
|
Model_name: QDA
|
|
Model func: QuadraticDiscriminantAnalysis()
|
|
|
|
Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', QuadraticDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.01000357 0.01294374 0.01351094 0.01364636 0.01392603 0.01322412
|
|
0.0132308 0.01357937 0.01318026 0.01335979]
|
|
|
|
mean value: 0.013060498237609863
|
|
|
|
key: score_time
|
|
value: [0.01163244 0.0116744 0.01197124 0.01201701 0.01205063 0.01258826
|
|
0.01453471 0.01262641 0.01148725 0.01319456]
|
|
|
|
mean value: 0.012377691268920899
|
|
|
|
key: test_mcc
|
|
value: [-0.6 -0.06666667 0.74535599 -0.29277002 0.25819889 0.46666667
|
|
-0.46666667 -0.29277002 -0.06666667 0. ]
|
|
|
|
mean value: -0.03153184948553623
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0. 0.33333333 0.8 0. 0.57142857 0.66666667
|
|
0.25 0. 0.33333333 0. ]
|
|
|
|
mean value: 0.2954761904761905
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0. 0.33333333 1. 0. 0.5 0.66666667
|
|
0.2 0. 0.33333333 0. ]
|
|
|
|
mean value: 0.30333333333333334
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0. 0.33333333 0.66666667 0. 0.66666667 0.66666667
|
|
0.33333333 0. 0.33333333 0. ]
|
|
|
|
mean value: 0.3
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.25 0.5 0.875 0.5 0.625 0.75
|
|
0.25 0.5 0.5 0.71428571]
|
|
|
|
mean value: 0.5464285714285715
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.2 0.46666667 0.83333333 0.4 0.63333333 0.73333333
|
|
0.26666667 0.4 0.46666667 0.5 ]
|
|
|
|
mean value: 0.48999999999999994
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0. 0.2 0.66666667 0. 0.4 0.5
|
|
0.14285714 0. 0.2 0. ]
|
|
|
|
mean value: 0.21095238095238095
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 34
|
|
|
|
mean value: 34.0
|
|
|
|
key: FP
|
|
value: 20
|
|
|
|
mean value: 20.0
|
|
|
|
key: FN
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: TP
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.17
|
|
|
|
Accuracy on Blind test: 0.48
|
|
|
|
Running classifier: 23
|
|
Model_name: Ridge Classifier
|
|
Model func: RidgeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.04468751 0.05357623 0.05370712 0.03210664 0.01244235 0.01242089
|
|
0.01257348 0.01239419 0.01240015 0.03188992]
|
|
|
|
mean value: 0.02781984806060791
|
|
|
|
key: score_time
|
|
value: [0.02110004 0.02102828 0.02099276 0.02104115 0.01156378 0.01158667
|
|
0.0115962 0.01159286 0.01153088 0.02093101]
|
|
|
|
mean value: 0.01629636287689209
|
|
|
|
key: test_mcc
|
|
value: [ 0.74535599 0.46666667 0.74535599 0.6 0.46666667 0.6
|
|
0.77459667 0.1490712 -0.06666667 0.73029674]
|
|
|
|
mean value: 0.5211343262748217
|
|
|
|
key: train_mcc
|
|
value: [0.96986363 0.96986363 1. 1. 1. 1.
|
|
1. 0.96986363 1. 1. ]
|
|
|
|
mean value: 0.9909590875629279
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.66666667 0.8 0.75 0.66666667 0.75
|
|
0.85714286 0.4 0.33333333 0.8 ]
|
|
|
|
mean value: 0.6823809523809523
|
|
|
|
key: train_fscore
|
|
value: [0.98039216 0.98039216 1. 1. 1. 1.
|
|
1. 0.98039216 1. 1. ]
|
|
|
|
mean value: 0.9941176470588236
|
|
|
|
key: test_precision
|
|
value: [1. 0.66666667 1. 0.6 0.66666667 0.6
|
|
0.75 0.5 0.33333333 0.66666667]
|
|
|
|
mean value: 0.6783333333333333
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.66666667 0.66666667 1. 0.66666667 1.
|
|
1. 0.33333333 0.33333333 1. ]
|
|
|
|
mean value: 0.7333333333333333
|
|
|
|
key: train_recall
|
|
value: [0.96153846 0.96153846 1. 1. 1. 1.
|
|
1. 0.96153846 1. 1. ]
|
|
|
|
mean value: 0.9884615384615385
|
|
|
|
key: test_accuracy
|
|
value: [0.875 0.75 0.875 0.75 0.75 0.75
|
|
0.875 0.625 0.5 0.85714286]
|
|
|
|
mean value: 0.7607142857142857
|
|
|
|
key: train_accuracy
|
|
value: [0.98591549 0.98591549 1. 1. 1. 1.
|
|
1. 0.98591549 1. 1. ]
|
|
|
|
mean value: 0.995774647887324
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 0.73333333 0.83333333 0.8 0.73333333 0.8
|
|
0.9 0.56666667 0.46666667 0.9 ]
|
|
|
|
mean value: 0.7566666666666666
|
|
|
|
key: train_roc_auc
|
|
value: [0.98076923 0.98076923 1. 1. 1. 1.
|
|
1. 0.98076923 1. 1. ]
|
|
|
|
mean value: 0.9942307692307694
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.5 0.66666667 0.6 0.5 0.6
|
|
0.75 0.25 0.2 0.66666667]
|
|
|
|
mean value: 0.54
|
|
|
|
key: train_jcc
|
|
value: [0.96153846 0.96153846 1. 1. 1. 1.
|
|
1. 0.96153846 1. 1. ]
|
|
|
|
mean value: 0.9884615384615385
|
|
|
|
key: TN
|
|
value: 39
|
|
|
|
mean value: 39.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: TP
|
|
value: 21
|
|
|
|
mean value: 21.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.25
|
|
|
|
Accuracy on Blind test: 0.68
|
|
|
|
Running classifier: 24
|
|
Model_name: Ridge ClassifierCV
|
|
Model func: RidgeClassifierCV(cv=10)
|
|
|
|
Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:130: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
baseline_CV['Data_source'] = 'CV'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:131: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
baseline_CV['Resampling'] = rs_none
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:136: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
baseline_BT['Data_source'] = 'BT'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:137: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
baseline_BT['Resampling'] = rs_none
|
|
Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifierCV(cv=10))])
|
|
|
|
key: fit_time
|
|
value: [0.12132359 0.10357308 0.14754748 0.10202861 0.10264635 0.11086059
|
|
0.16924834 0.22648358 0.17872405 0.17529178]
|
|
|
|
mean value: 0.14377274513244628
|
|
|
|
key: score_time
|
|
value: [0.02111197 0.01191688 0.02108765 0.01181531 0.02225852 0.01208162
|
|
0.02574563 0.02083993 0.02090573 0.01990175]
|
|
|
|
mean value: 0.018766498565673827
|
|
|
|
key: test_mcc
|
|
value: [ 0.74535599 0.46666667 0.74535599 -0.06666667 0.46666667 0.6
|
|
0.77459667 0.1490712 0.06666667 0.3 ]
|
|
|
|
mean value: 0.4247713186074663
|
|
|
|
key: train_mcc
|
|
value: [1. 0.96986363 1. 0.81830122 1. 1.
|
|
1. 0.96986363 1. 0.77297107]
|
|
|
|
mean value: 0.953099954418505
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.66666667 0.8 0.33333333 0.66666667 0.75
|
|
0.85714286 0.4 0.5 0.5 ]
|
|
|
|
mean value: 0.6273809523809524
|
|
|
|
key: train_fscore
|
|
value: [1. 0.98039216 1. 0.875 1. 1.
|
|
1. 0.98039216 1. 0.82608696]
|
|
|
|
mean value: 0.9661871270247229
|
|
|
|
key: test_precision
|
|
value: [1. 0.66666667 1. 0.33333333 0.66666667 0.6
|
|
0.75 0.5 0.4 0.5 ]
|
|
|
|
mean value: 0.6416666666666667
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 0.95454545 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9954545454545455
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.66666667 0.66666667 0.33333333 0.66666667 1.
|
|
1. 0.33333333 0.66666667 0.5 ]
|
|
|
|
mean value: 0.65
|
|
|
|
key: train_recall
|
|
value: [1. 0.96153846 1. 0.80769231 1. 1.
|
|
1. 0.96153846 1. 0.7037037 ]
|
|
|
|
mean value: 0.9434472934472934
|
|
|
|
key: test_accuracy
|
|
value: [0.875 0.75 0.875 0.5 0.75 0.75
|
|
0.875 0.625 0.5 0.71428571]
|
|
|
|
mean value: 0.7214285714285714
|
|
|
|
key: train_accuracy
|
|
value: [1. 0.98591549 1. 0.91549296 1. 1.
|
|
1. 0.98591549 1. 0.88888889]
|
|
|
|
mean value: 0.9776212832550861
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 0.73333333 0.83333333 0.46666667 0.73333333 0.8
|
|
0.9 0.56666667 0.53333333 0.65 ]
|
|
|
|
mean value: 0.705
|
|
|
|
key: train_roc_auc
|
|
value: [1. 0.98076923 1. 0.89273504 1. 1.
|
|
1. 0.98076923 1. 0.85185185]
|
|
|
|
mean value: 0.9706125356125355
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.5 0.66666667 0.2 0.5 0.6
|
|
0.75 0.25 0.33333333 0.33333333]
|
|
|
|
mean value: 0.47999999999999987
|
|
|
|
key: train_jcc
|
|
value: [1. 0.96153846 1. 0.77777778 1. 1.
|
|
1. 0.96153846 1. 0.7037037 ]
|
|
|
|
mean value: 0.9404558404558406
|
|
|
|
key: TN
|
|
value: 38
|
|
|
|
mean value: 38.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: TP
|
|
value: 19
|
|
|
|
mean value: 19.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.09
|
|
|
|
Accuracy on Blind test: 0.6
|
|
|
|
PASS: sorting df by score that is mapped onto the order I want
|
|
|
|
==============================================================
|
|
|
|
Running several classification models (n): 24
|
|
List of models:
|
|
('Logistic Regression', LogisticRegression(random_state=42))
|
|
('Logistic RegressionCV', LogisticRegressionCV(random_state=42))
|
|
('Gaussian NB', GaussianNB())
|
|
('Naive Bayes', BernoulliNB())
|
|
('K-Nearest Neighbors', KNeighborsClassifier())
|
|
('SVC', SVC(random_state=42))
|
|
('MLP', MLPClassifier(max_iter=500, random_state=42))
|
|
('Decision Tree', DecisionTreeClassifier(random_state=42))
|
|
('Extra Trees', ExtraTreesClassifier(random_state=42))
|
|
('Extra Tree', ExtraTreeClassifier(random_state=42))
|
|
('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42))
|
|
('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42))
|
|
('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0))
|
|
('LDA', LinearDiscriminantAnalysis())
|
|
('Multinomial', MultinomialNB())
|
|
('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42))
|
|
('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42))
|
|
('AdaBoost Classifier', AdaBoostClassifier(random_state=42))
|
|
('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))
|
|
('Gaussian Process', GaussianProcessClassifier(random_state=42))
|
|
('Gradient Boosting', GradientBoostingClassifier(random_state=42))
|
|
('QDA', QuadraticDiscriminantAnalysis())
|
|
('Ridge Classifier', RidgeClassifier(random_state=42))
|
|
('Ridge ClassifierCV', RidgeClassifierCV(cv=10))
|
|
|
|
================================================================
|
|
|
|
|
|
Running classifier: 1
|
|
Model_name: Logistic Regression
|
|
Model func: LogisticRegression(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegression(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02095628 0.02766466 0.02307987 0.02544475 0.02728295 0.0233891
|
|
0.02444839 0.02446628 0.02351332 0.02731419]
|
|
|
|
mean value: 0.024755978584289552
|
|
|
|
key: score_time
|
|
value: [0.01138926 0.01142073 0.01142931 0.01147127 0.01143646 0.01138425
|
|
0.01142192 0.0114305 0.0113945 0.01140404]
|
|
|
|
mean value: 0.01141822338104248
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.81649658 1. 0.21821789 0.40824829 0.81649658
|
|
0.6 0.65465367 0.21821789 0.6 ]
|
|
|
|
mean value: 0.6148827484427002
|
|
|
|
key: train_mcc
|
|
value: [0.97801929 0.95555556 0.95555556 0.95555556 0.95555556 0.97801929
|
|
0.95555556 0.95555556 1. 0.93356387]
|
|
|
|
mean value: 0.962293579241685
|
|
|
|
key: test_fscore
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
[0.90909091 0.90909091 1. 0.66666667 0.72727273 0.88888889
|
|
0.8 0.83333333 0.66666667 0.8 ]
|
|
|
|
mean value: 0.8201010101010102
|
|
|
|
key: train_fscore
|
|
value: [0.98876404 0.97777778 0.97777778 0.97777778 0.97777778 0.98876404
|
|
0.97777778 0.97777778 1. 0.96703297]
|
|
|
|
mean value: 0.9811227723587275
|
|
|
|
key: test_precision
|
|
value: [0.83333333 0.83333333 1. 0.57142857 0.66666667 1.
|
|
0.8 0.71428571 0.57142857 0.8 ]
|
|
|
|
mean value: 0.779047619047619
|
|
|
|
key: train_precision
|
|
value: [1. 0.97777778 0.97777778 0.97777778 0.97777778 1.
|
|
0.97777778 0.97777778 1. 0.95652174]
|
|
|
|
mean value: 0.9823188405797103
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 0.8 0.8 0.8 0.8 1. 0.8 0.8]
|
|
|
|
mean value: 0.8800000000000001
|
|
|
|
key: train_recall
|
|
value: [0.97777778 0.97777778 0.97777778 0.97777778 0.97777778 0.97777778
|
|
0.97777778 0.97777778 1. 0.97777778]
|
|
|
|
mean value: 0.9800000000000001
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.9 1. 0.6 0.7 0.9 0.8 0.8 0.6 0.8]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_accuracy
|
|
value: [0.98888889 0.97777778 0.97777778 0.97777778 0.97777778 0.98888889
|
|
0.97777778 0.97777778 1. 0.96666667]
|
|
|
|
mean value: 0.9811111111111112
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.9 1. 0.6 0.7 0.9 0.8 0.8 0.6 0.8]
|
|
|
|
mean value: 0.8000000000000002
|
|
|
|
key: train_roc_auc
|
|
value: [0.98888889 0.97777778 0.97777778 0.97777778 0.97777778 0.98888889
|
|
0.97777778 0.97777778 1. 0.96666667]
|
|
|
|
mean value: 0.9811111111111112
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.83333333 1. 0.5 0.57142857 0.8
|
|
0.66666667 0.71428571 0.5 0.66666667]
|
|
|
|
mean value: 0.7085714285714286
|
|
|
|
key: train_jcc
|
|
value: [0.97777778 0.95652174 0.95652174 0.95652174 0.95652174 0.97777778
|
|
0.95652174 0.95652174 1. 0.93617021]
|
|
|
|
mean value: 0.9630856203104123
|
|
|
|
key: TN
|
|
value: 36
|
|
|
|
mean value: 36.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: TP
|
|
value: 44
|
|
|
|
mean value: 44.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.23
|
|
|
|
Accuracy on Blind test: 0.65
|
|
|
|
Running classifier: 2
|
|
Model_name: Logistic RegressionCV
|
|
Model func: LogisticRegressionCV(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegressionCV(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.63891172 0.51611137 0.76950169 0.56394839 0.5907445 0.58179784
|
|
0.55293965 0.53497767 0.65838146 0.52853274]
|
|
|
|
mean value: 0.5935847043991089
|
|
|
|
key: score_time
|
|
value: [0.01189089 0.01190567 0.01321769 0.01461577 0.01429796 0.0120244
|
|
0.01439905 0.01183438 0.01506233 0.01375699]
|
|
|
|
mean value: 0.013300514221191407
|
|
|
|
key: test_mcc
|
|
value: [1. 0.81649658 1. 0.21821789 0.6 0.81649658
|
|
0.40824829 0.81649658 0.40824829 0.81649658]
|
|
|
|
mean value: 0.6900700794874622
|
|
|
|
key: train_mcc
|
|
value: [0.97801929 1. 1. 1. 1. 0.97801929
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9956038587687303
|
|
|
|
key: test_fscore
|
|
value: [1. 0.90909091 1. 0.66666667 0.8 0.88888889
|
|
0.66666667 0.90909091 0.72727273 0.88888889]
|
|
|
|
mean value: 0.8456565656565657
|
|
|
|
key: train_fscore
|
|
value: [0.98876404 1. 1. 1. 1. 0.98876404
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.997752808988764
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 1. 0.57142857 0.8 1.
|
|
0.75 0.83333333 0.66666667 1. ]
|
|
|
|
mean value: 0.8454761904761906
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 0.8 0.8 0.8 0.6 1. 0.8 0.8]
|
|
|
|
mean value: 0.86
|
|
|
|
key: train_recall
|
|
value: [0.97777778 1. 1. 1. 1. 0.97777778
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9955555555555555
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9 1. 0.6 0.8 0.9 0.7 0.9 0.7 0.9]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_accuracy
|
|
value: [0.98888889 1. 1. 1. 1. 0.98888889
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9977777777777778
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.9 1. 0.6 0.8 0.9 0.7 0.9 0.7 0.9]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [0.98888889 1. 1. 1. 1. 0.98888889
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9977777777777778
|
|
|
|
key: test_jcc
|
|
value: [1. 0.83333333 1. 0.5 0.66666667 0.8
|
|
0.5 0.83333333 0.57142857 0.8 ]
|
|
|
|
mean value: 0.7504761904761905
|
|
|
|
key: train_jcc
|
|
value: [0.97777778 1. 1. 1. 1. 0.97777778
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9955555555555555
|
|
|
|
key: TN
|
|
value: 41
|
|
|
|
mean value: 41.0
|
|
|
|
key: FP
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: FN
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: TP
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.25
|
|
|
|
Accuracy on Blind test: 0.68
|
|
|
|
Running classifier: 3
|
|
Model_name: Gaussian NB
|
|
Model func: GaussianNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianNB())])
|
|
|
|
key: fit_time
|
|
value: [0.011446 0.01138425 0.00847435 0.0083313 0.00806546 0.00832367
|
|
0.0079782 0.00797153 0.00808334 0.00805235]
|
|
|
|
mean value: 0.008811044692993163
|
|
|
|
key: score_time
|
|
value: [0.01148987 0.01026154 0.00851727 0.00848866 0.00831866 0.00834775
|
|
0.00825739 0.00826335 0.00835299 0.00837731]
|
|
|
|
mean value: 0.008867478370666504
|
|
|
|
key: test_mcc
|
|
value: [ 0.40824829 0.65465367 0.33333333 0.2 0.5 -0.21821789
|
|
0.2 0.33333333 -0.33333333 0. ]
|
|
|
|
mean value: 0.20780174042691812
|
|
|
|
key: train_mcc
|
|
value: [0.4412613 0.57055978 0.57055978 0.66097134 0.57055978 0.51708769
|
|
0.76486616 0.5527708 0.6681531 0.63737744]
|
|
|
|
mean value: 0.5954167177357577
|
|
|
|
key: test_fscore
|
|
value: [0.72727273 0.83333333 0.71428571 0.6 0.76923077 0.5
|
|
0.6 0.71428571 0.57142857 0.66666667]
|
|
|
|
mean value: 0.6696503496503496
|
|
|
|
key: train_fscore
|
|
value: [0.75229358 0.8 0.8 0.84 0.8 0.77876106
|
|
0.88659794 0.79279279 0.83870968 0.82568807]
|
|
|
|
mean value: 0.8114843121679529
|
|
|
|
key: test_precision
|
|
value: [0.66666667 0.71428571 0.55555556 0.6 0.625 0.42857143
|
|
0.6 0.55555556 0.44444444 0.5 ]
|
|
|
|
mean value: 0.5690079365079365
|
|
|
|
key: train_precision
|
|
value: [0.640625 0.67692308 0.67692308 0.76363636 0.67692308 0.64705882
|
|
0.82692308 0.66666667 0.8125 0.703125 ]
|
|
|
|
mean value: 0.709130416152475
|
|
|
|
key: test_recall
|
|
value: [0.8 1. 1. 0.6 1. 0.6 0.6 1. 0.8 1. ]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_recall
|
|
value: [0.91111111 0.97777778 0.97777778 0.93333333 0.97777778 0.97777778
|
|
0.95555556 0.97777778 0.86666667 1. ]
|
|
|
|
mean value: 0.9555555555555555
|
|
|
|
key: test_accuracy
|
|
value: [0.7 0.8 0.6 0.6 0.7 0.4 0.6 0.6 0.4 0.5]
|
|
|
|
mean value: 0.5900000000000001
|
|
|
|
key: train_accuracy
|
|
value: [0.7 0.75555556 0.75555556 0.82222222 0.75555556 0.72222222
|
|
0.87777778 0.74444444 0.83333333 0.78888889]
|
|
|
|
mean value: 0.7755555555555554
|
|
|
|
key: test_roc_auc
|
|
value: [0.7 0.8 0.6 0.6 0.7 0.4 0.6 0.6 0.4 0.5]
|
|
|
|
mean value: 0.5900000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [0.7 0.75555556 0.75555556 0.82222222 0.75555556 0.72222222
|
|
0.87777778 0.74444444 0.83333333 0.78888889]
|
|
|
|
mean value: 0.7755555555555554
|
|
|
|
key: test_jcc
|
|
value: [0.57142857 0.71428571 0.55555556 0.42857143 0.625 0.33333333
|
|
0.42857143 0.55555556 0.4 0.5 ]
|
|
|
|
mean value: 0.5112301587301588
|
|
|
|
key: train_jcc
|
|
value: [0.60294118 0.66666667 0.66666667 0.72413793 0.66666667 0.63768116
|
|
0.7962963 0.65671642 0.72222222 0.703125 ]
|
|
|
|
mean value: 0.6843120203354327
|
|
|
|
key: TN
|
|
value: 17
|
|
|
|
mean value: 17.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 33
|
|
|
|
mean value: 33.0
|
|
|
|
key: TP
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.25
|
|
|
|
Accuracy on Blind test: 0.55
|
|
|
|
Running classifier: 4
|
|
Model_name: Naive Bayes
|
|
Model func: BernoulliNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', BernoulliNB())])
|
|
|
|
key: fit_time
|
|
value: [0.00854492 0.00822186 0.00823379 0.00817966 0.00828838 0.00816011
|
|
0.00876474 0.00883484 0.00813937 0.00822973]
|
|
|
|
mean value: 0.008359742164611817
|
|
|
|
key: score_time
|
|
value: [0.00832868 0.00838923 0.00824618 0.00832224 0.00821042 0.00837898
|
|
0.00903296 0.00819707 0.00829124 0.00826311]
|
|
|
|
mean value: 0.008366012573242187
|
|
|
|
key: test_mcc
|
|
value: [ 0.65465367 -0.40824829 0.81649658 0. 0.40824829 0.40824829
|
|
0.6 0.40824829 -0.40824829 0. ]
|
|
|
|
mean value: 0.2479398542099566
|
|
|
|
key: train_mcc
|
|
value: [0.60238451 0.73994007 0.65487619 0.64700558 0.69162666 0.62988978
|
|
0.71554175 0.78086881 0.64508188 0.65487619]
|
|
|
|
mean value: 0.6762091428071246
|
|
|
|
key: test_fscore
|
|
value: [0.75 0.36363636 0.90909091 0.61538462 0.72727273 0.66666667
|
|
0.8 0.72727273 0.36363636 0.54545455]
|
|
|
|
mean value: 0.6468414918414919
|
|
|
|
key: train_fscore
|
|
value: [0.80851064 0.875 0.83673469 0.82978723 0.85106383 0.82474227
|
|
0.86315789 0.89361702 0.81818182 0.83673469]
|
|
|
|
mean value: 0.8437530092119255
|
|
|
|
key: test_precision
|
|
value: [1. 0.33333333 0.83333333 0.5 0.66666667 0.75
|
|
0.8 0.66666667 0.33333333 0.5 ]
|
|
|
|
mean value: 0.6383333333333334
|
|
|
|
key: train_precision
|
|
value: [0.7755102 0.82352941 0.77358491 0.79591837 0.81632653 0.76923077
|
|
0.82 0.85714286 0.8372093 0.77358491]
|
|
|
|
mean value: 0.8042037253825484
|
|
|
|
key: test_recall
|
|
value: [0.6 0.4 1. 0.8 0.8 0.6 0.8 0.8 0.4 0.6]
|
|
|
|
mean value: 0.6799999999999999
|
|
|
|
key: train_recall
|
|
value: [0.84444444 0.93333333 0.91111111 0.86666667 0.88888889 0.88888889
|
|
0.91111111 0.93333333 0.8 0.91111111]
|
|
|
|
mean value: 0.8888888888888887
|
|
|
|
key: test_accuracy
|
|
value: [0.8 0.3 0.9 0.5 0.7 0.7 0.8 0.7 0.3 0.5]
|
|
|
|
mean value: 0.62
|
|
|
|
key: train_accuracy
|
|
value: [0.8 0.86666667 0.82222222 0.82222222 0.84444444 0.81111111
|
|
0.85555556 0.88888889 0.82222222 0.82222222]
|
|
|
|
mean value: 0.8355555555555554
|
|
|
|
key: test_roc_auc
|
|
value: [0.8 0.3 0.9 0.5 0.7 0.7 0.8 0.7 0.3 0.5]
|
|
|
|
mean value: 0.62
|
|
|
|
key: train_roc_auc
|
|
value: [0.8 0.86666667 0.82222222 0.82222222 0.84444444 0.81111111
|
|
0.85555556 0.88888889 0.82222222 0.82222222]
|
|
|
|
mean value: 0.8355555555555556
|
|
|
|
key: test_jcc
|
|
value: [0.6 0.22222222 0.83333333 0.44444444 0.57142857 0.5
|
|
0.66666667 0.57142857 0.22222222 0.375 ]
|
|
|
|
mean value: 0.5006746031746031
|
|
|
|
key: train_jcc
|
|
value: [0.67857143 0.77777778 0.71929825 0.70909091 0.74074074 0.70175439
|
|
0.75925926 0.80769231 0.69230769 0.71929825]
|
|
|
|
mean value: 0.7305790992633099
|
|
|
|
key: TN
|
|
value: 28
|
|
|
|
mean value: 28.0
|
|
|
|
key: FP
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: FN
|
|
value: 22
|
|
|
|
mean value: 22.0
|
|
|
|
key: TP
|
|
value: 34
|
|
|
|
mean value: 34.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.1
|
|
|
|
Accuracy on Blind test: 0.52
|
|
|
|
Running classifier: 5
|
|
Model_name: K-Nearest Neighbors
|
|
Model func: KNeighborsClassifier()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', KNeighborsClassifier())])
|
|
|
|
key: fit_time
|
|
value: [0.01051331 0.00788808 0.0079484 0.00784755 0.00783825 0.00789666
|
|
0.00844765 0.00793743 0.00793314 0.00828576]
|
|
|
|
mean value: 0.00825362205505371
|
|
|
|
key: score_time
|
|
value: [0.01452923 0.00902128 0.00907946 0.00902486 0.00899076 0.00922275
|
|
0.00933123 0.00924468 0.00922418 0.00955987]
|
|
|
|
mean value: 0.00972282886505127
|
|
|
|
key: test_mcc
|
|
value: [ 0.2 0.6 0.81649658 0.5 0.65465367 -0.40824829
|
|
0.5 0.65465367 0.21821789 0.40824829]
|
|
|
|
mean value: 0.41440218125796735
|
|
|
|
key: train_mcc
|
|
value: [0.60540551 0.65487619 0.60540551 0.62237591 0.58969198 0.67082039
|
|
0.62360956 0.58137767 0.73624773 0.60540551]
|
|
|
|
mean value: 0.6295215990305435
|
|
|
|
key: test_fscore
|
|
value: [0.6 0.8 0.90909091 0.76923077 0.83333333 0.36363636
|
|
0.76923077 0.83333333 0.66666667 0.72727273]
|
|
|
|
mean value: 0.7271794871794872
|
|
|
|
key: train_fscore
|
|
value: [0.8125 0.83673469 0.8125 0.81318681 0.80808081 0.84210526
|
|
0.8172043 0.8 0.87234043 0.8125 ]
|
|
|
|
mean value: 0.822715230491025
|
|
|
|
key: test_precision
|
|
value: [0.6 0.8 0.83333333 0.625 0.71428571 0.33333333
|
|
0.625 0.71428571 0.57142857 0.66666667]
|
|
|
|
mean value: 0.6483333333333333
|
|
|
|
key: train_precision
|
|
value: [0.76470588 0.77358491 0.76470588 0.80434783 0.74074074 0.8
|
|
0.79166667 0.76 0.83673469 0.76470588]
|
|
|
|
mean value: 0.7801192480091116
|
|
|
|
key: test_recall
|
|
value: [0.6 0.8 1. 1. 1. 0.4 1. 1. 0.8 0.8]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_recall
|
|
value: [0.86666667 0.91111111 0.86666667 0.82222222 0.88888889 0.88888889
|
|
0.84444444 0.84444444 0.91111111 0.86666667]
|
|
|
|
mean value: 0.8711111111111112
|
|
|
|
key: test_accuracy
|
|
value: [0.6 0.8 0.9 0.7 0.8 0.3 0.7 0.8 0.6 0.7]
|
|
|
|
mean value: 0.69
|
|
|
|
key: train_accuracy
|
|
value: [0.8 0.82222222 0.8 0.81111111 0.78888889 0.83333333
|
|
0.81111111 0.78888889 0.86666667 0.8 ]
|
|
|
|
mean value: 0.8122222222222224
|
|
|
|
key: test_roc_auc
|
|
value: [0.6 0.8 0.9 0.7 0.8 0.3 0.7 0.8 0.6 0.7]
|
|
|
|
mean value: 0.69
|
|
|
|
key: train_roc_auc
|
|
value: [0.8 0.82222222 0.8 0.81111111 0.78888889 0.83333333
|
|
0.81111111 0.78888889 0.86666667 0.8 ]
|
|
|
|
mean value: 0.8122222222222222
|
|
|
|
key: test_jcc
|
|
value: [0.42857143 0.66666667 0.83333333 0.625 0.71428571 0.22222222
|
|
0.625 0.71428571 0.5 0.57142857]
|
|
|
|
mean value: 0.5900793650793651
|
|
|
|
key: train_jcc
|
|
value: [0.68421053 0.71929825 0.68421053 0.68518519 0.6779661 0.72727273
|
|
0.69090909 0.66666667 0.77358491 0.68421053]
|
|
|
|
mean value: 0.6993514501950366
|
|
|
|
key: TN
|
|
value: 27
|
|
|
|
mean value: 27.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 23
|
|
|
|
mean value: 23.0
|
|
|
|
key: TP
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.04
|
|
|
|
Accuracy on Blind test: 0.48
|
|
|
|
Running classifier: 6
|
|
Model_name: SVC
|
|
Model func: SVC(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SVC(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00914907 0.00866842 0.00909472 0.00902867 0.00853419 0.00859928
|
|
0.0086832 0.00868559 0.0086875 0.00870299]
|
|
|
|
mean value: 0.008783364295959472
|
|
|
|
key: score_time
|
|
value: [0.00892496 0.0084846 0.00915527 0.00837111 0.00835466 0.00840259
|
|
0.00836229 0.00844765 0.00840688 0.0084002 ]
|
|
|
|
mean value: 0.008531022071838378
|
|
|
|
key: test_mcc
|
|
value: [0.40824829 0.6 1. 0.40824829 0.40824829 0.81649658
|
|
0.6 0.65465367 0.21821789 0.40824829]
|
|
|
|
mean value: 0.5522361303727148
|
|
|
|
key: train_mcc
|
|
value: [0.8230355 0.86666667 0.84632727 0.84465303 0.82548988 0.87011096
|
|
0.88910845 0.86666667 0.91111111 0.84632727]
|
|
|
|
mean value: 0.8589496793973822
|
|
|
|
key: test_fscore
|
|
value: [0.66666667 0.8 1. 0.72727273 0.72727273 0.88888889
|
|
0.8 0.83333333 0.66666667 0.66666667]
|
|
|
|
mean value: 0.7776767676767677
|
|
|
|
key: train_fscore
|
|
value: [0.91304348 0.93333333 0.92473118 0.92134831 0.91489362 0.93617021
|
|
0.94505495 0.93333333 0.95555556 0.92473118]
|
|
|
|
mean value: 0.9302195155523411
|
|
|
|
key: test_precision
|
|
value: [0.75 0.8 1. 0.66666667 0.66666667 1.
|
|
0.8 0.71428571 0.57142857 0.75 ]
|
|
|
|
mean value: 0.7719047619047619
|
|
|
|
key: train_precision
|
|
value: [0.89361702 0.93333333 0.89583333 0.93181818 0.87755102 0.89795918
|
|
0.93478261 0.93333333 0.95555556 0.89583333]
|
|
|
|
mean value: 0.9149616904760952
|
|
|
|
key: test_recall
|
|
value: [0.6 0.8 1. 0.8 0.8 0.8 0.8 1. 0.8 0.6]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_recall
|
|
value: [0.93333333 0.93333333 0.95555556 0.91111111 0.95555556 0.97777778
|
|
0.95555556 0.93333333 0.95555556 0.95555556]
|
|
|
|
mean value: 0.9466666666666667
|
|
|
|
key: test_accuracy
|
|
value: [0.7 0.8 1. 0.7 0.7 0.9 0.8 0.8 0.6 0.7]
|
|
|
|
mean value: 0.77
|
|
|
|
key: train_accuracy
|
|
value: [0.91111111 0.93333333 0.92222222 0.92222222 0.91111111 0.93333333
|
|
0.94444444 0.93333333 0.95555556 0.92222222]
|
|
|
|
mean value: 0.928888888888889
|
|
|
|
key: test_roc_auc
|
|
value: [0.7 0.8 1. 0.7 0.7 0.9 0.8 0.8 0.6 0.7]
|
|
|
|
mean value: 0.77
|
|
|
|
key: train_roc_auc
|
|
value: [0.91111111 0.93333333 0.92222222 0.92222222 0.91111111 0.93333333
|
|
0.94444444 0.93333333 0.95555556 0.92222222]
|
|
|
|
mean value: 0.928888888888889
|
|
|
|
key: test_jcc
|
|
value: [0.5 0.66666667 1. 0.57142857 0.57142857 0.8
|
|
0.66666667 0.71428571 0.5 0.5 ]
|
|
|
|
mean value: 0.6490476190476191
|
|
|
|
key: train_jcc
|
|
value: [0.84 0.875 0.86 0.85416667 0.84313725 0.88
|
|
0.89583333 0.875 0.91489362 0.86 ]
|
|
|
|
mean value: 0.8698030871923237
|
|
|
|
key: TN
|
|
value: 37
|
|
|
|
mean value: 37.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: TP
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.1
|
|
|
|
Accuracy on Blind test: 0.5
|
|
|
|
Running classifier: 7
|
|
Model_name: MLP
|
|
Model func: MLPClassifier(max_iter=500, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MLPClassifier(max_iter=500, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.34990168 0.50119901 0.34612966 0.31338263 0.36281276 0.32218599
|
|
0.34134626 0.46834445 0.28037453 0.3393724 ]
|
|
|
|
mean value: 0.3625049352645874
|
|
|
|
key: score_time
|
|
value: [0.01199079 0.01198435 0.011935 0.01188803 0.01190996 0.01193237
|
|
0.01193595 0.0119586 0.01192045 0.01193857]
|
|
|
|
mean value: 0.011939406394958496
|
|
|
|
key: test_mcc
|
|
value: [0.65465367 0.81649658 1. 0. 0.2 0.81649658
|
|
0.40824829 0.65465367 0. 0.81649658]
|
|
|
|
mean value: 0.5367045374662995
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.75 0.90909091 1. 0.54545455 0.6 0.88888889
|
|
0.66666667 0.83333333 0.61538462 0.88888889]
|
|
|
|
mean value: 0.7697707847707849
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 1. 0.5 0.6 1.
|
|
0.75 0.71428571 0.5 1. ]
|
|
|
|
mean value: 0.7897619047619048
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.6 1. 1. 0.6 0.6 0.8 0.6 1. 0.8 0.8]
|
|
|
|
mean value: 0.78
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.8 0.9 1. 0.5 0.6 0.9 0.7 0.8 0.5 0.9]
|
|
|
|
mean value: 0.76
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.8 0.9 1. 0.5 0.6 0.9 0.7 0.8 0.5 0.9]
|
|
|
|
mean value: 0.76
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.6 0.83333333 1. 0.375 0.42857143 0.8
|
|
0.5 0.71428571 0.44444444 0.8 ]
|
|
|
|
mean value: 0.6495634920634921
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 37
|
|
|
|
mean value: 37.0
|
|
|
|
key: FP
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: FN
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: TP
|
|
value: 39
|
|
|
|
mean value: 39.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.13
|
|
|
|
Accuracy on Blind test: 0.62
|
|
|
|
Running classifier: 8
|
|
Model_name: Decision Tree
|
|
Model func: DecisionTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', DecisionTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01446295 0.01370049 0.01050329 0.01081491 0.01074243 0.01266026
|
|
0.01105475 0.01161981 0.01020932 0.01007462]
|
|
|
|
mean value: 0.011584281921386719
|
|
|
|
key: score_time
|
|
value: [0.01130819 0.01019692 0.00851107 0.00835633 0.00995207 0.00970793
|
|
0.00899029 0.00867486 0.00860381 0.00912476]
|
|
|
|
mean value: 0.009342622756958009
|
|
|
|
key: test_mcc
|
|
value: [1. 0.81649658 0.65465367 0.65465367 1. 0.6
|
|
0.65465367 0.40824829 0.40824829 0.81649658]
|
|
|
|
mean value: 0.701345075490711
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.90909091 0.83333333 0.83333333 1. 0.8
|
|
0.75 0.72727273 0.72727273 0.88888889]
|
|
|
|
mean value: 0.846919191919192
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 0.71428571 0.71428571 1. 0.8
|
|
1. 0.66666667 0.66666667 1. ]
|
|
|
|
mean value: 0.8395238095238096
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 1. 1. 0.8 0.6 0.8 0.8 0.8]
|
|
|
|
mean value: 0.8800000000000001
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9 0.8 0.8 1. 0.8 0.8 0.7 0.7 0.9]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.9 0.8 0.8 1. 0.8 0.8 0.7 0.7 0.9]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.83333333 0.71428571 0.71428571 1. 0.66666667
|
|
0.6 0.57142857 0.57142857 0.8 ]
|
|
|
|
mean value: 0.7471428571428571
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: TP
|
|
value: 44
|
|
|
|
mean value: 44.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.75
|
|
|
|
Accuracy on Blind test: 0.88
|
|
|
|
Running classifier: 9
|
|
Model_name: Extra Trees
|
|
Model func: ExtraTreesClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreesClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.08087611 0.08038592 0.08071065 0.08093286 0.08061075 0.0805552
|
|
0.08088231 0.08353758 0.08065701 0.08074427]
|
|
|
|
mean value: 0.08098926544189453
|
|
|
|
key: score_time
|
|
value: [0.01645565 0.01689053 0.01666975 0.01656699 0.0165782 0.01668191
|
|
0.01906228 0.01703238 0.01756358 0.01673245]
|
|
|
|
mean value: 0.017023372650146484
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.6 1. 0.40824829 0.6 0.5
|
|
0.2 0.65465367 0.21821789 0.65465367]
|
|
|
|
mean value: 0.5652270103043536
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.88888889 0.8 1. 0.72727273 0.8 0.57142857
|
|
0.6 0.83333333 0.66666667 0.75 ]
|
|
|
|
mean value: 0.7637590187590187
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.8 1. 0.66666667 0.8 1.
|
|
0.6 0.71428571 0.57142857 1. ]
|
|
|
|
mean value: 0.8152380952380952
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.8 0.8 1. 0.8 0.8 0.4 0.6 1. 0.8 0.6]
|
|
|
|
mean value: 0.76
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.8 1. 0.7 0.8 0.7 0.6 0.8 0.6 0.8]
|
|
|
|
mean value: 0.77
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.8 1. 0.7 0.8 0.7 0.6 0.8 0.6 0.8]
|
|
|
|
mean value: 0.77
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.8 0.66666667 1. 0.57142857 0.66666667 0.4
|
|
0.42857143 0.71428571 0.5 0.6 ]
|
|
|
|
mean value: 0.6347619047619047
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 39
|
|
|
|
mean value: 39.0
|
|
|
|
key: FP
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: FN
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: TP
|
|
value: 38
|
|
|
|
mean value: 38.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.3
|
|
|
|
Accuracy on Blind test: 0.7
|
|
|
|
Running classifier: 10
|
|
Model_name: Extra Tree
|
|
Model func: ExtraTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00929761 0.0082314 0.00812483 0.0081141 0.00811553 0.00808263
|
|
0.00860763 0.00804114 0.00888228 0.00918865]
|
|
|
|
mean value: 0.00846858024597168
|
|
|
|
key: score_time
|
|
value: [0.00910568 0.00822258 0.00867009 0.00826836 0.00844622 0.00843167
|
|
0.00880265 0.00870919 0.00882745 0.00881433]
|
|
|
|
mean value: 0.008629822731018066
|
|
|
|
key: test_mcc
|
|
value: [ 0.6 0.6 0.40824829 0.5 0. 0.40824829
|
|
0.21821789 0.5 -0.40824829 0.40824829]
|
|
|
|
mean value: 0.3234714471163718
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.8 0.72727273 0.76923077 0.44444444 0.66666667
|
|
0.5 0.76923077 0.36363636 0.66666667]
|
|
|
|
mean value: 0.6507148407148406
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.8 0.8 0.66666667 0.625 0.5 0.75
|
|
0.66666667 0.625 0.33333333 0.75 ]
|
|
|
|
mean value: 0.6516666666666666
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.8 0.8 0.8 1. 0.4 0.6 0.4 1. 0.4 0.6]
|
|
|
|
mean value: 0.68
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.8 0.8 0.7 0.7 0.5 0.7 0.6 0.7 0.3 0.7]
|
|
|
|
mean value: 0.65
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.8 0.8 0.7 0.7 0.5 0.7 0.6 0.7 0.3 0.7]
|
|
|
|
mean value: 0.65
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.66666667 0.57142857 0.625 0.28571429 0.5
|
|
0.33333333 0.625 0.22222222 0.5 ]
|
|
|
|
mean value: 0.4996031746031746
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 31
|
|
|
|
mean value: 31.0
|
|
|
|
key: FP
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: FN
|
|
value: 19
|
|
|
|
mean value: 19.0
|
|
|
|
key: TP
|
|
value: 34
|
|
|
|
mean value: 34.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.02
|
|
|
|
Accuracy on Blind test: 0.55
|
|
|
|
Running classifier: 11
|
|
Model_name: Random Forest
|
|
Model func: RandomForestClassifier(n_estimators=1000, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(n_estimators=1000, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [1.08314943 1.05717731 1.05498576 1.04640746 1.04371905 1.05806494
|
|
1.05173302 1.04391503 1.03815794 1.03380728]
|
|
|
|
mean value: 1.0511117219924926
|
|
|
|
key: score_time
|
|
value: [0.09315157 0.0880518 0.08978987 0.0893538 0.08839059 0.08848882
|
|
0.0899272 0.09106064 0.08935261 0.0950737 ]
|
|
|
|
mean value: 0.09026405811309815
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.65465367 1. 0.65465367 0.81649658 0.65465367
|
|
0.65465367 0.65465367 0.40824829 0.65465367]
|
|
|
|
mean value: 0.6969163476567177
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.88888889 0.83333333 1. 0.75 0.90909091 0.75
|
|
0.75 0.83333333 0.72727273 0.75 ]
|
|
|
|
mean value: 0.8191919191919192
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.71428571 1. 1. 0.83333333 1.
|
|
1. 0.71428571 0.66666667 1. ]
|
|
|
|
mean value: 0.892857142857143
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.8 1. 1. 0.6 1. 0.6 0.6 1. 0.8 0.6]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.8 1. 0.8 0.9 0.8 0.8 0.8 0.7 0.8]
|
|
|
|
mean value: 0.8300000000000001
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.8 1. 0.8 0.9 0.8 0.8 0.8 0.7 0.8]
|
|
|
|
mean value: 0.8300000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.8 0.71428571 1. 0.6 0.83333333 0.6
|
|
0.6 0.71428571 0.57142857 0.6 ]
|
|
|
|
mean value: 0.7033333333333334
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.6
|
|
|
|
Accuracy on Blind test: 0.82
|
|
|
|
Running classifier: 12
|
|
Model_name: Random Forest2
|
|
Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...age_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10,
|
|
oob_score=True, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.8008554 0.86556268 0.83195925 0.85484838 0.86451149 0.86783385
|
|
0.90637803 0.84007192 0.82203436 0.86065936]
|
|
|
|
mean value: 0.8514714717864991
|
|
|
|
key: score_time
|
|
value: [0.17614007 0.23751974 0.16117907 0.16920114 0.17847824 0.17201066
|
|
0.16586041 0.17395544 0.19984627 0.19024968]
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
|
|
mean value: 0.18244407176971436
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.65465367 0.81649658 0.65465367 0.81649658 0.81649658
|
|
0.81649658 0.40824829 0.40824829 0.65465367]
|
|
|
|
mean value: 0.6862940497690287
|
|
|
|
key: train_mcc
|
|
value: [1. 0.97801929 0.97801929 0.95555556 0.97801929 1.
|
|
0.97801929 0.97801929 1. 1. ]
|
|
|
|
mean value: 0.9845652024773812
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.83333333 0.88888889 0.75 0.90909091 0.88888889
|
|
0.88888889 0.72727273 0.72727273 0.75 ]
|
|
|
|
mean value: 0.8272727272727272
|
|
|
|
key: train_fscore
|
|
value: [1. 0.98901099 0.98901099 0.97777778 0.98901099 1.
|
|
0.98901099 0.98901099 1. 1. ]
|
|
|
|
mean value: 0.9922832722832723
|
|
|
|
key: test_precision
|
|
value: [0.83333333 0.71428571 1. 1. 0.83333333 1.
|
|
1. 0.66666667 0.66666667 1. ]
|
|
|
|
mean value: 0.8714285714285716
|
|
|
|
key: train_precision
|
|
value: [1. 0.97826087 0.97826087 0.97777778 0.97826087 1.
|
|
0.97826087 0.97826087 1. 1. ]
|
|
|
|
mean value: 0.9869082125603864
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 0.6 1. 0.8 0.8 0.8 0.8 0.6]
|
|
|
|
mean value: 0.8200000000000001
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 0.97777778 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9977777777777778
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.8 0.9 0.8 0.9 0.9 0.9 0.7 0.7 0.8]
|
|
|
|
mean value: 0.8300000000000001
|
|
|
|
key: train_accuracy
|
|
value: [1. 0.98888889 0.98888889 0.97777778 0.98888889 1.
|
|
0.98888889 0.98888889 1. 1. ]
|
|
|
|
mean value: 0.9922222222222222
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.8 0.9 0.8 0.9 0.9 0.9 0.7 0.7 0.8]
|
|
|
|
mean value: 0.8300000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [1. 0.98888889 0.98888889 0.97777778 0.98888889 1.
|
|
0.98888889 0.98888889 1. 1. ]
|
|
|
|
mean value: 0.9922222222222222
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.71428571 0.8 0.6 0.83333333 0.8
|
|
0.8 0.57142857 0.57142857 0.6 ]
|
|
|
|
mean value: 0.7123809523809523
|
|
|
|
key: train_jcc
|
|
value: [1. 0.97826087 0.97826087 0.95652174 0.97826087 1.
|
|
0.97826087 0.97826087 1. 1. ]
|
|
|
|
mean value: 0.9847826086956522
|
|
|
|
key: TN
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: FP
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 41
|
|
|
|
mean value: 41.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.6
|
|
|
|
Accuracy on Blind test: 0.82
|
|
|
|
Running classifier: 13
|
|
Model_name: XGBoost
|
|
Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...
|
|
interaction_constraints=None, learning_rate=None,
|
|
max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan,
|
|
monotone_constraints=None, n_estimators=100,
|
|
n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None,
|
|
reg_lambda=None, scale_pos_weight=None,
|
|
subsample=None, tree_method=None,
|
|
use_label_encoder=False,
|
|
validate_parameters=None, verbosity=0))])
|
|
|
|
key: fit_time
|
|
value: [0.03647876 0.03303075 0.03647852 0.03536677 0.03443265 0.03521061
|
|
0.03624892 0.03567147 0.03307509 0.03529716]
|
|
|
|
mean value: 0.03512907028198242
|
|
|
|
key: score_time
|
|
value: [0.0158205 0.01005316 0.06378174 0.00998211 0.01029038 0.01069117
|
|
0.00997925 0.00992107 0.01004457 0.0107832 ]
|
|
|
|
mean value: 0.01613471508026123
|
|
|
|
key: test_mcc
|
|
value: [1. 0.81649658 1. 1. 1. 0.6
|
|
0.81649658 0.6 0.5 0.81649658]
|
|
|
|
mean value: 0.8149489742783178
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.90909091 1. 1. 1. 0.8
|
|
0.88888889 0.8 0.76923077 0.88888889]
|
|
|
|
mean value: 0.9056099456099458
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 1. 1. 1. 0.8
|
|
1. 0.8 0.625 1. ]
|
|
|
|
mean value: 0.9058333333333334
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 1. 1. 0.8 0.8 0.8 1. 0.8]
|
|
|
|
mean value: 0.9200000000000002
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9 1. 1. 1. 0.8 0.9 0.8 0.7 0.9]
|
|
|
|
mean value: 0.9
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.9 1. 1. 1. 0.8 0.9 0.8 0.7 0.9]
|
|
|
|
mean value: 0.9
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.83333333 1. 1. 1. 0.66666667
|
|
0.8 0.66666667 0.625 0.8 ]
|
|
|
|
mean value: 0.8391666666666667
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 44
|
|
|
|
mean value: 44.0
|
|
|
|
key: FP
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: FN
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: TP
|
|
value: 46
|
|
|
|
mean value: 46.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.89
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 14
|
|
Model_name: LDA
|
|
Model func: LinearDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LinearDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.02231097 0.04178953 0.03549719 0.04157138 0.04162025 0.03488564
|
|
0.04136801 0.04148436 0.04149842 0.04407692]
|
|
|
|
mean value: 0.03861026763916016
|
|
|
|
key: score_time
|
|
value: [0.02140141 0.02215552 0.01681995 0.02330375 0.01159453 0.02025843
|
|
0.01248741 0.01387024 0.02162814 0.02118063]
|
|
|
|
mean value: 0.018470001220703126
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.81649658 0.6 0.40824829 0.40824829 0.21821789
|
|
0.65465367 0.5 0.40824829 0. ]
|
|
|
|
mean value: 0.4830609594191011
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.90909091 0.8 0.72727273 0.72727273 0.66666667
|
|
0.83333333 0.76923077 0.72727273 0.61538462]
|
|
|
|
mean value: 0.7684615384615385
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.83333333 0.83333333 0.8 0.66666667 0.66666667 0.57142857
|
|
0.71428571 0.625 0.66666667 0.5 ]
|
|
|
|
mean value: 0.6877380952380954
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 0.8 0.8 0.8 1. 1. 0.8 0.8]
|
|
|
|
mean value: 0.8800000000000001
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.9 0.8 0.7 0.7 0.6 0.8 0.7 0.7 0.5]
|
|
|
|
mean value: 0.73
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.9 0.8 0.7 0.7 0.6 0.8 0.7 0.7 0.5]
|
|
|
|
mean value: 0.73
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.83333333 0.66666667 0.57142857 0.57142857 0.5
|
|
0.71428571 0.625 0.57142857 0.44444444]
|
|
|
|
mean value: 0.6331349206349206
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 21
|
|
|
|
mean value: 21.0
|
|
|
|
key: TP
|
|
value: 44
|
|
|
|
mean value: 44.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.16
|
|
|
|
Accuracy on Blind test: 0.62
|
|
|
|
Running classifier: 15
|
|
Model_name: Multinomial
|
|
Model func: MultinomialNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MultinomialNB())])
|
|
|
|
key: fit_time
|
|
value: [0.01321387 0.01240396 0.00874496 0.0092957 0.00834417 0.00813174
|
|
0.00837374 0.00808525 0.00824094 0.00825596]
|
|
|
|
mean value: 0.009309029579162598
|
|
|
|
key: score_time
|
|
value: [0.01152873 0.01367998 0.00886726 0.00834537 0.00828481 0.00844979
|
|
0.00816917 0.00834799 0.00844622 0.00816035]
|
|
|
|
mean value: 0.009227967262268067
|
|
|
|
key: test_mcc
|
|
value: [ 0. 0.6 0.2 0.2 0.40824829 0.6
|
|
0. 0. -0.40824829 0.40824829]
|
|
|
|
mean value: 0.20082482904638627
|
|
|
|
key: train_mcc
|
|
value: [0.4260261 0.53665631 0.51571581 0.64444444 0.68888889 0.60238451
|
|
0.62360956 0.51111111 0.51161666 0.62237591]
|
|
|
|
mean value: 0.5682829320802611
|
|
|
|
key: test_fscore
|
|
value: [0.54545455 0.8 0.6 0.6 0.72727273 0.8
|
|
0.44444444 0.44444444 0.36363636 0.66666667]
|
|
|
|
mean value: 0.599191919191919
|
|
|
|
key: train_fscore
|
|
value: [0.72916667 0.75294118 0.73809524 0.82222222 0.84444444 0.80851064
|
|
0.8045977 0.75555556 0.76086957 0.80898876]
|
|
|
|
mean value: 0.7825391972164348
|
|
|
|
key: test_precision
|
|
value: [0.5 0.8 0.6 0.6 0.66666667 0.8
|
|
0.5 0.5 0.33333333 0.75 ]
|
|
|
|
mean value: 0.605
|
|
|
|
key: train_precision
|
|
value: [0.68627451 0.8 0.79487179 0.82222222 0.84444444 0.7755102
|
|
0.83333333 0.75555556 0.74468085 0.81818182]
|
|
|
|
mean value: 0.7875074733558554
|
|
|
|
key: test_recall
|
|
value: [0.6 0.8 0.6 0.6 0.8 0.8 0.4 0.4 0.4 0.6]
|
|
|
|
mean value: 0.6
|
|
|
|
key: train_recall
|
|
value: [0.77777778 0.71111111 0.68888889 0.82222222 0.84444444 0.84444444
|
|
0.77777778 0.75555556 0.77777778 0.8 ]
|
|
|
|
mean value: 0.78
|
|
|
|
key: test_accuracy
|
|
value: [0.5 0.8 0.6 0.6 0.7 0.8 0.5 0.5 0.3 0.7]
|
|
|
|
mean value: 0.6
|
|
|
|
key: train_accuracy
|
|
value: [0.71111111 0.76666667 0.75555556 0.82222222 0.84444444 0.8
|
|
0.81111111 0.75555556 0.75555556 0.81111111]
|
|
|
|
mean value: 0.7833333333333333
|
|
|
|
key: test_roc_auc
|
|
value: [0.5 0.8 0.6 0.6 0.7 0.8 0.5 0.5 0.3 0.7]
|
|
|
|
mean value: 0.6
|
|
|
|
key: train_roc_auc
|
|
value: [0.71111111 0.76666667 0.75555556 0.82222222 0.84444444 0.8
|
|
0.81111111 0.75555556 0.75555556 0.81111111]
|
|
|
|
mean value: 0.7833333333333332
|
|
|
|
key: test_jcc
|
|
value: [0.375 0.66666667 0.42857143 0.42857143 0.57142857 0.66666667
|
|
0.28571429 0.28571429 0.22222222 0.5 ]
|
|
|
|
mean value: 0.44305555555555554
|
|
|
|
key: train_jcc
|
|
value: [0.57377049 0.60377358 0.58490566 0.69811321 0.73076923 0.67857143
|
|
0.67307692 0.60714286 0.61403509 0.67924528]
|
|
|
|
mean value: 0.6443403754932071
|
|
|
|
key: TN
|
|
value: 30
|
|
|
|
mean value: 30.0
|
|
|
|
key: FP
|
|
value: 20
|
|
|
|
mean value: 20.0
|
|
|
|
key: FN
|
|
value: 20
|
|
|
|
mean value: 20.0
|
|
|
|
key: TP
|
|
value: 30
|
|
|
|
mean value: 30.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.26
|
|
|
|
Accuracy on Blind test: 0.35
|
|
|
|
Running classifier: 16
|
|
Model_name: Passive Aggresive
|
|
Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
PassiveAggressiveClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01003146 0.01199222 0.01306844 0.01315403 0.01348281 0.01319456
|
|
0.01266003 0.0128665 0.01410055 0.01346374]
|
|
|
|
mean value: 0.012801432609558105
|
|
|
|
key: score_time
|
|
value: [0.00822568 0.01112962 0.01109433 0.0114522 0.01152349 0.01129174
|
|
0.0113523 0.0113461 0.01147819 0.01160097]
|
|
|
|
mean value: 0.011049461364746094
|
|
|
|
key: test_mcc
|
|
value: [1. 0.33333333 1. 0. 0.6 0.81649658
|
|
0.40824829 0.81649658 0.40824829 1. ]
|
|
|
|
mean value: 0.6382823076116511
|
|
|
|
key: train_mcc
|
|
value: [1. 0.51730613 0.97801929 0.97801929 1. 0.95650071
|
|
0.89442719 0.87447463 1. 1. ]
|
|
|
|
mean value: 0.9198747257089167
|
|
|
|
key: test_fscore
|
|
value: [1. 0.33333333 1. 0.54545455 0.8 0.90909091
|
|
0.72727273 0.88888889 0.72727273 1. ]
|
|
|
|
mean value: 0.7931313131313131
|
|
|
|
key: train_fscore
|
|
value: [1. 0.59375 0.98876404 0.98876404 1. 0.97826087
|
|
0.94736842 0.92857143 1. 1. ]
|
|
|
|
mean value: 0.9425478809076917
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 0.5 0.8 0.83333333
|
|
0.66666667 1. 0.66666667 1. ]
|
|
|
|
mean value: 0.8466666666666667
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 0.95744681
|
|
0.9 1. 1. 1. ]
|
|
|
|
mean value: 0.9857446808510637
|
|
|
|
key: test_recall
|
|
value: [1. 0.2 1. 0.6 0.8 1. 0.8 0.8 0.8 1. ]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_recall
|
|
value: [1. 0.42222222 0.97777778 0.97777778 1. 1.
|
|
1. 0.86666667 1. 1. ]
|
|
|
|
mean value: 0.9244444444444444
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.6 1. 0.5 0.8 0.9 0.7 0.9 0.7 1. ]
|
|
|
|
mean value: 0.8100000000000002
|
|
|
|
key: train_accuracy
|
|
value: [1. 0.71111111 0.98888889 0.98888889 1. 0.97777778
|
|
0.94444444 0.93333333 1. 1. ]
|
|
|
|
mean value: 0.9544444444444444
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.6 1. 0.5 0.8 0.9 0.7 0.9 0.7 1. ]
|
|
|
|
mean value: 0.8100000000000002
|
|
|
|
key: train_roc_auc
|
|
value: [1. 0.71111111 0.98888889 0.98888889 1. 0.97777778
|
|
0.94444444 0.93333333 1. 1. ]
|
|
|
|
mean value: 0.9544444444444444
|
|
|
|
key: test_jcc
|
|
value: [1. 0.2 1. 0.375 0.66666667 0.83333333
|
|
0.57142857 0.8 0.57142857 1. ]
|
|
|
|
mean value: 0.7017857142857142
|
|
|
|
key: train_jcc
|
|
value: [1. 0.42222222 0.97777778 0.97777778 1. 0.95744681
|
|
0.9 0.86666667 1. 1. ]
|
|
|
|
mean value: 0.9101891252955083
|
|
|
|
key: TN
|
|
value: 41
|
|
|
|
mean value: 41.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: TP
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.28
|
|
|
|
Accuracy on Blind test: 0.7
|
|
|
|
Running classifier: 17
|
|
Model_name: Stochastic GDescent
|
|
Model func: SGDClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SGDClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00887799 0.01214647 0.01226735 0.01229191 0.01261067 0.01222086
|
|
0.01222086 0.01250696 0.01220679 0.01205516]
|
|
|
|
mean value: 0.011940503120422363
|
|
|
|
key: score_time
|
|
value: [0.00837612 0.0112083 0.0112288 0.0112474 0.01141977 0.01135349
|
|
0.01139736 0.01148057 0.0116353 0.01141691]
|
|
|
|
mean value: 0.011076402664184571
|
|
|
|
key: test_mcc
|
|
value: [0.6 0.81649658 1. 0. 0.6 0.40824829
|
|
0.6 0.6 0.40824829 0.6 ]
|
|
|
|
mean value: 0.5632993161855452
|
|
|
|
key: train_mcc
|
|
value: [0.95555556 0.95555556 0.93541435 1. 1. 0.95555556
|
|
0.97801929 1. 1. 0.93541435]
|
|
|
|
mean value: 0.9715514653897289
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.90909091 1. 0.54545455 0.8 0.72727273
|
|
0.8 0.8 0.66666667 0.8 ]
|
|
|
|
mean value: 0.7848484848484849
|
|
|
|
key: train_fscore
|
|
value: [0.97777778 0.97777778 0.96551724 1. 1. 0.97777778
|
|
0.98876404 1. 1. 0.96774194]
|
|
|
|
mean value: 0.9855356555140335
|
|
|
|
key: test_precision
|
|
value: [0.8 0.83333333 1. 0.5 0.8 0.66666667
|
|
0.8 0.8 0.75 0.8 ]
|
|
|
|
mean value: 0.775
|
|
|
|
key: train_precision
|
|
value: [0.97777778 0.97777778 1. 1. 1. 0.97777778
|
|
1. 1. 1. 0.9375 ]
|
|
|
|
mean value: 0.9870833333333333
|
|
|
|
key: test_recall
|
|
value: [0.8 1. 1. 0.6 0.8 0.8 0.8 0.8 0.6 0.8]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_recall
|
|
value: [0.97777778 0.97777778 0.93333333 1. 1. 0.97777778
|
|
0.97777778 1. 1. 1. ]
|
|
|
|
mean value: 0.9844444444444445
|
|
|
|
key: test_accuracy
|
|
value: [0.8 0.9 1. 0.5 0.8 0.7 0.8 0.8 0.7 0.8]
|
|
|
|
mean value: 0.78
|
|
|
|
key: train_accuracy
|
|
value: [0.97777778 0.97777778 0.96666667 1. 1. 0.97777778
|
|
0.98888889 1. 1. 0.96666667]
|
|
|
|
mean value: 0.9855555555555556
|
|
|
|
key: test_roc_auc
|
|
value: [0.8 0.9 1. 0.5 0.8 0.7 0.8 0.8 0.7 0.8]
|
|
|
|
mean value: 0.78
|
|
|
|
key: train_roc_auc
|
|
value: [0.97777778 0.97777778 0.96666667 1. 1. 0.97777778
|
|
0.98888889 1. 1. 0.96666667]
|
|
|
|
mean value: 0.9855555555555556
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.83333333 1. 0.375 0.66666667 0.57142857
|
|
0.66666667 0.66666667 0.5 0.66666667]
|
|
|
|
mean value: 0.6613095238095238
|
|
|
|
key: train_jcc
|
|
value: [0.95652174 0.95652174 0.93333333 1. 1. 0.95652174
|
|
0.97777778 1. 1. 0.9375 ]
|
|
|
|
mean value: 0.9718176328502416
|
|
|
|
key: TN
|
|
value: 38
|
|
|
|
mean value: 38.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: TP
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.04
|
|
|
|
Accuracy on Blind test: 0.52
|
|
|
|
Running classifier: 18
|
|
Model_name: AdaBoost Classifier
|
|
Model func: AdaBoostClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', AdaBoostClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.08591294 0.0870769 0.08603144 0.08604956 0.08540297 0.08636951
|
|
0.08703423 0.09059286 0.08867574 0.08594322]
|
|
|
|
mean value: 0.08690893650054932
|
|
|
|
key: score_time
|
|
value: [0.01451015 0.01454926 0.01424074 0.01429844 0.01511765 0.01439977
|
|
0.01509309 0.01519299 0.01466537 0.01518536]
|
|
|
|
mean value: 0.014725279808044434
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.81649658 1. 1. 1. 0.6
|
|
1. 0.81649658 0.5 0.81649658]
|
|
|
|
mean value: 0.8365986323710904
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.90909091 1. 1. 1. 0.8
|
|
1. 0.90909091 0.76923077 0.88888889]
|
|
|
|
mean value: 0.9185392385392387
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.83333333 0.83333333 1. 1. 1. 0.8
|
|
1. 0.83333333 0.625 1. ]
|
|
|
|
mean value: 0.8925000000000001
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 1. 1. 0.8 1. 1. 1. 0.8]
|
|
|
|
mean value: 0.9600000000000002
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.9 1. 1. 1. 0.8 1. 0.9 0.7 0.9]
|
|
|
|
mean value: 0.9099999999999999
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.9 1. 1. 1. 0.8 1. 0.9 0.7 0.9]
|
|
|
|
mean value: 0.9099999999999999
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.83333333 1. 1. 1. 0.66666667
|
|
1. 0.83333333 0.625 0.8 ]
|
|
|
|
mean value: 0.8591666666666666
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: FP
|
|
value: 2
|
|
|
|
mean value: 2.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 48
|
|
|
|
mean value: 48.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.83
|
|
|
|
Accuracy on Blind test: 0.92
|
|
|
|
Running classifier: 19
|
|
Model_name: Bagging Classifier
|
|
Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
BaggingClassifier(n_jobs=10, oob_score=True,
|
|
random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02613139 0.03143096 0.02982998 0.02666664 0.03157735 0.03069139
|
|
0.02667904 0.02771163 0.0300796 0.02920628]
|
|
|
|
mean value: 0.02900042533874512
|
|
|
|
key: score_time
|
|
value: [0.01907611 0.02446222 0.01595473 0.02101159 0.01648688 0.02567482
|
|
0.01766539 0.01646376 0.02223778 0.02316689]
|
|
|
|
mean value: 0.020220017433166503
|
|
|
|
key: test_mcc
|
|
value: [1. 0.81649658 1. 0.81649658 1. 0.81649658
|
|
0.81649658 1. 0.6 0.81649658]
|
|
|
|
mean value: 0.8682482904638629
|
|
|
|
key: train_mcc
|
|
value: [0.97801929 0.97801929 0.97801929 1. 1. 1.
|
|
0.97801929 1. 1. 1. ]
|
|
|
|
mean value: 0.9912077175374605
|
|
|
|
key: test_fscore
|
|
value: [1. 0.90909091 1. 0.90909091 1. 0.88888889
|
|
0.88888889 1. 0.8 0.88888889]
|
|
|
|
mean value: 0.9284848484848485
|
|
|
|
key: train_fscore
|
|
value: [0.98876404 0.98876404 0.98876404 1. 1. 1.
|
|
0.98901099 1. 1. 1. ]
|
|
|
|
mean value: 0.9955303123842449
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 1. 0.83333333 1. 1.
|
|
1. 1. 0.8 1. ]
|
|
|
|
mean value: 0.9466666666666667
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
0.97826087 1. 1. 1. ]
|
|
|
|
mean value: 0.9978260869565216
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 1. 1. 0.8 0.8 1. 0.8 0.8]
|
|
|
|
mean value: 0.9200000000000002
|
|
|
|
key: train_recall
|
|
value: [0.97777778 0.97777778 0.97777778 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9933333333333334
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9 1. 0.9 1. 0.9 0.9 1. 0.8 0.9]
|
|
|
|
mean value: 0.93
|
|
|
|
key: train_accuracy
|
|
value: [0.98888889 0.98888889 0.98888889 1. 1. 1.
|
|
0.98888889 1. 1. 1. ]
|
|
|
|
mean value: 0.9955555555555555
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.9 1. 0.9 1. 0.9 0.9 1. 0.8 0.9]
|
|
|
|
mean value: 0.93
|
|
|
|
key: train_roc_auc
|
|
value: [0.98888889 0.98888889 0.98888889 1. 1. 1.
|
|
0.98888889 1. 1. 1. ]
|
|
|
|
mean value: 0.9955555555555555
|
|
|
|
key: test_jcc
|
|
value: [1. 0.83333333 1. 0.83333333 1. 0.8
|
|
0.8 1. 0.66666667 0.8 ]
|
|
|
|
mean value: 0.8733333333333334
|
|
|
|
key: train_jcc
|
|
value: [0.97777778 0.97777778 0.97777778 1. 1. 1.
|
|
0.97826087 1. 1. 1. ]
|
|
|
|
mean value: 0.991159420289855
|
|
|
|
key: TN
|
|
value: 47
|
|
|
|
mean value: 47.0
|
|
|
|
key: FP
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: FN
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: TP
|
|
value: 46
|
|
|
|
mean value: 46.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.89
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 20
|
|
Model_name: Gaussian Process
|
|
Model func: GaussianProcessClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianProcessClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01337504 0.01569223 0.01567912 0.01595926 0.01574159 0.01597762
|
|
0.01579881 0.01569128 0.01606631 0.01576662]
|
|
|
|
mean value: 0.015574789047241211
|
|
|
|
key: score_time
|
|
value: [0.0116837 0.01153421 0.01165748 0.01159096 0.01157904 0.01181698
|
|
0.0116744 0.01153874 0.0116055 0.01170111]
|
|
|
|
mean value: 0.011638212203979491
|
|
|
|
key: test_mcc
|
|
value: [ 0.6 0.40824829 1. 0.40824829 0.40824829 0.
|
|
0.21821789 0.65465367 -0.21821789 0.40824829]
|
|
|
|
mean value: 0.3887646832563429
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.66666667 1. 0.72727273 0.72727273 0.44444444
|
|
0.66666667 0.83333333 0.5 0.66666667]
|
|
|
|
mean value: 0.7032323232323232
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.8 0.75 1. 0.66666667 0.66666667 0.5
|
|
0.57142857 0.71428571 0.42857143 0.75 ]
|
|
|
|
mean value: 0.6847619047619048
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.8 0.6 1. 0.8 0.8 0.4 0.8 1. 0.6 0.6]
|
|
|
|
mean value: 0.74
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.8 0.7 1. 0.7 0.7 0.5 0.6 0.8 0.4 0.7]
|
|
|
|
mean value: 0.6900000000000001
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.8 0.7 1. 0.7 0.7 0.5 0.6 0.8 0.4 0.7]
|
|
|
|
mean value: 0.6900000000000002
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.5 1. 0.57142857 0.57142857 0.28571429
|
|
0.5 0.71428571 0.33333333 0.5 ]
|
|
|
|
mean value: 0.5642857142857143
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 32
|
|
|
|
mean value: 32.0
|
|
|
|
key: FP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: FN
|
|
value: 18
|
|
|
|
mean value: 18.0
|
|
|
|
key: TP
|
|
value: 37
|
|
|
|
mean value: 37.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.15
|
|
|
|
Accuracy on Blind test: 0.6
|
|
|
|
Running classifier: 21
|
|
Model_name: Gradient Boosting
|
|
Model func: GradientBoostingClassifier(random_state=42)
|
|
|
|
Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GradientBoostingClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.22175002 0.20551085 0.21514559 0.19258976 0.19375014 0.21860838
|
|
0.19352555 0.22049546 0.18577838 0.19826937]
|
|
|
|
mean value: 0.20454235076904298
|
|
|
|
key: score_time
|
|
value: [0.00891447 0.00950909 0.00972724 0.00901461 0.00917888 0.00884056
|
|
0.00981069 0.00889659 0.01069164 0.00902009]
|
|
|
|
mean value: 0.009360384941101075
|
|
|
|
key: test_mcc
|
|
value: [1. 0.81649658 1. 0.40824829 0.81649658 0.6
|
|
0.81649658 0.81649658 0.40824829 0.65465367]
|
|
|
|
mean value: 0.7337136575346608
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.90909091 1. 0.66666667 0.88888889 0.8
|
|
0.88888889 0.88888889 0.72727273 0.75 ]
|
|
|
|
mean value: 0.8519696969696969
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 1. 0.75 1. 0.8
|
|
1. 1. 0.66666667 1. ]
|
|
|
|
mean value: 0.9049999999999999
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 0.6 0.8 0.8 0.8 0.8 0.8 0.6]
|
|
|
|
mean value: 0.8200000000000001
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9 1. 0.7 0.9 0.8 0.9 0.9 0.7 0.8]
|
|
|
|
mean value: 0.86
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.9 1. 0.7 0.9 0.8 0.9 0.9 0.7 0.8]
|
|
|
|
mean value: 0.86
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.83333333 1. 0.5 0.8 0.66666667
|
|
0.8 0.8 0.57142857 0.6 ]
|
|
|
|
mean value: 0.7571428571428571
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 45
|
|
|
|
mean value: 45.0
|
|
|
|
key: FP
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: FN
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: TP
|
|
value: 41
|
|
|
|
mean value: 41.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.84
|
|
|
|
Accuracy on Blind test: 0.92
|
|
|
|
Running classifier: 22
|
|
Model_name: QDA
|
|
Model func: QuadraticDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', QuadraticDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.01039934 0.0146544 0.01419163 0.01512098 0.01400018 0.01431561
|
|
0.01405144 0.01417041 0.01411915 0.01453304]
|
|
|
|
mean value: 0.01395561695098877
|
|
|
|
key: score_time
|
|
value: [0.01178169 0.01186323 0.01167774 0.01193404 0.01269293 0.01163626
|
|
0.014534 0.01400065 0.01272821 0.01334691]
|
|
|
|
mean value: 0.012619566917419434
|
|
|
|
key: test_mcc
|
|
value: [0.65465367 0.6 0.81649658 0.6 0.81649658 0.40824829
|
|
0.40824829 0.65465367 0.6 0.5 ]
|
|
|
|
mean value: 0.6058797084199132
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.83333333 0.8 0.90909091 0.8 0.90909091 0.72727273
|
|
0.72727273 0.83333333 0.8 0.76923077]
|
|
|
|
mean value: 0.8108624708624708
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.71428571 0.8 0.83333333 0.8 0.83333333 0.66666667
|
|
0.66666667 0.71428571 0.8 0.625 ]
|
|
|
|
mean value: 0.7453571428571428
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.8 1. 0.8 1. 0.8 0.8 1. 0.8 1. ]
|
|
|
|
mean value: 0.9
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.8 0.8 0.9 0.8 0.9 0.7 0.7 0.8 0.8 0.7]
|
|
|
|
mean value: 0.79
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.8 0.8 0.9 0.8 0.9 0.7 0.7 0.8 0.8 0.7]
|
|
|
|
mean value: 0.79
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.71428571 0.66666667 0.83333333 0.66666667 0.83333333 0.57142857
|
|
0.57142857 0.71428571 0.66666667 0.625 ]
|
|
|
|
mean value: 0.6863095238095238
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 34
|
|
|
|
mean value: 34.0
|
|
|
|
key: FP
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: FN
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: TP
|
|
value: 45
|
|
|
|
mean value: 45.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.22
|
|
|
|
Accuracy on Blind test: 0.68
|
|
|
|
Running classifier: 23
|
|
Model_name: Ridge Classifier
|
|
Model func: RidgeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.0339694 0.0281744 0.02761769 0.02563477 0.03163433 0.03103805
|
|
0.03008103 0.02972746 0.02814841 0.02832222]
|
|
|
|
mean value: 0.029434776306152342
|
|
|
|
key: score_time
|
|
value: [0.01380706 0.02080441 0.02085018 0.02093291 0.02080822 0.02095509
|
|
0.02182198 0.02258253 0.02093005 0.020823 ]
|
|
|
|
mean value: 0.02043154239654541
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.81649658 1. 0.5 0.81649658 0.6
|
|
0.6 0.65465367 0.21821789 0.81649658]
|
|
|
|
mean value: 0.6838857884654874
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 0.97801929 1. 1. 1.
|
|
0.97801929 1. 1. 1. ]
|
|
|
|
mean value: 0.9956038587687303
|
|
|
|
key: test_fscore
|
|
value: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:206: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
smnc_CV['Data_source'] = 'CV'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:207: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
smnc_CV['Resampling'] = rs_smnc
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:212: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
smnc_BT['Data_source'] = 'BT'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:213: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
smnc_BT['Resampling'] = rs_smnc
|
|
[0.90909091 0.90909091 1. 0.76923077 0.90909091 0.8
|
|
0.8 0.83333333 0.66666667 0.88888889]
|
|
|
|
mean value: 0.8485392385392385
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 0.98876404 1. 1. 1.
|
|
0.98876404 1. 1. 1. ]
|
|
|
|
mean value: 0.997752808988764
|
|
|
|
key: test_precision
|
|
value: [0.83333333 0.83333333 1. 0.625 0.83333333 0.8
|
|
0.8 0.71428571 0.57142857 1. ]
|
|
|
|
mean value: 0.8010714285714287
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 1. 1. 0.8 0.8 1. 0.8 0.8]
|
|
|
|
mean value: 0.9200000000000002
|
|
|
|
key: train_recall
|
|
value: [1. 1. 0.97777778 1. 1. 1.
|
|
0.97777778 1. 1. 1. ]
|
|
|
|
mean value: 0.9955555555555555
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.9 1. 0.7 0.9 0.8 0.8 0.8 0.6 0.9]
|
|
|
|
mean value: 0.8300000000000001
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 0.98888889 1. 1. 1.
|
|
0.98888889 1. 1. 1. ]
|
|
|
|
mean value: 0.9977777777777778
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.9 1. 0.7 0.9 0.8 0.8 0.8 0.6 0.9]
|
|
|
|
mean value: 0.8300000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 0.98888889 1. 1. 1.
|
|
0.98888889 1. 1. 1. ]
|
|
|
|
mean value: 0.9977777777777778
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.83333333 1. 0.625 0.83333333 0.66666667
|
|
0.66666667 0.71428571 0.5 0.8 ]
|
|
|
|
mean value: 0.7472619047619047
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 0.97777778 1. 1. 1.
|
|
0.97777778 1. 1. 1. ]
|
|
|
|
mean value: 0.9955555555555555
|
|
|
|
key: TN
|
|
value: 37
|
|
|
|
mean value: 37.0
|
|
|
|
key: FP
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: FN
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: TP
|
|
value: 46
|
|
|
|
mean value: 46.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.16
|
|
|
|
Accuracy on Blind test: 0.62
|
|
|
|
Running classifier: 24
|
|
Model_name: Ridge ClassifierCV
|
|
Model func: RidgeClassifierCV(cv=10)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifierCV(cv=10))])
|
|
|
|
key: fit_time
|
|
value: [0.1647613 0.21787906 0.20268655 0.18552685 0.1953547 0.18074489
|
|
0.18419719 0.18565655 0.18220353 0.20094514]
|
|
|
|
mean value: 0.18999557495117186
|
|
|
|
key: score_time
|
|
value: [0.02277732 0.02263975 0.02119422 0.02119184 0.02137756 0.02144146
|
|
0.02110291 0.02121115 0.01738858 0.02114844]
|
|
|
|
mean value: 0.021147322654724122
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.65465367 0.81649658 0.5 1. 0.40824829
|
|
0.81649658 0.65465367 0.21821789 0.81649658]
|
|
|
|
mean value: 0.6701759845826715
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.83333333 0.90909091 0.76923077 1. 0.72727273
|
|
0.90909091 0.83333333 0.66666667 0.88888889]
|
|
|
|
mean value: 0.8445998445998445
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.83333333 0.71428571 0.83333333 0.625 1. 0.66666667
|
|
0.83333333 0.71428571 0.57142857 1. ]
|
|
|
|
mean value: 0.7791666666666667
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 1. 1. 0.8 1. 1. 0.8 0.8]
|
|
|
|
mean value: 0.9400000000000001
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.8 0.9 0.7 1. 0.7 0.9 0.8 0.6 0.9]
|
|
|
|
mean value: 0.8200000000000001
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.8 0.9 0.7 1. 0.7 0.9 0.8 0.6 0.9]
|
|
|
|
mean value: 0.8200000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.71428571 0.83333333 0.625 1. 0.57142857
|
|
0.83333333 0.71428571 0.5 0.8 ]
|
|
|
|
mean value: 0.7424999999999999
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 35
|
|
|
|
mean value: 35.0
|
|
|
|
key: FP
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: FN
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: TP
|
|
value: 47
|
|
|
|
mean value: 47.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.05
|
|
|
|
Accuracy on Blind test: 0.57
|
|
|
|
PASS: sorting df by score that is mapped onto the order I want
|
|
|
|
==============================================================
|
|
|
|
Running several classification models (n): 24
|
|
List of models:
|
|
('Logistic Regression', LogisticRegression(random_state=42))
|
|
('Logistic RegressionCV', LogisticRegressionCV(random_state=42))
|
|
('Gaussian NB', GaussianNB())
|
|
('Naive Bayes', BernoulliNB())
|
|
('K-Nearest Neighbors', KNeighborsClassifier())
|
|
('SVC', SVC(random_state=42))
|
|
('MLP', MLPClassifier(max_iter=500, random_state=42))
|
|
('Decision Tree', DecisionTreeClassifier(random_state=42))
|
|
('Extra Trees', ExtraTreesClassifier(random_state=42))
|
|
('Extra Tree', ExtraTreeClassifier(random_state=42))
|
|
('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42))
|
|
('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42))
|
|
('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0))
|
|
('LDA', LinearDiscriminantAnalysis())
|
|
('Multinomial', MultinomialNB())
|
|
('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42))
|
|
('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42))
|
|
('AdaBoost Classifier', AdaBoostClassifier(random_state=42))
|
|
('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))
|
|
('Gaussian Process', GaussianProcessClassifier(random_state=42))
|
|
('Gradient Boosting', GradientBoostingClassifier(random_state=42))
|
|
('QDA', QuadraticDiscriminantAnalysis())
|
|
('Ridge Classifier', RidgeClassifier(random_state=42))
|
|
('Ridge ClassifierCV', RidgeClassifierCV(cv=10))
|
|
|
|
================================================================
|
|
|
|
|
|
Running classifier: 1
|
|
Model_name: Logistic Regression
|
|
Model func: LogisticRegression(random_state=42)
|
|
|
|
Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegression(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02255177 0.02571154 0.02445078 0.02460313 0.02436972 0.02525306
|
|
0.02503276 0.02916312 0.02374983 0.02672315]
|
|
|
|
mean value: 0.025160884857177733
|
|
|
|
key: score_time
|
|
value: [0.01158285 0.01172376 0.01152539 0.01155019 0.01155806 0.01169276
|
|
0.0116291 0.01217937 0.01163387 0.01157951]
|
|
|
|
mean value: 0.01166548728942871
|
|
|
|
key: test_mcc
|
|
value: [1. 0.6 1. 0.65465367 0.5 0.81649658
|
|
0.6 0.40824829 0. 0.81649658]
|
|
|
|
mean value: 0.6395895123027293
|
|
|
|
key: train_mcc
|
|
value: [0.97801929 0.93356387 0.93356387 0.95650071 0.91201231 0.91111111
|
|
0.91201231 0.93356387 0.95555556 0.93541435]
|
|
|
|
mean value: 0.936131725451825
|
|
|
|
key: test_fscore
|
|
value: [1. 0.8 1. 0.83333333 0.76923077 0.88888889
|
|
0.8 0.72727273 0.54545455 0.88888889]
|
|
|
|
mean value: 0.8253069153069154
|
|
|
|
key: train_fscore
|
|
value: [0.98901099 0.96703297 0.96703297 0.97826087 0.95652174 0.95555556
|
|
0.95652174 0.96703297 0.97777778 0.96774194]
|
|
|
|
mean value: 0.9682489506753182
|
|
|
|
key: test_precision
|
|
value: [1. 0.8 1. 0.71428571 0.625 1.
|
|
0.8 0.66666667 0.5 1. ]
|
|
|
|
mean value: 0.8105952380952381
|
|
|
|
key: train_precision
|
|
value: [0.97826087 0.95652174 0.95652174 0.95744681 0.93617021 0.95555556
|
|
0.93617021 0.95652174 0.97777778 0.9375 ]
|
|
|
|
mean value: 0.9548446654332409
|
|
|
|
key: test_recall
|
|
value: [1. 0.8 1. 1. 1. 0.8 0.8 0.8 0.6 0.8]
|
|
|
|
mean value: 0.86
|
|
|
|
key: train_recall
|
|
value: [1. 0.97777778 0.97777778 1. 0.97777778 0.95555556
|
|
0.97777778 0.97777778 0.97777778 1. ]
|
|
|
|
mean value: 0.9822222222222223
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.8 1. 0.8 0.7 0.9 0.8 0.7 0.5 0.9]
|
|
|
|
mean value: 0.8099999999999999
|
|
|
|
key: train_accuracy
|
|
value: [0.98888889 0.96666667 0.96666667 0.97777778 0.95555556 0.95555556
|
|
0.95555556 0.96666667 0.97777778 0.96666667]
|
|
|
|
mean value: 0.9677777777777778
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.8 1. 0.8 0.7 0.9 0.8 0.7 0.5 0.9]
|
|
|
|
mean value: 0.8099999999999999
|
|
|
|
key: train_roc_auc
|
|
value: [0.98888889 0.96666667 0.96666667 0.97777778 0.95555556 0.95555556
|
|
0.95555556 0.96666667 0.97777778 0.96666667]
|
|
|
|
mean value: 0.9677777777777778
|
|
|
|
key: test_jcc
|
|
value: [1. 0.66666667 1. 0.71428571 0.625 0.8
|
|
0.66666667 0.57142857 0.375 0.8 ]
|
|
|
|
mean value: 0.7219047619047619
|
|
|
|
key: train_jcc
|
|
value: [0.97826087 0.93617021 0.93617021 0.95744681 0.91666667 0.91489362
|
|
0.91666667 0.93617021 0.95652174 0.9375 ]
|
|
|
|
mean value: 0.9386467005858773
|
|
|
|
key: TN
|
|
value: 38
|
|
|
|
mean value: 38.0
|
|
|
|
key: FP
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: FN
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: TP
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.37
|
|
|
|
Accuracy on Blind test: 0.72
|
|
|
|
Running classifier: 2
|
|
Model_name: Logistic RegressionCV
|
|
Model func: LogisticRegressionCV(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegressionCV(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.54653025 0.52189708 0.73818922 0.56501412 0.52099514 0.58223414
|
|
0.6111896 0.52498031 0.51234984 0.58435845]
|
|
|
|
mean value: 0.5707738161087036
|
|
|
|
key: score_time
|
|
value: [0.01446366 0.01307082 0.01306009 0.0130105 0.01183724 0.01319456
|
|
0.01186347 0.01306581 0.01307225 0.01450109]
|
|
|
|
mean value: 0.013113951683044434
|
|
|
|
key: test_mcc
|
|
value: [1. 0.6 1. 0.5 0.65465367 0.81649658
|
|
0.81649658 0.40824829 0.5 0.81649658]
|
|
|
|
mean value: 0.7112391703955019
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.97801929 1. 1. ]
|
|
|
|
mean value: 0.9978019293843652
|
|
|
|
key: test_fscore
|
|
value: [1. 0.8 1. 0.76923077 0.83333333 0.88888889
|
|
0.88888889 0.72727273 0.76923077 0.88888889]
|
|
|
|
mean value: 0.8565734265734266
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.98901099 1. 1. ]
|
|
|
|
mean value: 0.9989010989010989
|
|
|
|
key: test_precision
|
|
value: [1. 0.8 1. 0.625 0.71428571 1.
|
|
1. 0.66666667 0.625 1. ]
|
|
|
|
mean value: 0.843095238095238
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.97826087 1. 1. ]
|
|
|
|
mean value: 0.9978260869565216
|
|
|
|
key: test_recall
|
|
value: [1. 0.8 1. 1. 1. 0.8 0.8 0.8 1. 0.8]
|
|
|
|
mean value: 0.9
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.8 1. 0.7 0.8 0.9 0.9 0.7 0.7 0.9]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.98888889 1. 1. ]
|
|
|
|
mean value: 0.9988888888888889
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.8 1. 0.7 0.8 0.9 0.9 0.7 0.7 0.9]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.98888889 1. 1. ]
|
|
|
|
mean value: 0.9988888888888889
|
|
|
|
key: test_jcc
|
|
value: [1. 0.66666667 1. 0.625 0.71428571 0.8
|
|
0.8 0.57142857 0.625 0.8 ]
|
|
|
|
mean value: 0.7602380952380952
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.97826087 1. 1. ]
|
|
|
|
mean value: 0.9978260869565216
|
|
|
|
key: TN
|
|
value: 39
|
|
|
|
mean value: 39.0
|
|
|
|
key: FP
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: FN
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: TP
|
|
value: 45
|
|
|
|
mean value: 45.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.25
|
|
|
|
Accuracy on Blind test: 0.68
|
|
|
|
Running classifier: 3
|
|
Model_name: Gaussian NB
|
|
Model func: GaussianNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianNB())])
|
|
|
|
key: fit_time
|
|
value: [0.01176167 0.01163745 0.00861454 0.0102284 0.0082593 0.0084765
|
|
0.00813198 0.00795722 0.00810385 0.00791645]
|
|
|
|
mean value: 0.009108734130859376
|
|
|
|
key: score_time
|
|
value: [0.01180577 0.00988865 0.00878239 0.00933552 0.00824714 0.00900102
|
|
0.00843334 0.00827384 0.00830865 0.00864124]
|
|
|
|
mean value: 0.009071755409240722
|
|
|
|
key: test_mcc
|
|
value: [ 0. 0.40824829 0.33333333 0.21821789 0.33333333 -0.21821789
|
|
0.21821789 0. 0. 0. ]
|
|
|
|
mean value: 0.1293132847366522
|
|
|
|
key: train_mcc
|
|
value: [0.41781451 0.57601843 0.47133199 0.70004007 0.50917508 0.43808583
|
|
0.57906602 0.52094589 0.55610507 0.51854497]
|
|
|
|
mean value: 0.528712785434277
|
|
|
|
key: test_fscore
|
|
value: [0.54545455 0.72727273 0.71428571 0.66666667 0.71428571 0.5
|
|
0.66666667 0.61538462 0.61538462 0.61538462]
|
|
|
|
mean value: 0.638078588078588
|
|
|
|
key: train_fscore
|
|
value: [0.73786408 0.80373832 0.76363636 0.85714286 0.77777778 0.74509804
|
|
0.79569892 0.78181818 0.7826087 0.78095238]
|
|
|
|
mean value: 0.7826335616353517
|
|
|
|
key: test_precision
|
|
value: [0.5 0.66666667 0.55555556 0.57142857 0.55555556 0.42857143
|
|
0.57142857 0.5 0.5 0.5 ]
|
|
|
|
mean value: 0.5349206349206349
|
|
|
|
key: train_precision
|
|
value: [0.65517241 0.69354839 0.64615385 0.79245283 0.66666667 0.66666667
|
|
0.77083333 0.66153846 0.76595745 0.68333333]
|
|
|
|
mean value: 0.7002323385579375
|
|
|
|
key: test_recall
|
|
value: [0.6 0.8 1. 0.8 1. 0.6 0.8 0.8 0.8 0.8]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_recall
|
|
value: [0.84444444 0.95555556 0.93333333 0.93333333 0.93333333 0.84444444
|
|
0.82222222 0.95555556 0.8 0.91111111]
|
|
|
|
mean value: 0.8933333333333333
|
|
|
|
key: test_accuracy
|
|
value: [0.5 0.7 0.6 0.6 0.6 0.4 0.6 0.5 0.5 0.5]
|
|
|
|
mean value: 0.55
|
|
|
|
key: train_accuracy
|
|
value: [0.7 0.76666667 0.71111111 0.84444444 0.73333333 0.71111111
|
|
0.78888889 0.73333333 0.77777778 0.74444444]
|
|
|
|
mean value: 0.7511111111111111
|
|
|
|
key: test_roc_auc
|
|
value: [0.5 0.7 0.6 0.6 0.6 0.4 0.6 0.5 0.5 0.5]
|
|
|
|
mean value: 0.55
|
|
|
|
key: train_roc_auc
|
|
value: [0.7 0.76666667 0.71111111 0.84444444 0.73333333 0.71111111
|
|
0.78888889 0.73333333 0.77777778 0.74444444]
|
|
|
|
mean value: 0.7511111111111111
|
|
|
|
key: test_jcc
|
|
value: [0.375 0.57142857 0.55555556 0.5 0.55555556 0.33333333
|
|
0.5 0.44444444 0.44444444 0.44444444]
|
|
|
|
mean value: 0.47242063492063496
|
|
|
|
key: train_jcc
|
|
value: [0.58461538 0.671875 0.61764706 0.75 0.63636364 0.59375
|
|
0.66071429 0.64179104 0.64285714 0.640625 ]
|
|
|
|
mean value: 0.6440238553150099
|
|
|
|
key: TN
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 35
|
|
|
|
mean value: 35.0
|
|
|
|
key: TP
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.39
|
|
|
|
Accuracy on Blind test: 0.65
|
|
|
|
Running classifier: 4
|
|
Model_name: Naive Bayes
|
|
Model func: BernoulliNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', BernoulliNB())])
|
|
|
|
key: fit_time
|
|
value: [0.01204419 0.00879407 0.00843763 0.00823641 0.00820732 0.00811648
|
|
0.00903416 0.0081718 0.00817847 0.00820756]
|
|
|
|
mean value: 0.008742809295654297
|
|
|
|
key: score_time
|
|
value: [0.01041579 0.00831723 0.0082469 0.00830102 0.00827026 0.00827599
|
|
0.00908613 0.00831842 0.00828457 0.00827765]
|
|
|
|
mean value: 0.008579397201538086
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0. 0.81649658 0.40824829 0.40824829 0.5
|
|
0. 0. 0. 0.21821789]
|
|
|
|
mean value: 0.31677076330191706
|
|
|
|
key: train_mcc
|
|
value: [0.64700558 0.62609903 0.51571581 0.57792049 0.44992127 0.60238451
|
|
0.60059347 0.60059347 0.69162666 0.55610507]
|
|
|
|
mean value: 0.5867965370349755
|
|
|
|
key: test_fscore
|
|
value: [0.88888889 0.54545455 0.88888889 0.72727273 0.72727273 0.57142857
|
|
0.44444444 0.54545455 0.44444444 0.5 ]
|
|
|
|
mean value: 0.6283549783549783
|
|
|
|
key: train_fscore
|
|
value: [0.81395349 0.82105263 0.77083333 0.78651685 0.69879518 0.79069767
|
|
0.79545455 0.80434783 0.8372093 0.7826087 ]
|
|
|
|
mean value: 0.7901469531877712
|
|
|
|
key: test_precision
|
|
value: [1. 0.5 1. 0.66666667 0.66666667 1.
|
|
0.5 0.5 0.5 0.66666667]
|
|
|
|
mean value: 0.7
|
|
|
|
key: train_precision
|
|
value: [0.85365854 0.78 0.7254902 0.79545455 0.76315789 0.82926829
|
|
0.81395349 0.78723404 0.87804878 0.76595745]
|
|
|
|
mean value: 0.7992223223759711
|
|
|
|
key: test_recall
|
|
value: [0.8 0.6 0.8 0.8 0.8 0.4 0.4 0.6 0.4 0.4]
|
|
|
|
mean value: 0.6000000000000001
|
|
|
|
key: train_recall
|
|
value: [0.77777778 0.86666667 0.82222222 0.77777778 0.64444444 0.75555556
|
|
0.77777778 0.82222222 0.8 0.8 ]
|
|
|
|
mean value: 0.7844444444444444
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.5 0.9 0.7 0.7 0.7 0.5 0.5 0.5 0.6]
|
|
|
|
mean value: 0.65
|
|
|
|
key: train_accuracy
|
|
value: [0.82222222 0.81111111 0.75555556 0.78888889 0.72222222 0.8
|
|
0.8 0.8 0.84444444 0.77777778]
|
|
|
|
mean value: 0.7922222222222222
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.5 0.9 0.7 0.7 0.7 0.5 0.5 0.5 0.6]
|
|
|
|
mean value: 0.65
|
|
|
|
key: train_roc_auc
|
|
value: [0.82222222 0.81111111 0.75555556 0.78888889 0.72222222 0.8
|
|
0.8 0.8 0.84444444 0.77777778]
|
|
|
|
mean value: 0.7922222222222223
|
|
|
|
key: test_jcc
|
|
value: [0.8 0.375 0.8 0.57142857 0.57142857 0.4
|
|
0.28571429 0.375 0.28571429 0.33333333]
|
|
|
|
mean value: 0.4797619047619047
|
|
|
|
key: train_jcc
|
|
value: [0.68627451 0.69642857 0.62711864 0.64814815 0.53703704 0.65384615
|
|
0.66037736 0.67272727 0.72 0.64285714]
|
|
|
|
mean value: 0.6544814838406611
|
|
|
|
key: TN
|
|
value: 35
|
|
|
|
mean value: 35.0
|
|
|
|
key: FP
|
|
value: 20
|
|
|
|
mean value: 20.0
|
|
|
|
key: FN
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: TP
|
|
value: 30
|
|
|
|
mean value: 30.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.06
|
|
|
|
Accuracy on Blind test: 0.5
|
|
|
|
Running classifier: 5
|
|
Model_name: K-Nearest Neighbors
|
|
Model func: KNeighborsClassifier()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', KNeighborsClassifier())])
|
|
|
|
key: fit_time
|
|
value: [0.0103817 0.00782585 0.00789905 0.0080235 0.00852895 0.00891089
|
|
0.00892091 0.00831962 0.00858092 0.00874972]
|
|
|
|
mean value: 0.008614110946655273
|
|
|
|
key: score_time
|
|
value: [0.00999832 0.00927591 0.01990318 0.01393294 0.01388645 0.01472855
|
|
0.00999117 0.00985885 0.00980926 0.01003838]
|
|
|
|
mean value: 0.012142300605773926
|
|
|
|
key: test_mcc
|
|
value: [ 0.40824829 0.21821789 0. 0.40824829 0.81649658 -0.2
|
|
0.21821789 0. 0. 0.40824829]
|
|
|
|
mean value: 0.22776772327912997
|
|
|
|
key: train_mcc
|
|
value: [0.53452248 0.53990552 0.43808583 0.51314236 0.56980288 0.60971232
|
|
0.58137767 0.49897013 0.60540551 0.50418417]
|
|
|
|
mean value: 0.5395108888868825
|
|
|
|
key: test_fscore
|
|
value: [0.72727273 0.5 0.61538462 0.72727273 0.90909091 0.4
|
|
0.66666667 0.61538462 0.61538462 0.72727273]
|
|
|
|
mean value: 0.6503729603729604
|
|
|
|
key: train_fscore
|
|
value: [0.77419355 0.78350515 0.74509804 0.76595745 0.8 0.81632653
|
|
0.8 0.76767677 0.8125 0.77227723]
|
|
|
|
mean value: 0.7837534715062253
|
|
|
|
key: test_precision
|
|
value: [0.66666667 0.66666667 0.5 0.66666667 0.83333333 0.4
|
|
0.57142857 0.5 0.5 0.66666667]
|
|
|
|
mean value: 0.5971428571428572
|
|
|
|
key: train_precision
|
|
value: [0.75 0.73076923 0.66666667 0.73469388 0.72727273 0.75471698
|
|
0.76 0.7037037 0.76470588 0.69642857]
|
|
|
|
mean value: 0.7288957640876937
|
|
|
|
key: test_recall
|
|
value: [0.8 0.4 0.8 0.8 1. 0.4 0.8 0.8 0.8 0.8]
|
|
|
|
mean value: 0.74
|
|
|
|
key: train_recall
|
|
value: [0.8 0.84444444 0.84444444 0.8 0.88888889 0.88888889
|
|
0.84444444 0.84444444 0.86666667 0.86666667]
|
|
|
|
mean value: 0.8488888888888889
|
|
|
|
key: test_accuracy
|
|
value: [0.7 0.6 0.5 0.7 0.9 0.4 0.6 0.5 0.5 0.7]
|
|
|
|
mean value: 0.6100000000000001
|
|
|
|
key: train_accuracy
|
|
value: [0.76666667 0.76666667 0.71111111 0.75555556 0.77777778 0.8
|
|
0.78888889 0.74444444 0.8 0.74444444]
|
|
|
|
mean value: 0.7655555555555555
|
|
|
|
key: test_roc_auc
|
|
value: [0.7 0.6 0.5 0.7 0.9 0.4 0.6 0.5 0.5 0.7]
|
|
|
|
mean value: 0.6100000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [0.76666667 0.76666667 0.71111111 0.75555556 0.77777778 0.8
|
|
0.78888889 0.74444444 0.8 0.74444444]
|
|
|
|
mean value: 0.7655555555555555
|
|
|
|
key: test_jcc
|
|
value: [0.57142857 0.33333333 0.44444444 0.57142857 0.83333333 0.25
|
|
0.5 0.44444444 0.44444444 0.57142857]
|
|
|
|
mean value: 0.49642857142857133
|
|
|
|
key: train_jcc
|
|
value: [0.63157895 0.6440678 0.59375 0.62068966 0.66666667 0.68965517
|
|
0.66666667 0.62295082 0.68421053 0.62903226]
|
|
|
|
mean value: 0.6449268508950567
|
|
|
|
key: TN
|
|
value: 24
|
|
|
|
mean value: 24.0
|
|
|
|
key: FP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: FN
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: TP
|
|
value: 37
|
|
|
|
mean value: 37.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.07
|
|
|
|
Accuracy on Blind test: 0.48
|
|
|
|
Running classifier: 6
|
|
Model_name: SVC
|
|
Model func: SVC(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SVC(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00865793 0.00888205 0.00870848 0.00853825 0.0088017 0.00992823
|
|
0.00926638 0.00858331 0.00977707 0.00882268]
|
|
|
|
mean value: 0.00899660587310791
|
|
|
|
key: score_time
|
|
value: [0.00861406 0.00844789 0.00833035 0.008533 0.00839972 0.00914431
|
|
0.00835848 0.00843406 0.00843239 0.00836515]
|
|
|
|
mean value: 0.008505940437316895
|
|
|
|
key: test_mcc
|
|
value: [0.65465367 0.40824829 0.6 0.6 0.81649658 0.21821789
|
|
0.40824829 0.21821789 0. 0.65465367]
|
|
|
|
mean value: 0.4578736283743391
|
|
|
|
key: train_mcc
|
|
value: [0.76486616 0.75574218 0.77777778 0.76026311 0.73405869 0.78478493
|
|
0.77777778 0.84632727 0.80498447 0.78086881]
|
|
|
|
mean value: 0.7787451171317092
|
|
|
|
key: test_fscore
|
|
value: [0.75 0.66666667 0.8 0.8 0.90909091 0.5
|
|
0.72727273 0.66666667 0.54545455 0.75 ]
|
|
|
|
mean value: 0.7115151515151515
|
|
|
|
key: train_fscore
|
|
value: [0.86746988 0.87640449 0.88888889 0.87058824 0.86363636 0.88095238
|
|
0.88888889 0.91954023 0.89411765 0.88372093]
|
|
|
|
mean value: 0.8834207938737174
|
|
|
|
key: test_precision
|
|
value: [1. 0.75 0.8 0.8 0.83333333 0.66666667
|
|
0.66666667 0.57142857 0.5 1. ]
|
|
|
|
mean value: 0.7588095238095238
|
|
|
|
key: train_precision
|
|
value: [0.94736842 0.88636364 0.88888889 0.925 0.88372093 0.94871795
|
|
0.88888889 0.95238095 0.95 0.92682927]
|
|
|
|
mean value: 0.9198158934818188
|
|
|
|
key: test_recall
|
|
value: [0.6 0.6 0.8 0.8 1. 0.4 0.8 0.8 0.6 0.6]
|
|
|
|
mean value: 0.7
|
|
|
|
key: train_recall
|
|
value: [0.8 0.86666667 0.88888889 0.82222222 0.84444444 0.82222222
|
|
0.88888889 0.88888889 0.84444444 0.84444444]
|
|
|
|
mean value: 0.8511111111111112
|
|
|
|
key: test_accuracy
|
|
value: [0.8 0.7 0.8 0.8 0.9 0.6 0.7 0.6 0.5 0.8]
|
|
|
|
mean value: 0.72
|
|
|
|
key: train_accuracy
|
|
value: [0.87777778 0.87777778 0.88888889 0.87777778 0.86666667 0.88888889
|
|
0.88888889 0.92222222 0.9 0.88888889]
|
|
|
|
mean value: 0.8877777777777778
|
|
|
|
key: test_roc_auc
|
|
value: [0.8 0.7 0.8 0.8 0.9 0.6 0.7 0.6 0.5 0.8]
|
|
|
|
mean value: 0.72
|
|
|
|
key: train_roc_auc
|
|
value: [0.87777778 0.87777778 0.88888889 0.87777778 0.86666667 0.88888889
|
|
0.88888889 0.92222222 0.9 0.88888889]
|
|
|
|
mean value: 0.8877777777777778
|
|
|
|
key: test_jcc
|
|
value: [0.6 0.5 0.66666667 0.66666667 0.83333333 0.33333333
|
|
0.57142857 0.5 0.375 0.6 ]
|
|
|
|
mean value: 0.5646428571428571
|
|
|
|
key: train_jcc
|
|
value: [0.76595745 0.78 0.8 0.77083333 0.76 0.78723404
|
|
0.8 0.85106383 0.80851064 0.79166667]
|
|
|
|
mean value: 0.791526595744681
|
|
|
|
key: TN
|
|
value: 37
|
|
|
|
mean value: 37.0
|
|
|
|
key: FP
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: FN
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: TP
|
|
value: 35
|
|
|
|
mean value: 35.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.04
|
|
|
|
Accuracy on Blind test: 0.62
|
|
|
|
Running classifier: 7
|
|
Model_name: MLP
|
|
Model func: MLPClassifier(max_iter=500, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MLPClassifier(max_iter=500, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.35339808 0.34032393 0.34420347 0.40963769 0.37293458 0.36176634
|
|
0.34549356 0.38289881 0.32440186 0.49558091]
|
|
|
|
mean value: 0.37306392192840576
|
|
|
|
key: score_time
|
|
value: [0.01189828 0.01193237 0.01191092 0.012012 0.01190543 0.01191354
|
|
0.0119226 0.01200247 0.01195073 0.01197505]
|
|
|
|
mean value: 0.011942338943481446
|
|
|
|
key: test_mcc
|
|
value: [1. 0.6 1. 0.5 0.65465367 0.40824829
|
|
0.40824829 0.5 0.65465367 0.81649658]
|
|
|
|
mean value: 0.6542300503271407
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.8 1. 0.76923077 0.83333333 0.66666667
|
|
0.72727273 0.76923077 0.83333333 0.88888889]
|
|
|
|
mean value: 0.8287956487956487
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.8 1. 0.625 0.71428571 0.75
|
|
0.66666667 0.625 0.71428571 1. ]
|
|
|
|
mean value: 0.7895238095238095
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.8 1. 1. 1. 0.6 0.8 1. 1. 0.8]
|
|
|
|
mean value: 0.9
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.8 1. 0.7 0.8 0.7 0.7 0.7 0.8 0.9]
|
|
|
|
mean value: 0.8099999999999999
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.8 1. 0.7 0.8 0.7 0.7 0.7 0.8 0.9]
|
|
|
|
mean value: 0.8099999999999999
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.66666667 1. 0.625 0.71428571 0.5
|
|
0.57142857 0.625 0.71428571 0.8 ]
|
|
|
|
mean value: 0.7216666666666667
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 36
|
|
|
|
mean value: 36.0
|
|
|
|
key: FP
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: FN
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: TP
|
|
value: 45
|
|
|
|
mean value: 45.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.12
|
|
|
|
Accuracy on Blind test: 0.6
|
|
|
|
Running classifier: 8
|
|
Model_name: Decision Tree
|
|
Model func: DecisionTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', DecisionTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01333547 0.01328611 0.0099473 0.00956702 0.0093472 0.00933528
|
|
0.00904536 0.00918818 0.00934958 0.00936794]
|
|
|
|
mean value: 0.010176944732666015
|
|
|
|
key: score_time
|
|
value: [0.01137042 0.01056337 0.00862908 0.00830317 0.00825858 0.00827336
|
|
0.00822067 0.00820446 0.00816536 0.00829554]
|
|
|
|
mean value: 0.008828401565551758
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.81649658 1. 0.65465367 0.65465367 0.6
|
|
0.81649658 0.40824829 0.81649658 0.81649658]
|
|
|
|
mean value: 0.7400038536518447
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.90909091 1. 0.83333333 0.83333333 0.8
|
|
0.88888889 0.72727273 0.90909091 0.88888889]
|
|
|
|
mean value: 0.8698989898989898
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.83333333 0.83333333 1. 0.71428571 0.71428571 0.8
|
|
1. 0.66666667 0.83333333 1. ]
|
|
|
|
mean value: 0.8395238095238096
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 1. 1. 0.8 0.8 0.8 1. 0.8]
|
|
|
|
mean value: 0.9200000000000002
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.9 1. 0.8 0.8 0.8 0.9 0.7 0.9 0.9]
|
|
|
|
mean value: 0.8600000000000001
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.9 1. 0.8 0.8 0.8 0.9 0.7 0.9 0.9]
|
|
|
|
mean value: 0.8600000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.83333333 1. 0.71428571 0.71428571 0.66666667
|
|
0.8 0.57142857 0.83333333 0.8 ]
|
|
|
|
mean value: 0.7766666666666666
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: FP
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: FN
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: TP
|
|
value: 46
|
|
|
|
mean value: 46.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.89
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 9
|
|
Model_name: Extra Trees
|
|
Model func: ExtraTreesClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreesClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.08124042 0.07976699 0.08055592 0.08180213 0.0852704 0.08435702
|
|
0.0828104 0.08187795 0.08418012 0.08399606]
|
|
|
|
mean value: 0.0825857400894165
|
|
|
|
key: score_time
|
|
value: [0.01652694 0.01657915 0.01681757 0.01673293 0.0175488 0.01702619
|
|
0.0173912 0.01703215 0.01830769 0.01708388]
|
|
|
|
mean value: 0.01710464954376221
|
|
|
|
key: test_mcc
|
|
value: [1. 0.40824829 0.81649658 0.65465367 0.81649658 0.5
|
|
0.40824829 0.40824829 0.65465367 0.65465367]
|
|
|
|
mean value: 0.6321699045370972
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.66666667 0.88888889 0.83333333 0.90909091 0.57142857
|
|
0.72727273 0.72727273 0.83333333 0.75 ]
|
|
|
|
mean value: 0.7907287157287157
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.75 1. 0.71428571 0.83333333 1.
|
|
0.66666667 0.66666667 0.71428571 1. ]
|
|
|
|
mean value: 0.8345238095238094
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.6 0.8 1. 1. 0.4 0.8 0.8 1. 0.6]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.7 0.9 0.8 0.9 0.7 0.7 0.7 0.8 0.8]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.7 0.9 0.8 0.9 0.7 0.7 0.7 0.8 0.8]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.5 0.8 0.71428571 0.83333333 0.4
|
|
0.57142857 0.57142857 0.71428571 0.6 ]
|
|
|
|
mean value: 0.6704761904761904
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: TP
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.49
|
|
|
|
Accuracy on Blind test: 0.78
|
|
|
|
Running classifier: 10
|
|
Model_name: Extra Tree
|
|
Model func: ExtraTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.0081377 0.00820899 0.00818825 0.00807595 0.00807285 0.00856829
|
|
0.00839448 0.00841856 0.00827169 0.00830626]
|
|
|
|
mean value: 0.008264303207397461
|
|
|
|
key: score_time
|
|
value: [0.00828624 0.00839925 0.00824952 0.00820351 0.00828624 0.00887203
|
|
0.00863862 0.00841212 0.00832582 0.00837159]
|
|
|
|
mean value: 0.00840449333190918
|
|
|
|
key: test_mcc
|
|
value: [ 1. 0.40824829 0.81649658 0.65465367 0.81649658 -0.5
|
|
0.40824829 0.21821789 0.81649658 0.65465367]
|
|
|
|
mean value: 0.5293511555362851
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.72727273 0.88888889 0.83333333 0.90909091 0.46153846
|
|
0.72727273 0.66666667 0.90909091 0.75 ]
|
|
|
|
mean value: 0.7873154623154623
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.66666667 1. 0.71428571 0.83333333 0.375
|
|
0.66666667 0.57142857 0.83333333 1. ]
|
|
|
|
mean value: 0.7660714285714285
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.8 0.8 1. 1. 0.6 0.8 0.8 1. 0.6]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.7 0.9 0.8 0.9 0.3 0.7 0.6 0.9 0.8]
|
|
|
|
mean value: 0.76
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.7 0.9 0.8 0.9 0.3 0.7 0.6 0.9 0.8]
|
|
|
|
mean value: 0.76
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.57142857 0.8 0.71428571 0.83333333 0.3
|
|
0.57142857 0.5 0.83333333 0.6 ]
|
|
|
|
mean value: 0.6723809523809523
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 34
|
|
|
|
mean value: 34.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: TP
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.56
|
|
|
|
Accuracy on Blind test: 0.8
|
|
|
|
Running classifier: 11
|
|
Model_name: Random Forest
|
|
Model func: RandomForestClassifier(n_estimators=1000, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(n_estimators=1000, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [1.0857687 1.02543187 1.06359982 1.03614068 1.06371474 1.02588892
|
|
1.02457833 1.05441475 1.06075287 1.04914474]
|
|
|
|
mean value: 1.048943543434143
|
|
|
|
key: score_time
|
|
value: [0.09435582 0.09166527 0.087533 0.09539175 0.08773398 0.0948875
|
|
0.094208 0.09529018 0.09117222 0.09602284]
|
|
|
|
mean value: 0.09282605648040772
|
|
|
|
key: test_mcc
|
|
value: [1. 0.81649658 1. 0.81649658 1. 0.65465367
|
|
0.81649658 0.40824829 0.65465367 0.65465367]
|
|
|
|
mean value: 0.7821699045370971
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.88888889 1. 0.90909091 1. 0.75
|
|
0.88888889 0.72727273 0.83333333 0.75 ]
|
|
|
|
mean value: 0.8747474747474746
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 0.83333333 1. 1.
|
|
1. 0.66666667 0.71428571 1. ]
|
|
|
|
mean value: 0.9214285714285714
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.8 1. 1. 1. 0.6 0.8 0.8 1. 0.6]
|
|
|
|
mean value: 0.86
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9 1. 0.9 1. 0.8 0.9 0.7 0.8 0.8]
|
|
|
|
mean value: 0.8800000000000001
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.9 1. 0.9 1. 0.8 0.9 0.7 0.8 0.8]
|
|
|
|
mean value: 0.8800000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.8 1. 0.83333333 1. 0.6
|
|
0.8 0.57142857 0.71428571 0.6 ]
|
|
|
|
mean value: 0.7919047619047619
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 45
|
|
|
|
mean value: 45.0
|
|
|
|
key: FP
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: FN
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: TP
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.55
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
|
|
Accuracy on Blind test: 0.8
|
|
|
|
Running classifier: 12
|
|
Model_name: Random Forest2
|
|
Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...age_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10,
|
|
oob_score=True, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.89968204 0.85457993 0.8354466 0.8666656 0.85017776 0.82677722
|
|
0.88512731 0.85213256 0.84619737 0.91214228]
|
|
|
|
mean value: 0.8628928661346436
|
|
|
|
key: score_time
|
|
value: [0.13997507 0.14476967 0.14723778 0.16803837 0.16983438 0.17666507
|
|
0.17594314 0.1609211 0.17945981 0.17022514]
|
|
|
|
mean value: 0.16330695152282715
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.81649658 0.81649658 0.81649658 1. 0.81649658
|
|
0.6 0.40824829 0.81649658 0.65465367]
|
|
|
|
mean value: 0.7561881446738197
|
|
|
|
key: train_mcc
|
|
value: [0.95555556 0.95555556 0.93356387 0.95555556 0.95650071 0.95650071
|
|
0.95650071 0.93356387 1. 0.97801929]
|
|
|
|
mean value: 0.9581315847088575
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.88888889 0.88888889 0.90909091 1. 0.88888889
|
|
0.8 0.72727273 0.90909091 0.75 ]
|
|
|
|
mean value: 0.867121212121212
|
|
|
|
key: train_fscore
|
|
value: [0.97777778 0.97777778 0.96629213 0.97777778 0.97826087 0.97727273
|
|
0.97826087 0.96629213 1. 0.98876404]
|
|
|
|
mean value: 0.9788476114343239
|
|
|
|
key: test_precision
|
|
value: [0.83333333 1. 1. 0.83333333 1. 1.
|
|
0.8 0.66666667 0.83333333 1. ]
|
|
|
|
mean value: 0.8966666666666667
|
|
|
|
key: train_precision
|
|
value: [0.97777778 0.97777778 0.97727273 0.97777778 0.95744681 1.
|
|
0.95744681 0.97727273 1. 1. ]
|
|
|
|
mean value: 0.9802772404900064
|
|
|
|
key: test_recall
|
|
value: [1. 0.8 0.8 1. 1. 0.8 0.8 0.8 1. 0.6]
|
|
|
|
mean value: 0.86
|
|
|
|
key: train_recall
|
|
value: [0.97777778 0.97777778 0.95555556 0.97777778 1. 0.95555556
|
|
1. 0.95555556 1. 0.97777778]
|
|
|
|
mean value: 0.9777777777777779
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.9 0.9 0.9 1. 0.9 0.8 0.7 0.9 0.8]
|
|
|
|
mean value: 0.8700000000000001
|
|
|
|
key: train_accuracy
|
|
value: [0.97777778 0.97777778 0.96666667 0.97777778 0.97777778 0.97777778
|
|
0.97777778 0.96666667 1. 0.98888889]
|
|
|
|
mean value: 0.9788888888888889
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.9 0.9 0.9 1. 0.9 0.8 0.7 0.9 0.8]
|
|
|
|
mean value: 0.8700000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [0.97777778 0.97777778 0.96666667 0.97777778 0.97777778 0.97777778
|
|
0.97777778 0.96666667 1. 0.98888889]
|
|
|
|
mean value: 0.9788888888888889
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.8 0.8 0.83333333 1. 0.8
|
|
0.66666667 0.57142857 0.83333333 0.6 ]
|
|
|
|
mean value: 0.7738095238095237
|
|
|
|
key: train_jcc
|
|
value: [0.95652174 0.95652174 0.93478261 0.95652174 0.95744681 0.95555556
|
|
0.95744681 0.93478261 1. 0.97777778]
|
|
|
|
mean value: 0.9587357385137218
|
|
|
|
key: TN
|
|
value: 44
|
|
|
|
mean value: 44.0
|
|
|
|
key: FP
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: FN
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: TP
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.61
|
|
|
|
Accuracy on Blind test: 0.82
|
|
|
|
Running classifier: 13
|
|
Model_name: XGBoost
|
|
Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...
|
|
interaction_constraints=None, learning_rate=None,
|
|
max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan,
|
|
monotone_constraints=None, n_estimators=100,
|
|
n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None,
|
|
reg_lambda=None, scale_pos_weight=None,
|
|
subsample=None, tree_method=None,
|
|
use_label_encoder=False,
|
|
validate_parameters=None, verbosity=0))])
|
|
|
|
key: fit_time
|
|
value: [0.04202104 0.08573484 0.03951287 0.03615594 0.03516817 0.03643227
|
|
0.03565812 0.03513455 0.03435874 0.03614831]
|
|
|
|
mean value: 0.04163248538970947
|
|
|
|
key: score_time
|
|
value: [0.01022768 0.01082444 0.01064587 0.01083302 0.0103128 0.01038003
|
|
0.01106954 0.01054955 0.01016593 0.01010156]
|
|
|
|
mean value: 0.010511040687561035
|
|
|
|
key: test_mcc
|
|
value: [1. 0.81649658 1. 1. 1. 0.6
|
|
0.81649658 0.6 0.5 1. ]
|
|
|
|
mean value: 0.8332993161855452
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.90909091 1. 1. 1. 0.8
|
|
0.88888889 0.8 0.76923077 1. ]
|
|
|
|
mean value: 0.9167210567210569
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 1. 1. 1. 0.8
|
|
1. 0.8 0.625 1. ]
|
|
|
|
mean value: 0.9058333333333334
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 1. 1. 0.8 0.8 0.8 1. 1. ]
|
|
|
|
mean value: 0.9400000000000001
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9 1. 1. 1. 0.8 0.9 0.8 0.7 1. ]
|
|
|
|
mean value: 0.9099999999999999
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.9 1. 1. 1. 0.8 0.9 0.8 0.7 1. ]
|
|
|
|
mean value: 0.9099999999999999
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.83333333 1. 1. 1. 0.66666667
|
|
0.8 0.66666667 0.625 1. ]
|
|
|
|
mean value: 0.8591666666666666
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 44
|
|
|
|
mean value: 44.0
|
|
|
|
key: FP
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: FN
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: TP
|
|
value: 47
|
|
|
|
mean value: 47.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.89
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 14
|
|
Model_name: LDA
|
|
Model func: LinearDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LinearDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.02282596 0.03948879 0.03882122 0.04212666 0.0416882 0.04012561
|
|
0.04158378 0.0415206 0.04133034 0.04192233]
|
|
|
|
mean value: 0.03914334774017334
|
|
|
|
key: score_time
|
|
value: [0.02050471 0.02193856 0.02390456 0.02119875 0.02115989 0.02110076
|
|
0.01587558 0.02117825 0.02245665 0.020365 ]
|
|
|
|
mean value: 0.02096827030181885
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.21821789 1. 0.65465367 0.81649658 0.
|
|
0.6 0.65465367 0.5 0.81649658]
|
|
|
|
mean value: 0.6077014974435124
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.66666667 1. 0.83333333 0.90909091 0.61538462
|
|
0.8 0.83333333 0.76923077 0.88888889]
|
|
|
|
mean value: 0.8225019425019425
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.83333333 0.57142857 1. 0.71428571 0.83333333 0.5
|
|
0.8 0.71428571 0.625 1. ]
|
|
|
|
mean value: 0.7591666666666667
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.8 1. 1. 1. 0.8 0.8 1. 1. 0.8]
|
|
|
|
mean value: 0.9200000000000002
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.6 1. 0.8 0.9 0.5 0.8 0.8 0.7 0.9]
|
|
|
|
mean value: 0.79
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.6 1. 0.8 0.9 0.5 0.8 0.8 0.7 0.9]
|
|
|
|
mean value: 0.79
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.5 1. 0.71428571 0.83333333 0.44444444
|
|
0.66666667 0.71428571 0.625 0.8 ]
|
|
|
|
mean value: 0.7131349206349207
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 33
|
|
|
|
mean value: 33.0
|
|
|
|
key: FP
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: FN
|
|
value: 17
|
|
|
|
mean value: 17.0
|
|
|
|
key: TP
|
|
value: 46
|
|
|
|
mean value: 46.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.0
|
|
|
|
Accuracy on Blind test: 0.5
|
|
|
|
Running classifier: 15
|
|
Model_name: Multinomial
|
|
Model func: MultinomialNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MultinomialNB())])
|
|
|
|
key: fit_time
|
|
value: [0.02296448 0.0084753 0.00825715 0.00825763 0.00829768 0.00805593
|
|
0.00811934 0.00863338 0.00841451 0.00900841]
|
|
|
|
mean value: 0.009848380088806152
|
|
|
|
key: score_time
|
|
value: [0.01073384 0.00855923 0.00831366 0.00836492 0.00844288 0.00820661
|
|
0.00821209 0.00819874 0.00902271 0.00844789]
|
|
|
|
mean value: 0.008650255203247071
|
|
|
|
key: test_mcc
|
|
value: [ 0. 0.40824829 0.6 0.2 0.40824829 0.
|
|
-0.2 0.2 -0.2 0.21821789]
|
|
|
|
mean value: 0.16347144711637185
|
|
|
|
key: train_mcc
|
|
value: [0.4000988 0.38118125 0.40492914 0.4949134 0.40249224 0.48900965
|
|
0.44992127 0.40249224 0.33366304 0.40249224]
|
|
|
|
mean value: 0.41611932587025435
|
|
|
|
key: test_fscore
|
|
value: [0.54545455 0.66666667 0.8 0.6 0.66666667 0.54545455
|
|
0.4 0.6 0.4 0.5 ]
|
|
|
|
mean value: 0.5724242424242425
|
|
|
|
key: train_fscore
|
|
value: [0.69662921 0.66666667 0.6746988 0.72289157 0.68235294 0.74725275
|
|
0.69879518 0.68235294 0.65909091 0.68235294]
|
|
|
|
mean value: 0.6913083902191556
|
|
|
|
key: test_precision
|
|
value: [0.5 0.75 0.8 0.6 0.75 0.5
|
|
0.4 0.6 0.4 0.66666667]
|
|
|
|
mean value: 0.5966666666666668
|
|
|
|
key: train_precision
|
|
value: [0.70454545 0.71794872 0.73684211 0.78947368 0.725 0.73913043
|
|
0.76315789 0.725 0.6744186 0.725 ]
|
|
|
|
mean value: 0.730051689613847
|
|
|
|
key: test_recall
|
|
value: [0.6 0.6 0.8 0.6 0.6 0.6 0.4 0.6 0.4 0.4]
|
|
|
|
mean value: 0.56
|
|
|
|
key: train_recall
|
|
value: [0.68888889 0.62222222 0.62222222 0.66666667 0.64444444 0.75555556
|
|
0.64444444 0.64444444 0.64444444 0.64444444]
|
|
|
|
mean value: 0.6577777777777778
|
|
|
|
key: test_accuracy
|
|
value: [0.5 0.7 0.8 0.6 0.7 0.5 0.4 0.6 0.4 0.6]
|
|
|
|
mean value: 0.58
|
|
|
|
key: train_accuracy
|
|
value: [0.7 0.68888889 0.7 0.74444444 0.7 0.74444444
|
|
0.72222222 0.7 0.66666667 0.7 ]
|
|
|
|
mean value: 0.7066666666666667
|
|
|
|
key: test_roc_auc
|
|
value: [0.5 0.7 0.8 0.6 0.7 0.5 0.4 0.6 0.4 0.6]
|
|
|
|
mean value: 0.5800000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [0.7 0.68888889 0.7 0.74444444 0.7 0.74444444
|
|
0.72222222 0.7 0.66666667 0.7 ]
|
|
|
|
mean value: 0.7066666666666667
|
|
|
|
key: test_jcc
|
|
value: [0.375 0.5 0.66666667 0.42857143 0.5 0.375
|
|
0.25 0.42857143 0.25 0.33333333]
|
|
|
|
mean value: 0.4107142857142857
|
|
|
|
key: train_jcc
|
|
value: [0.53448276 0.5 0.50909091 0.56603774 0.51785714 0.59649123
|
|
0.53703704 0.51785714 0.49152542 0.51785714]
|
|
|
|
mean value: 0.528823652096811
|
|
|
|
key: TN
|
|
value: 30
|
|
|
|
mean value: 30.0
|
|
|
|
key: FP
|
|
value: 22
|
|
|
|
mean value: 22.0
|
|
|
|
key: FN
|
|
value: 20
|
|
|
|
mean value: 20.0
|
|
|
|
key: TP
|
|
value: 28
|
|
|
|
mean value: 28.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.14
|
|
|
|
Accuracy on Blind test: 0.48
|
|
|
|
Running classifier: 16
|
|
Model_name: Passive Aggresive
|
|
Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
PassiveAggressiveClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01120234 0.01247239 0.01248765 0.01223207 0.01297379 0.01218271
|
|
0.01372743 0.01458359 0.01382494 0.01353407]
|
|
|
|
mean value: 0.012922096252441406
|
|
|
|
key: score_time
|
|
value: [0.00857997 0.01098967 0.01123166 0.01120615 0.01122999 0.01121783
|
|
0.01131988 0.01128626 0.01141262 0.01127839]
|
|
|
|
mean value: 0.010975241661071777
|
|
|
|
key: test_mcc
|
|
value: [1. 0.81649658 0.65465367 0.40824829 0.33333333 0.81649658
|
|
0.65465367 0.5 0.2 0.65465367]
|
|
|
|
mean value: 0.6038535797776581
|
|
|
|
key: train_mcc
|
|
value: [0.97801929 0.83553169 0.77919372 0.89087081 0.89442719 0.93541435
|
|
0.95650071 0.95650071 0.97801929 0.77919372]
|
|
|
|
mean value: 0.8983671497421686
|
|
|
|
key: test_fscore
|
|
value: [1. 0.88888889 0.83333333 0.72727273 0.71428571 0.90909091
|
|
0.83333333 0.76923077 0.6 0.75 ]
|
|
|
|
mean value: 0.8025435675435675
|
|
|
|
key: train_fscore
|
|
value: [0.98901099 0.90243902 0.89108911 0.94623656 0.94736842 0.96774194
|
|
0.97826087 0.97826087 0.98876404 0.86075949]
|
|
|
|
mean value: 0.9449931315733553
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.71428571 0.66666667 0.55555556 0.83333333
|
|
0.71428571 0.625 0.6 1. ]
|
|
|
|
mean value: 0.7709126984126984
|
|
|
|
key: train_precision
|
|
value: [0.97826087 1. 0.80357143 0.91666667 0.9 0.9375
|
|
0.95744681 0.95744681 1. 1. ]
|
|
|
|
mean value: 0.945089258182459
|
|
|
|
key: test_recall
|
|
value: [1. 0.8 1. 0.8 1. 1. 1. 1. 0.6 0.6]
|
|
|
|
mean value: 0.8799999999999999
|
|
|
|
key: train_recall
|
|
value: [1. 0.82222222 1. 0.97777778 1. 1.
|
|
1. 1. 0.97777778 0.75555556]
|
|
|
|
mean value: 0.9533333333333331
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9 0.8 0.7 0.6 0.9 0.8 0.7 0.6 0.8]
|
|
|
|
mean value: 0.78
|
|
|
|
key: train_accuracy
|
|
value: [0.98888889 0.91111111 0.87777778 0.94444444 0.94444444 0.96666667
|
|
0.97777778 0.97777778 0.98888889 0.87777778]
|
|
|
|
mean value: 0.9455555555555556
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.9 0.8 0.7 0.6 0.9 0.8 0.7 0.6 0.8]
|
|
|
|
mean value: 0.78
|
|
|
|
key: train_roc_auc
|
|
value: [0.98888889 0.91111111 0.87777778 0.94444444 0.94444444 0.96666667
|
|
0.97777778 0.97777778 0.98888889 0.87777778]
|
|
|
|
mean value: 0.9455555555555556
|
|
|
|
key: test_jcc
|
|
value: [1. 0.8 0.71428571 0.57142857 0.55555556 0.83333333
|
|
0.71428571 0.625 0.42857143 0.6 ]
|
|
|
|
mean value: 0.6842460317460317
|
|
|
|
key: train_jcc
|
|
value: [0.97826087 0.82222222 0.80357143 0.89795918 0.9 0.9375
|
|
0.95744681 0.95744681 0.97777778 0.75555556]
|
|
|
|
mean value: 0.8987740654386946
|
|
|
|
key: TN
|
|
value: 34
|
|
|
|
mean value: 34.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: TP
|
|
value: 44
|
|
|
|
mean value: 44.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.27
|
|
|
|
Accuracy on Blind test: 0.68
|
|
|
|
Running classifier: 17
|
|
Model_name: Stochastic GDescent
|
|
Model func: SGDClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SGDClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00876498 0.01228714 0.01203871 0.01212764 0.01211023 0.01215172
|
|
0.01181269 0.01210976 0.01228261 0.01225233]
|
|
|
|
mean value: 0.011793780326843261
|
|
|
|
key: score_time
|
|
value: [0.00829244 0.01176238 0.01127028 0.01124716 0.01124883 0.01142573
|
|
0.01126313 0.01129913 0.01135111 0.01130509]
|
|
|
|
mean value: 0.011046528816223145
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.33333333 0.81649658 0.65465367 0.33333333 0.65465367
|
|
0.33333333 0.21821789 0.5 0.6 ]
|
|
|
|
mean value: 0.5260518393507398
|
|
|
|
key: train_mcc
|
|
value: [0.91111111 0.42919754 0.82548988 0.95555556 0.93541435 0.91473203
|
|
0.4108907 0.83553169 1. 1. ]
|
|
|
|
mean value: 0.8217922862521331
|
|
|
|
key: test_fscore
|
|
value: [0.88888889 0.71428571 0.88888889 0.83333333 0.71428571 0.83333333
|
|
0.33333333 0.66666667 0.76923077 0.8 ]
|
|
|
|
mean value: 0.7442246642246643
|
|
|
|
key: train_fscore
|
|
value: [0.95555556 0.74380165 0.90697674 0.97777778 0.96774194 0.95744681
|
|
0.44827586 0.91836735 1. 1. ]
|
|
|
|
mean value: 0.8875943683414192
|
|
|
|
key: test_precision
|
|
value: [1. 0.55555556 1. 0.71428571 0.55555556 0.71428571
|
|
1. 0.57142857 0.625 0.8 ]
|
|
|
|
mean value: 0.753611111111111
|
|
|
|
key: train_precision
|
|
value: [0.95555556 0.59210526 0.95121951 0.97777778 0.9375 0.91836735
|
|
1. 0.8490566 1. 1. ]
|
|
|
|
mean value: 0.9181582059398711
|
|
|
|
key: test_recall
|
|
value: [0.8 1. 0.8 1. 1. 1. 0.2 0.8 1. 0.8]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_recall
|
|
value: [0.95555556 1. 0.86666667 0.97777778 1. 1.
|
|
0.28888889 1. 1. 1. ]
|
|
|
|
mean value: 0.9088888888888889
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.6 0.9 0.8 0.6 0.8 0.6 0.6 0.7 0.8]
|
|
|
|
mean value: 0.73
|
|
|
|
key: train_accuracy
|
|
value: [0.95555556 0.65555556 0.91111111 0.97777778 0.96666667 0.95555556
|
|
0.64444444 0.91111111 1. 1. ]
|
|
|
|
mean value: 0.8977777777777778
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.6 0.9 0.8 0.6 0.8 0.6 0.6 0.7 0.8]
|
|
|
|
mean value: 0.7300000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [0.95555556 0.65555556 0.91111111 0.97777778 0.96666667 0.95555556
|
|
0.64444444 0.91111111 1. 1. ]
|
|
|
|
mean value: 0.8977777777777778
|
|
|
|
key: test_jcc
|
|
value: [0.8 0.55555556 0.8 0.71428571 0.55555556 0.71428571
|
|
0.2 0.5 0.625 0.66666667]
|
|
|
|
mean value: 0.6131349206349206
|
|
|
|
key: train_jcc
|
|
value: [0.91489362 0.59210526 0.82978723 0.95652174 0.9375 0.91836735
|
|
0.28888889 0.8490566 1. 1. ]
|
|
|
|
mean value: 0.8287120692953408
|
|
|
|
key: TN
|
|
value: 31
|
|
|
|
mean value: 31.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 19
|
|
|
|
mean value: 19.0
|
|
|
|
key: TP
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.51
|
|
|
|
Accuracy on Blind test: 0.78
|
|
|
|
Running classifier: 18
|
|
Model_name: AdaBoost Classifier
|
|
Model func: AdaBoostClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', AdaBoostClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.07988095 0.07883573 0.07912493 0.07934546 0.07975411 0.07876873
|
|
0.07892227 0.0793283 0.07942939 0.07979774]
|
|
|
|
mean value: 0.07931876182556152
|
|
|
|
key: score_time
|
|
value: [0.01414919 0.01410151 0.01419544 0.01427197 0.01422954 0.01440668
|
|
0.01421189 0.01422381 0.01437879 0.01422191]
|
|
|
|
mean value: 0.014239072799682617
|
|
|
|
key: test_mcc
|
|
value: [0.65465367 0.81649658 1. 0.81649658 1. 0.81649658
|
|
0.81649658 0.65465367 0.65465367 1. ]
|
|
|
|
mean value: 0.8229947335834836
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.83333333 0.90909091 1. 0.90909091 1. 0.88888889
|
|
0.88888889 0.83333333 0.83333333 1. ]
|
|
|
|
mean value: 0.9095959595959597
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.71428571 0.83333333 1. 0.83333333 1. 1.
|
|
1. 0.71428571 0.71428571 1. ]
|
|
|
|
mean value: 0.880952380952381
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 1. 1. 0.8 0.8 1. 1. 1. ]
|
|
|
|
mean value: 0.96
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.8 0.9 1. 0.9 1. 0.9 0.9 0.8 0.8 1. ]
|
|
|
|
mean value: 0.9
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.8 0.9 1. 0.9 1. 0.9 0.9 0.8 0.8 1. ]
|
|
|
|
mean value: 0.9
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.71428571 0.83333333 1. 0.83333333 1. 0.8
|
|
0.8 0.71428571 0.71428571 1. ]
|
|
|
|
mean value: 0.840952380952381
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: FP
|
|
value: 2
|
|
|
|
mean value: 2.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 48
|
|
|
|
mean value: 48.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.89
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 19
|
|
Model_name: Bagging Classifier
|
|
Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
BaggingClassifier(n_jobs=10, oob_score=True,
|
|
random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02679038 0.02585912 0.02684498 0.03183603 0.04586864 0.03085876
|
|
0.03211308 0.03402162 0.03574443 0.03347778]
|
|
|
|
mean value: 0.03234148025512695
|
|
|
|
key: score_time
|
|
value: [0.02189565 0.01761818 0.02150726 0.0278151 0.02133465 0.02162409
|
|
0.02341056 0.02291417 0.03275824 0.01854229]
|
|
|
|
mean value: 0.022942018508911134
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.81649658 1. 1. 1. 0.6
|
|
0.81649658 0.65465367 0.81649658 1. ]
|
|
|
|
mean value: 0.8520639994418883
|
|
|
|
key: train_mcc
|
|
value: [0.97801929 1. 0.97801929 1. 1. 1.
|
|
0.97801929 1. 1. 1. ]
|
|
|
|
mean value: 0.9934057881530954
|
|
|
|
key: test_fscore
|
|
value: [0.88888889 0.90909091 1. 1. 1. 0.8
|
|
0.88888889 0.83333333 0.90909091 1. ]
|
|
|
|
mean value: 0.9229292929292929
|
|
|
|
key: train_fscore
|
|
value: [0.98876404 1. 0.98876404 1. 1. 1.
|
|
0.98901099 1. 1. 1. ]
|
|
|
|
mean value: 0.9966539078898629
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 1. 1. 1. 0.8
|
|
1. 0.71428571 0.83333333 1. ]
|
|
|
|
mean value: 0.9180952380952382
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
0.97826087 1. 1. 1. ]
|
|
|
|
mean value: 0.9978260869565216
|
|
|
|
key: test_recall
|
|
value: [0.8 1. 1. 1. 1. 0.8 0.8 1. 1. 1. ]
|
|
|
|
mean value: 0.9400000000000001
|
|
|
|
key: train_recall
|
|
value: [0.97777778 1. 0.97777778 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9955555555555555
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.9 1. 1. 1. 0.8 0.9 0.8 0.9 1. ]
|
|
|
|
mean value: 0.9199999999999999
|
|
|
|
key: train_accuracy
|
|
value: [0.98888889 1. 0.98888889 1. 1. 1.
|
|
0.98888889 1. 1. 1. ]
|
|
|
|
mean value: 0.9966666666666667
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.9 1. 1. 1. 0.8 0.9 0.8 0.9 1. ]
|
|
|
|
mean value: 0.9199999999999999
|
|
|
|
key: train_roc_auc
|
|
value: [0.98888889 1. 0.98888889 1. 1. 1.
|
|
0.98888889 1. 1. 1. ]
|
|
|
|
mean value: 0.9966666666666667
|
|
|
|
key: test_jcc
|
|
value: [0.8 0.83333333 1. 1. 1. 0.66666667
|
|
0.8 0.71428571 0.83333333 1. ]
|
|
|
|
mean value: 0.8647619047619047
|
|
|
|
key: train_jcc
|
|
value: [0.97777778 1. 0.97777778 1. 1. 1.
|
|
0.97826087 1. 1. 1. ]
|
|
|
|
mean value: 0.9933816425120773
|
|
|
|
key: TN
|
|
value: 45
|
|
|
|
mean value: 45.0
|
|
|
|
key: FP
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: FN
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: TP
|
|
value: 47
|
|
|
|
mean value: 47.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.95
|
|
|
|
Accuracy on Blind test: 0.98
|
|
|
|
Running classifier: 20
|
|
Model_name: Gaussian Process
|
|
Model func: GaussianProcessClassifier(random_state=42)
|
|
|
|
Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianProcessClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01471233 0.01499677 0.01495695 0.01577234 0.01556015 0.01557922
|
|
0.01554871 0.01555872 0.01553726 0.01553893]
|
|
|
|
mean value: 0.015376138687133788
|
|
|
|
key: score_time
|
|
value: [0.01128864 0.01108527 0.0116055 0.01157355 0.01152849 0.01179886
|
|
0.01158834 0.01157236 0.0115447 0.01676178]
|
|
|
|
mean value: 0.012034749984741211
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.40824829 1. 0.65465367 0.65465367 0.21821789
|
|
0.21821789 0.65465367 0.5 0.65465367]
|
|
|
|
mean value: 0.5779795334695482
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.66666667 1. 0.83333333 0.83333333 0.5
|
|
0.66666667 0.83333333 0.76923077 0.75 ]
|
|
|
|
mean value: 0.7761655011655011
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.83333333 0.75 1. 0.71428571 0.71428571 0.66666667
|
|
0.57142857 0.71428571 0.625 1. ]
|
|
|
|
mean value: 0.7589285714285714
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.6 1. 1. 1. 0.4 0.8 1. 1. 0.6]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.7 1. 0.8 0.8 0.6 0.6 0.8 0.7 0.8]
|
|
|
|
mean value: 0.77
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.7 1. 0.8 0.8 0.6 0.6 0.8 0.7 0.8]
|
|
|
|
mean value: 0.77
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.5 1. 0.71428571 0.71428571 0.33333333
|
|
0.5 0.71428571 0.625 0.6 ]
|
|
|
|
mean value: 0.653452380952381
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 35
|
|
|
|
mean value: 35.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: TP
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.03
|
|
|
|
Accuracy on Blind test: 0.52
|
|
|
|
Running classifier: 21
|
|
Model_name: Gradient Boosting
|
|
Model func: GradientBoostingClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GradientBoostingClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.21461725 0.18806386 0.1732049 0.16499853 0.17371368 0.16821599
|
|
0.14020443 0.19140482 0.18640518 0.16943955]
|
|
|
|
mean value: 0.1770268201828003
|
|
|
|
key: score_time
|
|
value: [0.00959754 0.00923371 0.00887346 0.00884986 0.00876808 0.00874138
|
|
0.00899005 0.00879264 0.00887346 0.00937819]
|
|
|
|
mean value: 0.009009838104248047
|
|
|
|
key: test_mcc
|
|
value: [1. 0.81649658 1. 0.81649658 1. 0.81649658
|
|
0.81649658 1. 0.65465367 1. ]
|
|
|
|
mean value: 0.8920639994418881
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.90909091 1. 0.90909091 1. 0.88888889
|
|
0.88888889 1. 0.83333333 1. ]
|
|
|
|
mean value: 0.942929292929293
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 1. 0.83333333 1. 1.
|
|
1. 1. 0.71428571 1. ]
|
|
|
|
mean value: 0.9380952380952381
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 1. 1. 0.8 0.8 1. 1. 1. ]
|
|
|
|
mean value: 0.96
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9 1. 0.9 1. 0.9 0.9 1. 0.8 1. ]
|
|
|
|
mean value: 0.9400000000000001
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.9 1. 0.9 1. 0.9 0.9 1. 0.8 1. ]
|
|
|
|
mean value: 0.9400000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.83333333 1. 0.83333333 1. 0.8
|
|
0.8 1. 0.71428571 1. ]
|
|
|
|
mean value: 0.8980952380952381
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 46
|
|
|
|
mean value: 46.0
|
|
|
|
key: FP
|
|
value: 2
|
|
|
|
mean value: 2.0
|
|
|
|
key: FN
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: TP
|
|
value: 48
|
|
|
|
mean value: 48.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.95
|
|
|
|
Accuracy on Blind test: 0.98
|
|
|
|
Running classifier: 22
|
|
Model_name: QDA
|
|
Model func: QuadraticDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', QuadraticDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.01019168 0.01433969 0.01385474 0.01470232 0.0138483 0.01425314
|
|
0.01388431 0.01397109 0.0143342 0.01450634]
|
|
|
|
mean value: 0.013788580894470215
|
|
|
|
key: score_time
|
|
value: [0.01203132 0.01153278 0.0118506 0.01162958 0.01282692 0.01163149
|
|
0.01344275 0.01311135 0.01172233 0.01355076]
|
|
|
|
mean value: 0.012332987785339356
|
|
|
|
key: test_mcc
|
|
value: [1. 0.65465367 0.65465367 1. 1. 0.5
|
|
0.65465367 0.65465367 1. 0.65465367]
|
|
|
|
mean value: 0.7773268353539886
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
[1. 0.75 0.75 1. 1. 0.57142857
|
|
0.75 0.75 1. 0.75 ]
|
|
|
|
mean value: 0.8321428571428571
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.6 0.6 1. 1. 0.4 0.6 0.6 1. 0.6]
|
|
|
|
mean value: 0.74
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.8 0.8 1. 1. 0.7 0.8 0.8 1. 0.8]
|
|
|
|
mean value: 0.8700000000000001
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.8 0.8 1. 1. 0.7 0.8 0.8 1. 0.8]
|
|
|
|
mean value: 0.8700000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.6 0.6 1. 1. 0.4 0.6 0.6 1. 0.6]
|
|
|
|
mean value: 0.74
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: FP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: FN
|
|
value: 0
|
|
|
|
mean value: 0.0
|
|
|
|
key: TP
|
|
value: 37
|
|
|
|
mean value: 37.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.0
|
|
|
|
Accuracy on Blind test: 0.65
|
|
|
|
Running classifier: 23
|
|
Model_name: Ridge Classifier
|
|
Model func: RidgeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02094769 0.01264119 0.01544523 0.02882671 0.03682876 0.02940774
|
|
0.0313282 0.03155279 0.05608463 0.03202128]
|
|
|
|
mean value: 0.029508423805236817
|
|
|
|
key: score_time
|
|
value: [0.01188612 0.01171374 0.0116632 0.01729679 0.01832533 0.02263856
|
|
0.02219057 0.0126164 0.01564741 0.01932001]
|
|
|
|
mean value: 0.01632981300354004
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.6 1. 0.5 0.65465367 0.6
|
|
0.6 0.21821789 0. 0.81649658]
|
|
|
|
mean value: 0.5805864722799422
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.97801929 0.97801929 1. ]
|
|
|
|
mean value: 0.9956038587687303
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.8 1. 0.76923077 0.83333333 0.8
|
|
0.8 0.66666667 0.54545455 0.88888889]
|
|
|
|
mean value: 0.8012665112665113
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.98901099 0.98876404 1. ]
|
|
|
|
mean value: 0.997777503395481
|
|
|
|
key: test_precision
|
|
value: [0.83333333 0.8 1. 0.625 0.71428571 0.8
|
|
0.8 0.57142857 0.5 1. ]
|
|
|
|
mean value: 0.7644047619047619
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.97826087 1. 1. ]
|
|
|
|
mean value: 0.9978260869565216
|
|
|
|
key: test_recall
|
|
value: [1. 0.8 1. 1. 1. 0.8 0.8 0.8 0.6 0.8]
|
|
|
|
mean value: 0.86
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 1. 0.97777778 1. ]
|
|
|
|
mean value: 0.9977777777777778
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.8 1. 0.7 0.8 0.8 0.8 0.6 0.5 0.9]
|
|
|
|
mean value: 0.78
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.98888889 0.98888889 1. ]
|
|
|
|
mean value: 0.9977777777777778
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.8 1. 0.7 0.8 0.8 0.8 0.6 0.5 0.9]
|
|
|
|
mean value: 0.78
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.98888889 0.98888889 1. ]
|
|
|
|
mean value: 0.9977777777777778
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.66666667 1. 0.625 0.71428571 0.66666667
|
|
0.66666667 0.5 0.375 0.8 ]
|
|
|
|
mean value: 0.6847619047619047
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.97826087 0.97777778 1. ]
|
|
|
|
mean value: 0.9956038647342995
|
|
|
|
key: TN
|
|
value: 35
|
|
|
|
mean value: 35.0
|
|
|
|
key: FP
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: FN
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: TP
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.16
|
|
|
|
Accuracy on Blind test: 0.62
|
|
|
|
Running classifier: 24
|
|
Model_name: Ridge ClassifierCV
|
|
Model func: RidgeClassifierCV(cv=10)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifierCV(cv=10))])
|
|
|
|
key: fit_time
|
|
value: [0.09823203 0.18534827 0.08709049 0.1154747 0.18197918 0.18414021
|
|
0.16416526 0.08809209 0.2873745 0.2193656 ]
|
|
|
|
mean value: 0.1611262321472168
|
|
|
|
key: score_time
|
|
value: [0.02172661 0.01154947 0.01647282 0.02121782 0.02233553 0.02070236
|
|
0.01157141 0.01160717 0.02128386 0.01674223]
|
|
|
|
mean value: 0.017520928382873537
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.65465367 0.81649658 0.5 0.81649658 0.21821789
|
|
0.6 0.40824829 0. 0.65465367]
|
|
|
|
mean value: 0.5485263264898987
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.86666667 0.97801929 1. ]
|
|
|
|
mean value: 0.9844685960510319
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.83333333 0.90909091 0.76923077 0.90909091 0.66666667
|
|
0.8 0.72727273 0.54545455 0.75 ]
|
|
|
|
mean value: 0.7819230769230769
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.93333333 0.98876404 1. ]
|
|
|
|
mean value: 0.9922097378277155
|
|
|
|
key: test_precision
|
|
value: [0.83333333 0.71428571 0.83333333 0.625 0.83333333 0.57142857
|
|
0.8 0.66666667 0.5 1. ]
|
|
|
|
mean value: 0.7377380952380953
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.93333333 1. 1. ]
|
|
|
|
mean value: 0.9933333333333334
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 1. 1. 0.8 0.8 0.8 0.6 0.6]
|
|
|
|
mean value: 0.86
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.93333333 0.97777778 1. ]
|
|
|
|
mean value: 0.9911111111111112
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.8 0.9 0.7 0.9 0.6 0.8 0.7 0.5 0.8]
|
|
|
|
mean value: 0.76
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.93333333 0.98888889 1. ]
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:282: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
ros_CV['Data_source'] = 'CV'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:283: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
ros_CV['Resampling'] = rs_ros
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:288: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
ros_BT['Data_source'] = 'BT'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:289: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
ros_BT['Resampling'] = rs_ros
|
|
|
|
mean value: 0.9922222222222222
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.8 0.9 0.7 0.9 0.6 0.8 0.7 0.5 0.8]
|
|
|
|
mean value: 0.76
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.93333333 0.98888889 1. ]
|
|
|
|
mean value: 0.9922222222222222
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.71428571 0.83333333 0.625 0.83333333 0.5
|
|
0.66666667 0.57142857 0.375 0.6 ]
|
|
|
|
mean value: 0.6552380952380952
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.875 0.97777778 1. ]
|
|
|
|
mean value: 0.9852777777777778
|
|
|
|
key: TN
|
|
value: 33
|
|
|
|
mean value: 33.0
|
|
|
|
key: FP
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: FN
|
|
value: 17
|
|
|
|
mean value: 17.0
|
|
|
|
key: TP
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.03
|
|
|
|
Accuracy on Blind test: 0.52
|
|
|
|
PASS: sorting df by score that is mapped onto the order I want
|
|
|
|
==============================================================
|
|
|
|
Running several classification models (n): 24
|
|
List of models:
|
|
('Logistic Regression', LogisticRegression(random_state=42))
|
|
('Logistic RegressionCV', LogisticRegressionCV(random_state=42))
|
|
('Gaussian NB', GaussianNB())
|
|
('Naive Bayes', BernoulliNB())
|
|
('K-Nearest Neighbors', KNeighborsClassifier())
|
|
('SVC', SVC(random_state=42))
|
|
('MLP', MLPClassifier(max_iter=500, random_state=42))
|
|
('Decision Tree', DecisionTreeClassifier(random_state=42))
|
|
('Extra Trees', ExtraTreesClassifier(random_state=42))
|
|
('Extra Tree', ExtraTreeClassifier(random_state=42))
|
|
('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42))
|
|
('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42))
|
|
('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0))
|
|
('LDA', LinearDiscriminantAnalysis())
|
|
('Multinomial', MultinomialNB())
|
|
('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42))
|
|
('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42))
|
|
('AdaBoost Classifier', AdaBoostClassifier(random_state=42))
|
|
('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))
|
|
('Gaussian Process', GaussianProcessClassifier(random_state=42))
|
|
('Gradient Boosting', GradientBoostingClassifier(random_state=42))
|
|
('QDA', QuadraticDiscriminantAnalysis())
|
|
('Ridge Classifier', RidgeClassifier(random_state=42))
|
|
('Ridge ClassifierCV', RidgeClassifierCV(cv=10))
|
|
|
|
================================================================
|
|
|
|
|
|
Running classifier: 1
|
|
Model_name: Logistic Regression
|
|
Model func: LogisticRegression(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegression(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02247024 0.02279019 0.0214591 0.02027655 0.02126312 0.02295542
|
|
0.02090645 0.02397966 0.02241564 0.02300167]
|
|
|
|
mean value: 0.022151803970336913
|
|
|
|
key: score_time
|
|
value: [0.0113349 0.01142359 0.01140141 0.01136398 0.01136851 0.01148033
|
|
0.01136971 0.01144123 0.01137614 0.01134181]
|
|
|
|
mean value: 0.011390161514282227
|
|
|
|
key: test_mcc
|
|
value: [0.4472136 1. 0. 0. 0. 1.
|
|
0. 0. 0.40824829 1. ]
|
|
|
|
mean value: 0.3855461885963821
|
|
|
|
key: train_mcc
|
|
value: [0.88527041 0.96225045 0.92307692 1. 0.92307692 0.88527041
|
|
0.96225045 0.92307692 0.92450142 0.96291111]
|
|
|
|
mean value: 0.9351685024695351
|
|
|
|
key: test_fscore
|
|
value: [0.5 1. 0.66666667 0.4 0.57142857 1.
|
|
0.4 0.57142857 0.66666667 1. ]
|
|
|
|
mean value: 0.6776190476190476
|
|
|
|
key: train_fscore
|
|
value: [0.94117647 0.98039216 0.96153846 1. 0.96153846 0.94339623
|
|
0.98039216 0.96153846 0.96296296 0.98039216]
|
|
|
|
mean value: 0.9673327515169913
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.5 0.5 0.5 1. 0.5 0.5 0.5 1. ]
|
|
|
|
mean value: 0.7
|
|
|
|
key: train_precision
|
|
value: [0.96 1. 0.96153846 1. 0.96153846 0.92592593
|
|
1. 0.96153846 0.96296296 1. ]
|
|
|
|
mean value: 0.9733504273504273
|
|
|
|
key: test_recall
|
|
value: [0.33333333 1. 1. 0.33333333 0.66666667 1.
|
|
0.33333333 0.66666667 1. 1. ]
|
|
|
|
mean value: 0.7333333333333333
|
|
|
|
key: train_recall
|
|
value: [0.92307692 0.96153846 0.96153846 1. 0.96153846 0.96153846
|
|
0.96153846 0.96153846 0.96296296 0.96153846]
|
|
|
|
mean value: 0.9616809116809117
|
|
|
|
key: test_accuracy
|
|
value: [0.66666667 1. 0.5 0.5 0.5 1.
|
|
0.5 0.5 0.6 1. ]
|
|
|
|
mean value: 0.6766666666666665
|
|
|
|
key: train_accuracy
|
|
value: [0.94230769 0.98076923 0.96153846 1. 0.96153846 0.94230769
|
|
0.98076923 0.96153846 0.96226415 0.98113208]
|
|
|
|
mean value: 0.9674165457184325
|
|
|
|
key: test_roc_auc
|
|
value: [0.66666667 1. 0.5 0.5 0.5 1.
|
|
0.5 0.5 0.66666667 1. ]
|
|
|
|
mean value: 0.6833333333333333
|
|
|
|
key: train_roc_auc
|
|
value: [0.94230769 0.98076923 0.96153846 1. 0.96153846 0.94230769
|
|
0.98076923 0.96153846 0.96225071 0.98076923]
|
|
|
|
mean value: 0.9673789173789175
|
|
|
|
key: test_jcc
|
|
value: [0.33333333 1. 0.5 0.25 0.4 1.
|
|
0.25 0.4 0.5 1. ]
|
|
|
|
mean value: 0.5633333333333332
|
|
|
|
key: train_jcc
|
|
value: [0.88888889 0.96153846 0.92592593 1. 0.92592593 0.89285714
|
|
0.96153846 0.92592593 0.92857143 0.96153846]
|
|
|
|
mean value: 0.9372710622710623
|
|
|
|
key: TN
|
|
value: 18
|
|
|
|
mean value: 18.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: TP
|
|
value: 21
|
|
|
|
mean value: 21.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.17
|
|
|
|
Accuracy on Blind test: 0.57
|
|
|
|
Running classifier: 2
|
|
Model_name: Logistic RegressionCV
|
|
Model func: LogisticRegressionCV(random_state=42)
|
|
|
|
Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegressionCV(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.3368907 0.35755754 0.36199379 0.30242968 0.30465245 0.3230257
|
|
0.34169865 0.33659816 0.40250373 0.39995384]
|
|
|
|
mean value: 0.3467304229736328
|
|
|
|
key: score_time
|
|
value: [0.01330423 0.01177645 0.01205063 0.01192522 0.01168323 0.01168942
|
|
0.01167512 0.01225448 0.01178122 0.01063108]
|
|
|
|
mean value: 0.011877107620239257
|
|
|
|
key: test_mcc
|
|
value: [0.70710678 1. 0.70710678 0. 0. 1.
|
|
0. 0.4472136 1. 1. ]
|
|
|
|
mean value: 0.5861427157873053
|
|
|
|
key: train_mcc
|
|
value: [0.96225045 0.96225045 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9924500897298753
|
|
|
|
key: test_fscore
|
|
value: [0.8 1. 0.85714286 0.4 0.57142857 1.
|
|
0.4 0.75 1. 1. ]
|
|
|
|
mean value: 0.7778571428571428
|
|
|
|
key: train_fscore
|
|
value: [0.98039216 0.98039216 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.996078431372549
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.75 0.5 0.5 1. 0.5 0.6 1. 1. ]
|
|
|
|
mean value: 0.7849999999999999
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 1. 1. 0.33333333 0.66666667 1.
|
|
0.33333333 1. 1. 1. ]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_recall
|
|
value: [0.96153846 0.96153846 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9923076923076923
|
|
|
|
key: test_accuracy
|
|
value: [0.83333333 1. 0.83333333 0.5 0.5 1.
|
|
0.5 0.66666667 1. 1. ]
|
|
|
|
mean value: 0.7833333333333334
|
|
|
|
key: train_accuracy
|
|
value: [0.98076923 0.98076923 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9961538461538462
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 1. 0.83333333 0.5 0.5 1.
|
|
0.5 0.66666667 1. 1. ]
|
|
|
|
mean value: 0.7833333333333334
|
|
|
|
key: train_roc_auc
|
|
value: [0.98076923 0.98076923 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9961538461538462
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 1. 0.75 0.25 0.4 1.
|
|
0.25 0.6 1. 1. ]
|
|
|
|
mean value: 0.6916666666666667
|
|
|
|
key: train_jcc
|
|
value: [0.96153846 0.96153846 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9923076923076923
|
|
|
|
key: TN
|
|
value: 22
|
|
|
|
mean value: 22.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 23
|
|
|
|
mean value: 23.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.25
|
|
|
|
Accuracy on Blind test: 0.62
|
|
|
|
Running classifier: 3
|
|
Model_name: Gaussian NB
|
|
Model func: GaussianNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianNB())])
|
|
|
|
key: fit_time
|
|
value: [0.01113558 0.01096749 0.00920558 0.00855684 0.00815439 0.00800776
|
|
0.00789642 0.00879645 0.00837684 0.00784945]
|
|
|
|
mean value: 0.008894681930541992
|
|
|
|
key: score_time
|
|
value: [0.01130128 0.01066971 0.00945187 0.0082593 0.00822663 0.00822568
|
|
0.00823283 0.00896049 0.00820088 0.00893879]
|
|
|
|
mean value: 0.00904674530029297
|
|
|
|
key: test_mcc
|
|
value: [-0.33333333 0.70710678 0. -0.33333333 0. 0.4472136
|
|
-0.4472136 -0.70710678 -0.16666667 0.40824829]
|
|
|
|
mean value: -0.04250850428694703
|
|
|
|
key: train_mcc
|
|
value: [0.50336201 0.64676167 0.69230769 0.58080232 0.77151675 0.75878691
|
|
0.62279916 0.81312325 0.67348073 0.51261937]
|
|
|
|
mean value: 0.6575559848126133
|
|
|
|
key: test_fscore
|
|
value: [0.33333333 0.85714286 0.66666667 0.33333333 0.66666667 0.75
|
|
0.5 0. 0.4 0.5 ]
|
|
|
|
mean value: 0.5007142857142858
|
|
|
|
key: train_fscore
|
|
value: [0.76363636 0.83333333 0.84615385 0.8 0.88888889 0.88135593
|
|
0.82142857 0.89795918 0.81632653 0.77419355]
|
|
|
|
mean value: 0.8323276198317204
|
|
|
|
key: test_precision
|
|
value: [0.33333333 0.75 0.5 0.33333333 0.5 0.6
|
|
0.4 0. 0.33333333 1. ]
|
|
|
|
mean value: 0.475
|
|
|
|
key: train_precision
|
|
value: [0.72413793 0.73529412 0.84615385 0.75862069 0.85714286 0.78787879
|
|
0.76666667 0.95652174 0.90909091 0.66666667]
|
|
|
|
mean value: 0.8008174211066882
|
|
|
|
key: test_recall
|
|
value: [0.33333333 1. 1. 0.33333333 1. 1.
|
|
0.66666667 0. 0.5 0.33333333]
|
|
|
|
mean value: 0.6166666666666666
|
|
|
|
key: train_recall
|
|
value: [0.80769231 0.96153846 0.84615385 0.84615385 0.92307692 1.
|
|
0.88461538 0.84615385 0.74074074 0.92307692]
|
|
|
|
mean value: 0.8779202279202278
|
|
|
|
key: test_accuracy
|
|
value: [0.33333333 0.83333333 0.5 0.33333333 0.5 0.66666667
|
|
0.33333333 0.16666667 0.4 0.6 ]
|
|
|
|
mean value: 0.4666666666666666
|
|
|
|
key: train_accuracy
|
|
value: [0.75 0.80769231 0.84615385 0.78846154 0.88461538 0.86538462
|
|
0.80769231 0.90384615 0.83018868 0.73584906]
|
|
|
|
mean value: 0.8219883889695211
|
|
|
|
key: test_roc_auc
|
|
value: [0.33333333 0.83333333 0.5 0.33333333 0.5 0.66666667
|
|
0.33333333 0.16666667 0.41666667 0.66666667]
|
|
|
|
mean value: 0.4750000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [0.75 0.80769231 0.84615385 0.78846154 0.88461538 0.86538462
|
|
0.80769231 0.90384615 0.83190883 0.73931624]
|
|
|
|
mean value: 0.8225071225071225
|
|
|
|
key: test_jcc
|
|
value: [0.2 0.75 0.5 0.2 0.5 0.6
|
|
0.33333333 0. 0.25 0.33333333]
|
|
|
|
mean value: 0.36666666666666664
|
|
|
|
key: train_jcc
|
|
value: [0.61764706 0.71428571 0.73333333 0.66666667 0.8 0.78787879
|
|
0.6969697 0.81481481 0.68965517 0.63157895]
|
|
|
|
mean value: 0.7152830192554758
|
|
|
|
key: TN
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: FP
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: FN
|
|
value: 20
|
|
|
|
mean value: 20.0
|
|
|
|
key: TP
|
|
value: 18
|
|
|
|
mean value: 18.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.39
|
|
|
|
Accuracy on Blind test: 0.65
|
|
|
|
Running classifier: 4
|
|
Model_name: Naive Bayes
|
|
Model func: BernoulliNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', BernoulliNB())])
|
|
|
|
key: fit_time
|
|
value: [0.00813365 0.00844741 0.00814033 0.00801945 0.00898266 0.00798416
|
|
0.00815678 0.00803804 0.00806212 0.00834966]
|
|
|
|
mean value: 0.008231425285339355
|
|
|
|
key: score_time
|
|
value: [0.00833273 0.00839019 0.00825405 0.00831342 0.00851583 0.00821781
|
|
0.00835562 0.00828004 0.00859928 0.00834107]
|
|
|
|
mean value: 0.008360004425048828
|
|
|
|
key: test_mcc
|
|
value: [ 0. 0.33333333 -0.33333333 0. 0.33333333 0.33333333
|
|
-0.33333333 -0.70710678 -0.16666667 0.16666667]
|
|
|
|
mean value: -0.03737734478532143
|
|
|
|
key: train_mcc
|
|
value: [0.57735027 0.73131034 0.61538462 0.65433031 0.84615385 0.70064905
|
|
0.58080232 0.6172134 0.58547009 0.54921597]
|
|
|
|
mean value: 0.6457880196732791
|
|
|
|
key: test_fscore
|
|
value: [0.57142857 0.66666667 0.33333333 0.4 0.66666667 0.66666667
|
|
0.33333333 0. 0.4 0.66666667]
|
|
|
|
mean value: 0.4704761904761905
|
|
|
|
key: train_fscore
|
|
value: [0.79245283 0.86792453 0.80769231 0.83018868 0.92307692 0.85714286
|
|
0.8 0.81481481 0.79245283 0.77777778]
|
|
|
|
mean value: 0.8263523548429209
|
|
|
|
key: test_precision
|
|
value: [0.5 0.66666667 0.33333333 0.5 0.66666667 0.66666667
|
|
0.33333333 0. 0.33333333 0.66666667]
|
|
|
|
mean value: 0.4666666666666666
|
|
|
|
key: train_precision
|
|
value: [0.77777778 0.85185185 0.80769231 0.81481481 0.92307692 0.8
|
|
0.75862069 0.78571429 0.80769231 0.75 ]
|
|
|
|
mean value: 0.8077240958275441
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.66666667 0.33333333 0.33333333 0.66666667 0.66666667
|
|
0.33333333 0. 0.5 0.66666667]
|
|
|
|
mean value: 0.4833333333333333
|
|
|
|
key: train_recall
|
|
value: [0.80769231 0.88461538 0.80769231 0.84615385 0.92307692 0.92307692
|
|
0.84615385 0.84615385 0.77777778 0.80769231]
|
|
|
|
mean value: 0.847008547008547
|
|
|
|
key: test_accuracy
|
|
value: [0.5 0.66666667 0.33333333 0.5 0.66666667 0.66666667
|
|
0.33333333 0.16666667 0.4 0.6 ]
|
|
|
|
mean value: 0.4833333333333333
|
|
|
|
key: train_accuracy
|
|
value: [0.78846154 0.86538462 0.80769231 0.82692308 0.92307692 0.84615385
|
|
0.78846154 0.80769231 0.79245283 0.77358491]
|
|
|
|
mean value: 0.8219883889695211
|
|
|
|
key: test_roc_auc
|
|
value: [0.5 0.66666667 0.33333333 0.5 0.66666667 0.66666667
|
|
0.33333333 0.16666667 0.41666667 0.58333333]
|
|
|
|
mean value: 0.4833333333333333
|
|
|
|
key: train_roc_auc
|
|
value: [0.78846154 0.86538462 0.80769231 0.82692308 0.92307692 0.84615385
|
|
0.78846154 0.80769231 0.79273504 0.77421652]
|
|
|
|
mean value: 0.8220797720797719
|
|
|
|
key: test_jcc
|
|
value: [0.4 0.5 0.2 0.25 0.5 0.5 0.2 0. 0.25 0.5 ]
|
|
|
|
mean value: 0.32999999999999996
|
|
|
|
key: train_jcc
|
|
value: [0.65625 0.76666667 0.67741935 0.70967742 0.85714286 0.75
|
|
0.66666667 0.6875 0.65625 0.63636364]
|
|
|
|
mean value: 0.7063936601033376
|
|
|
|
key: TN
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: FP
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: FN
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: TP
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.02
|
|
|
|
Accuracy on Blind test: 0.45
|
|
|
|
Running classifier: 5
|
|
Model_name: K-Nearest Neighbors
|
|
Model func: KNeighborsClassifier()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', KNeighborsClassifier())])
|
|
|
|
key: fit_time
|
|
value: [0.01033497 0.00774837 0.00769258 0.00766277 0.00772595 0.00759411
|
|
0.00768042 0.00764728 0.00764275 0.00769997]
|
|
|
|
mean value: 0.007942914962768555
|
|
|
|
key: score_time
|
|
value: [0.01190901 0.00875068 0.00876427 0.0087657 0.00875902 0.0089035
|
|
0.00881863 0.00920081 0.00875378 0.00871611]
|
|
|
|
mean value: 0.009134149551391602
|
|
|
|
key: test_mcc
|
|
value: [ 0.70710678 0.70710678 0. -0.4472136 0.4472136 0.4472136
|
|
-0.4472136 0. 0.66666667 0.66666667]
|
|
|
|
mean value: 0.27475468957064286
|
|
|
|
key: train_mcc
|
|
value: [0.34641016 0.34641016 0.54006172 0.65433031 0.50951017 0.54006172
|
|
0.65433031 0.57735027 0.43447293 0.50997151]
|
|
|
|
mean value: 0.511290926764467
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.8 0.57142857 0. 0.75 0.5
|
|
0.5 0.57142857 0.8 0.8 ]
|
|
|
|
mean value: 0.6092857142857143
|
|
|
|
key: train_fscore
|
|
value: [0.66666667 0.66666667 0.76 0.82352941 0.72340426 0.76
|
|
0.82352941 0.78431373 0.71698113 0.75471698]
|
|
|
|
mean value: 0.7479808250879637
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.5 0. 0.6 1.
|
|
0.4 0.5 0.66666667 1. ]
|
|
|
|
mean value: 0.6666666666666667
|
|
|
|
key: train_precision
|
|
value: [0.68 0.68 0.79166667 0.84 0.80952381 0.79166667
|
|
0.84 0.8 0.73076923 0.74074074]
|
|
|
|
mean value: 0.7704367114367114
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.66666667 0.66666667 0. 1. 0.33333333
|
|
0.66666667 0.66666667 1. 0.66666667]
|
|
|
|
mean value: 0.6333333333333333
|
|
|
|
key: train_recall
|
|
value: [0.65384615 0.65384615 0.73076923 0.80769231 0.65384615 0.73076923
|
|
0.80769231 0.76923077 0.7037037 0.76923077]
|
|
|
|
mean value: 0.728062678062678
|
|
|
|
key: test_accuracy
|
|
value: [0.83333333 0.83333333 0.5 0.33333333 0.66666667 0.66666667
|
|
0.33333333 0.5 0.8 0.8 ]
|
|
|
|
mean value: 0.6266666666666666
|
|
|
|
key: train_accuracy
|
|
value: [0.67307692 0.67307692 0.76923077 0.82692308 0.75 0.76923077
|
|
0.82692308 0.78846154 0.71698113 0.75471698]
|
|
|
|
mean value: 0.7548621190130624
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 0.83333333 0.5 0.33333333 0.66666667 0.66666667
|
|
0.33333333 0.5 0.83333333 0.83333333]
|
|
|
|
mean value: 0.6333333333333333
|
|
|
|
key: train_roc_auc
|
|
value: [0.67307692 0.67307692 0.76923077 0.82692308 0.75 0.76923077
|
|
0.82692308 0.78846154 0.71723647 0.75498575]
|
|
|
|
mean value: 0.7549145299145299
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.66666667 0.4 0. 0.6 0.33333333
|
|
0.33333333 0.4 0.66666667 0.66666667]
|
|
|
|
mean value: 0.47333333333333344
|
|
|
|
key: train_jcc
|
|
value: [0.5 0.5 0.61290323 0.7 0.56666667 0.61290323
|
|
0.7 0.64516129 0.55882353 0.60606061]
|
|
|
|
mean value: 0.6002518544074522
|
|
|
|
key: TN
|
|
value: 18
|
|
|
|
mean value: 18.0
|
|
|
|
key: FP
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: FN
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: TP
|
|
value: 18
|
|
|
|
mean value: 18.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.1
|
|
|
|
Accuracy on Blind test: 0.55
|
|
|
|
Running classifier: 6
|
|
Model_name: SVC
|
|
Model func: SVC(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SVC(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00983238 0.00817156 0.00807023 0.00817275 0.00810385 0.00809503
|
|
0.00810146 0.00812173 0.00830746 0.00820637]
|
|
|
|
mean value: 0.008318281173706055
|
|
|
|
key: score_time
|
|
value: [0.00890064 0.00827098 0.00825214 0.00835896 0.00818491 0.00825906
|
|
0.00822496 0.00825191 0.00823522 0.00826621]
|
|
|
|
mean value: 0.008320498466491699
|
|
|
|
key: test_mcc
|
|
value: [-0.70710678 0.70710678 0. -0.4472136 -0.4472136 0.70710678
|
|
-0.4472136 -0.4472136 0. 0.40824829]
|
|
|
|
mean value: -0.06734993103494209
|
|
|
|
key: train_mcc
|
|
value: [0.58789635 0.77151675 0.80829038 0.84615385 0.84615385 0.84866842
|
|
0.80829038 0.77849894 0.71546507 0.81612228]
|
|
|
|
mean value: 0.7827056268909882
|
|
|
|
key: test_fscore
|
|
value: [0.28571429 0.8 0.66666667 0. 0.5 0.8
|
|
0.5 0.5 0.57142857 0.5 ]
|
|
|
|
mean value: 0.5123809523809524
|
|
|
|
key: train_fscore
|
|
value: [0.80701754 0.88 0.90566038 0.92307692 0.92307692 0.92592593
|
|
0.90566038 0.89285714 0.86666667 0.89795918]
|
|
|
|
mean value: 0.8927901063853682
|
|
|
|
key: test_precision
|
|
value: [0.25 1. 0.5 0. 0.4 1. 0.4 0.4 0.4 1. ]
|
|
|
|
mean value: 0.535
|
|
|
|
key: train_precision
|
|
value: [0.74193548 0.91666667 0.88888889 0.92307692 0.92307692 0.89285714
|
|
0.88888889 0.83333333 0.78787879 0.95652174]
|
|
|
|
mean value: 0.8753124777668958
|
|
|
|
key: test_recall
|
|
value: [0.33333333 0.66666667 1. 0. 0.66666667 0.66666667
|
|
0.66666667 0.66666667 1. 0.33333333]
|
|
|
|
mean value: 0.5999999999999999
|
|
|
|
key: train_recall
|
|
value: [0.88461538 0.84615385 0.92307692 0.92307692 0.92307692 0.96153846
|
|
0.92307692 0.96153846 0.96296296 0.84615385]
|
|
|
|
mean value: 0.9155270655270658
|
|
|
|
key: test_accuracy
|
|
value: [0.16666667 0.83333333 0.5 0.33333333 0.33333333 0.83333333
|
|
0.33333333 0.33333333 0.4 0.6 ]
|
|
|
|
mean value: 0.4666666666666667
|
|
|
|
key: train_accuracy
|
|
value: [0.78846154 0.88461538 0.90384615 0.92307692 0.92307692 0.92307692
|
|
0.90384615 0.88461538 0.8490566 0.90566038]
|
|
|
|
mean value: 0.888933236574746
|
|
|
|
key: test_roc_auc
|
|
value: [0.16666667 0.83333333 0.5 0.33333333 0.33333333 0.83333333
|
|
0.33333333 0.33333333 0.5 0.66666667]
|
|
|
|
mean value: 0.4833333333333333
|
|
|
|
key: train_roc_auc
|
|
value: [0.78846154 0.88461538 0.90384615 0.92307692 0.92307692 0.92307692
|
|
0.90384615 0.88461538 0.8468661 0.9045584 ]
|
|
|
|
mean value: 0.8886039886039887
|
|
|
|
key: test_jcc
|
|
value: [0.16666667 0.66666667 0.5 0. 0.33333333 0.66666667
|
|
0.33333333 0.33333333 0.4 0.33333333]
|
|
|
|
mean value: 0.37333333333333335
|
|
|
|
key: train_jcc
|
|
value: [0.67647059 0.78571429 0.82758621 0.85714286 0.85714286 0.86206897
|
|
0.82758621 0.80645161 0.76470588 0.81481481]
|
|
|
|
mean value: 0.807968427761662
|
|
|
|
key: TN
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FP
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: FN
|
|
value: 19
|
|
|
|
mean value: 19.0
|
|
|
|
key: TP
|
|
value: 17
|
|
|
|
mean value: 17.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.17
|
|
|
|
Accuracy on Blind test: 0.52
|
|
|
|
Running classifier: 7
|
|
Model_name: MLP
|
|
Model func: MLPClassifier(max_iter=500, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MLPClassifier(max_iter=500, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.27118325 0.26555204 0.26822782 0.3543098 0.23144531 0.2879324
|
|
0.27575207 0.28139663 0.31252456 0.28410864]
|
|
|
|
mean value: 0.2832432508468628
|
|
|
|
key: score_time
|
|
value: [0.01188612 0.01264691 0.01187563 0.0120225 0.01204729 0.01216674
|
|
0.01212144 0.01220417 0.01201892 0.0119679 ]
|
|
|
|
mean value: 0.012095761299133301
|
|
|
|
key: test_mcc
|
|
value: [ 0.33333333 1. 0.4472136 -0.4472136 0. 1.
|
|
0. -0.33333333 -0.16666667 1. ]
|
|
|
|
mean value: 0.2833333333333333
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.66666667 1. 0.75 0. 0.57142857 1.
|
|
0.4 0.33333333 0.4 1. ]
|
|
|
|
mean value: 0.6121428571428572
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.66666667 1. 0.6 0. 0.5 1.
|
|
0.5 0.33333333 0.33333333 1. ]
|
|
|
|
mean value: 0.5933333333333333
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 1. 1. 0. 0.66666667 1.
|
|
0.33333333 0.33333333 0.5 1. ]
|
|
|
|
mean value: 0.65
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.66666667 1. 0.66666667 0.33333333 0.5 1.
|
|
0.5 0.33333333 0.4 1. ]
|
|
|
|
mean value: 0.64
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.66666667 1. 0.66666667 0.33333333 0.5 1.
|
|
0.5 0.33333333 0.41666667 1. ]
|
|
|
|
mean value: 0.6416666666666667
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.5 1. 0.6 0. 0.4 1. 0.25 0.2 0.25 1. ]
|
|
|
|
mean value: 0.52
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 18
|
|
|
|
mean value: 18.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: TP
|
|
value: 19
|
|
|
|
mean value: 19.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.18
|
|
|
|
Accuracy on Blind test: 0.6
|
|
|
|
Running classifier: 8
|
|
Model_name: Decision Tree
|
|
Model func: DecisionTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', DecisionTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01426125 0.01220822 0.00889158 0.0087297 0.00895548 0.00863051
|
|
0.00851607 0.00860262 0.00862265 0.00863194]
|
|
|
|
mean value: 0.009605002403259278
|
|
|
|
key: score_time
|
|
value: [0.01187658 0.0103178 0.00851917 0.00812912 0.00841165 0.00818539
|
|
0.00814438 0.00818205 0.0081892 0.0081985 ]
|
|
|
|
mean value: 0.008815383911132813
|
|
|
|
key: test_mcc
|
|
value: [0.70710678 1. 0. 0.4472136 0. 0.70710678
|
|
1. 0.70710678 0.66666667 1. ]
|
|
|
|
mean value: 0.6235200605726268
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.8 1. 0.57142857 0.5 0.57142857 0.85714286
|
|
1. 0.85714286 0.8 1. ]
|
|
|
|
mean value: 0.7957142857142857
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.5 1. 0.5 0.75
|
|
1. 0.75 0.66666667 1. ]
|
|
|
|
mean value: 0.8166666666666668
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 1. 0.66666667 0.33333333 0.66666667 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.8333333333333333
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.83333333 1. 0.5 0.66666667 0.5 0.83333333
|
|
1. 0.83333333 0.8 1. ]
|
|
|
|
mean value: 0.7966666666666666
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 1. 0.5 0.66666667 0.5 0.83333333
|
|
1. 0.83333333 0.83333333 1. ]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 1. 0.4 0.33333333 0.4 0.75
|
|
1. 0.75 0.66666667 1. ]
|
|
|
|
mean value: 0.6966666666666667
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 22
|
|
|
|
mean value: 22.0
|
|
|
|
key: FP
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 24
|
|
|
|
mean value: 24.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.95
|
|
|
|
Accuracy on Blind test: 0.98
|
|
|
|
Running classifier: 9
|
|
Model_name: Extra Trees
|
|
Model func: ExtraTreesClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreesClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.08186579 0.07851267 0.07875395 0.08117771 0.07774711 0.07864809
|
|
0.07936549 0.08071136 0.07908726 0.07877016]
|
|
|
|
mean value: 0.07946395874023438
|
|
|
|
key: score_time
|
|
value: [0.01654387 0.01662087 0.01674056 0.01655841 0.01825023 0.01663804
|
|
0.0165503 0.01738572 0.01664352 0.01671124]
|
|
|
|
mean value: 0.01686427593231201
|
|
|
|
key: test_mcc
|
|
value: [-0.33333333 0.70710678 0. 0. -0.70710678 0.70710678
|
|
0. -0.4472136 0.16666667 0.40824829]
|
|
|
|
mean value: 0.050147480948378606
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.33333333 0.8 0.57142857 0.4 0.28571429 0.8
|
|
0.57142857 0.5 0.5 0.5 ]
|
|
|
|
mean value: 0.5261904761904763
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.33333333 1. 0.5 0.5 0.25 1.
|
|
0.5 0.4 0.5 1. ]
|
|
|
|
mean value: 0.5983333333333333
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.33333333 0.66666667 0.66666667 0.33333333 0.33333333 0.66666667
|
|
0.66666667 0.66666667 0.5 0.33333333]
|
|
|
|
mean value: 0.5166666666666666
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.33333333 0.83333333 0.5 0.5 0.16666667 0.83333333
|
|
0.5 0.33333333 0.6 0.6 ]
|
|
|
|
mean value: 0.5199999999999999
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.33333333 0.83333333 0.5 0.5 0.16666667 0.83333333
|
|
0.5 0.33333333 0.58333333 0.66666667]
|
|
|
|
mean value: 0.525
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.2 0.66666667 0.4 0.25 0.16666667 0.66666667
|
|
0.4 0.33333333 0.33333333 0.33333333]
|
|
|
|
mean value: 0.375
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: FP
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: FN
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: TP
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.26
|
|
|
|
Accuracy on Blind test: 0.65
|
|
|
|
Running classifier: 10
|
|
Model_name: Extra Tree
|
|
Model func: ExtraTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00797677 0.00787926 0.00788188 0.00788665 0.00801349 0.0082798
|
|
0.00797129 0.0079236 0.00790405 0.00862122]
|
|
|
|
mean value: 0.00803380012512207
|
|
|
|
key: score_time
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
[0.0082202 0.00814605 0.00813627 0.00815105 0.00814056 0.008214
|
|
0.00821114 0.00814772 0.00879383 0.00855756]
|
|
|
|
mean value: 0.00827183723449707
|
|
|
|
key: test_mcc
|
|
value: [ 0. 0.70710678 -0.4472136 -0.70710678 -0.70710678 -0.4472136
|
|
0. 0.4472136 0.66666667 0.66666667]
|
|
|
|
mean value: 0.017901295664682747
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.4 0.8 0.5 0. 0.28571429 0.
|
|
0.57142857 0.75 0.8 0.8 ]
|
|
|
|
mean value: 0.49071428571428577
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.5 1. 0.4 0. 0.25 0.
|
|
0.5 0.6 0.66666667 1. ]
|
|
|
|
mean value: 0.4916666666666666
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.33333333 0.66666667 0.66666667 0. 0.33333333 0.
|
|
0.66666667 1. 1. 0.66666667]
|
|
|
|
mean value: 0.5333333333333333
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.5 0.83333333 0.33333333 0.16666667 0.16666667 0.33333333
|
|
0.5 0.66666667 0.8 0.8 ]
|
|
|
|
mean value: 0.51
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.5 0.83333333 0.33333333 0.16666667 0.16666667 0.33333333
|
|
0.5 0.66666667 0.83333333 0.83333333]
|
|
|
|
mean value: 0.5166666666666666
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.25 0.66666667 0.33333333 0. 0.16666667 0.
|
|
0.4 0.6 0.66666667 0.66666667]
|
|
|
|
mean value: 0.375
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: FP
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: FN
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: TP
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.21
|
|
|
|
Accuracy on Blind test: 0.57
|
|
|
|
Running classifier: 11
|
|
Model_name: Random Forest
|
|
Model func: RandomForestClassifier(n_estimators=1000, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(n_estimators=1000, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.98296475 0.98631954 0.98036337 0.98156142 0.97943497 0.98355842
|
|
0.98102999 0.97444534 0.98225522 0.97287917]
|
|
|
|
mean value: 0.980481219291687
|
|
|
|
key: score_time
|
|
value: [0.0900774 0.09229064 0.09402585 0.09391332 0.09271097 0.0916388
|
|
0.09372568 0.09287024 0.09086537 0.08647847]
|
|
|
|
mean value: 0.09185967445373536
|
|
|
|
key: test_mcc
|
|
value: [0. 1. 0. 0.4472136 0. 1.
|
|
0. 0. 1. 0.66666667]
|
|
|
|
mean value: 0.41138802621666243
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.4 1. 0.66666667 0.5 0.57142857 1.
|
|
0.57142857 0.57142857 1. 0.8 ]
|
|
|
|
mean value: 0.7080952380952381
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.5 1. 0.5 1. 0.5 1. 0.5 0.5 1. 1. ]
|
|
|
|
mean value: 0.75
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.33333333 1. 1. 0.33333333 0.66666667 1.
|
|
0.66666667 0.66666667 1. 0.66666667]
|
|
|
|
mean value: 0.7333333333333333
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.5 1. 0.5 0.66666667 0.5 1.
|
|
0.5 0.5 1. 0.8 ]
|
|
|
|
mean value: 0.6966666666666665
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.5 1. 0.5 0.66666667 0.5 1.
|
|
0.5 0.5 1. 0.83333333]
|
|
|
|
mean value: 0.7
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.25 1. 0.5 0.33333333 0.4 1.
|
|
0.4 0.4 1. 0.66666667]
|
|
|
|
mean value: 0.595
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 19
|
|
|
|
mean value: 19.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: TP
|
|
value: 21
|
|
|
|
mean value: 21.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.56
|
|
|
|
Accuracy on Blind test: 0.8
|
|
|
|
Running classifier: 12
|
|
Model_name: Random Forest2
|
|
Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...age_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10,
|
|
oob_score=True, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.77408147 0.83756614 0.84048009 0.8518939 0.8126564 0.85278058
|
|
0.81466842 0.89787817 0.79662037 0.82353354]
|
|
|
|
mean value: 0.8302159070968628
|
|
|
|
key: score_time
|
|
value: [0.18825769 0.19139767 0.17149496 0.18060875 0.18776417 0.12078071
|
|
0.18363857 0.19443631 0.18648338 0.21026301]
|
|
|
|
mean value: 0.18151252269744872
|
|
|
|
key: test_mcc
|
|
value: [0. 1. 0. 0.4472136 0. 1.
|
|
0. 0.70710678 0.66666667 0.66666667]
|
|
|
|
mean value: 0.4487653710019838
|
|
|
|
key: train_mcc
|
|
value: [0.9258201 0.96225045 1. 0.96225045 1. 0.96225045
|
|
1. 0.96225045 0.92450142 1. ]
|
|
|
|
mean value: 0.9699323318871482
|
|
|
|
key: test_fscore
|
|
value: [0.4 1. 0.66666667 0.5 0.57142857 1.
|
|
0.57142857 0.85714286 0.8 0.8 ]
|
|
|
|
mean value: 0.7166666666666666
|
|
|
|
key: train_fscore
|
|
value: [0.96 0.98039216 1. 0.98039216 1. 0.98113208
|
|
1. 0.98113208 0.96296296 1. ]
|
|
|
|
mean value: 0.9846011427631851
|
|
|
|
key: test_precision
|
|
value: [0.5 1. 0.5 1. 0.5 1.
|
|
0.5 0.75 0.66666667 1. ]
|
|
|
|
mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
0.7416666666666667
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 0.96296296
|
|
1. 0.96296296 0.96296296 1. ]
|
|
|
|
mean value: 0.9888888888888889
|
|
|
|
key: test_recall
|
|
value: [0.33333333 1. 1. 0.33333333 0.66666667 1.
|
|
0.66666667 1. 1. 0.66666667]
|
|
|
|
mean value: 0.7666666666666667
|
|
|
|
key: train_recall
|
|
value: [0.92307692 0.96153846 1. 0.96153846 1. 1.
|
|
1. 1. 0.96296296 1. ]
|
|
|
|
mean value: 0.980911680911681
|
|
|
|
key: test_accuracy
|
|
value: [0.5 1. 0.5 0.66666667 0.5 1.
|
|
0.5 0.83333333 0.8 0.8 ]
|
|
|
|
mean value: 0.71
|
|
|
|
key: train_accuracy
|
|
value: [0.96153846 0.98076923 1. 0.98076923 1. 0.98076923
|
|
1. 0.98076923 0.96226415 1. ]
|
|
|
|
mean value: 0.9846879535558781
|
|
|
|
key: test_roc_auc
|
|
value: [0.5 1. 0.5 0.66666667 0.5 1.
|
|
0.5 0.83333333 0.83333333 0.83333333]
|
|
|
|
mean value: 0.7166666666666666
|
|
|
|
key: train_roc_auc
|
|
value: [0.96153846 0.98076923 1. 0.98076923 1. 0.98076923
|
|
1. 0.98076923 0.96225071 1. ]
|
|
|
|
mean value: 0.9846866096866098
|
|
|
|
key: test_jcc
|
|
value: [0.25 1. 0.5 0.33333333 0.4 1.
|
|
0.4 0.75 0.66666667 0.66666667]
|
|
|
|
mean value: 0.5966666666666667
|
|
|
|
key: train_jcc
|
|
value: [0.92307692 0.96153846 1. 0.96153846 1. 0.96296296
|
|
1. 0.96296296 0.92857143 1. ]
|
|
|
|
mean value: 0.9700651200651201
|
|
|
|
key: TN
|
|
value: 19
|
|
|
|
mean value: 19.0
|
|
|
|
key: FP
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: FN
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: TP
|
|
value: 22
|
|
|
|
mean value: 22.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.51
|
|
|
|
Accuracy on Blind test: 0.78
|
|
|
|
Running classifier: 13
|
|
Model_name: XGBoost
|
|
Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...
|
|
interaction_constraints=None, learning_rate=None,
|
|
max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan,
|
|
monotone_constraints=None, n_estimators=100,
|
|
n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None,
|
|
reg_lambda=None, scale_pos_weight=None,
|
|
subsample=None, tree_method=None,
|
|
use_label_encoder=False,
|
|
validate_parameters=None, verbosity=0))])
|
|
|
|
key: fit_time
|
|
value: [0.06364822 0.03136325 0.02988315 0.02997828 0.030056 0.03207469
|
|
0.03320622 0.03187776 0.03167343 0.04203773]
|
|
|
|
mean value: 0.035579872131347653
|
|
|
|
key: score_time
|
|
value: [0.01013684 0.01014662 0.01000309 0.01048088 0.01002479 0.01074505
|
|
0.00997162 0.01008677 0.01084256 0.01022935]
|
|
|
|
mean value: 0.010266757011413575
|
|
|
|
key: test_mcc
|
|
value: [0.70710678 1. 0.33333333 0.4472136 0.33333333 0.70710678
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.752809382453972
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.8 1. 0.66666667 0.5 0.66666667 0.85714286
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.849047619047619
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.66666667 1. 0.66666667 0.75
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9083333333333332
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 1. 0.66666667 0.33333333 0.66666667 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.8333333333333333
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.8666666666666668
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.8666666666666668
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 1. 0.5 0.33333333 0.5 0.75
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.775
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: FP
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: FN
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: TP
|
|
value: 24
|
|
|
|
mean value: 24.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 1.0
|
|
|
|
Accuracy on Blind test: 1.0
|
|
|
|
Running classifier: 14
|
|
Model_name: LDA
|
|
Model func: LinearDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LinearDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.01090717 0.04319477 0.03406215 0.0360713 0.03453875 0.03405213
|
|
0.04831815 0.01464033 0.01464176 0.01442671]
|
|
|
|
mean value: 0.028485321998596193
|
|
|
|
key: score_time
|
|
value: [0.01095223 0.02088284 0.02107692 0.01997852 0.02296853 0.0319159
|
|
0.01285052 0.01170683 0.01175642 0.01163101]
|
|
|
|
mean value: 0.01757197380065918
|
|
|
|
key: test_mcc
|
|
value: [ 0.70710678 1. 0. -0.33333333 0. 0.70710678
|
|
0.33333333 0.70710678 0. 0.61237244]
|
|
|
|
mean value: 0.37336927792554375
|
|
|
|
key: train_mcc
|
|
value: [1. 0.84615385 1. 0.96225045 0.96225045 0.96225045
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9732905192101976
|
|
|
|
key: test_fscore
|
|
value: [0.8 1. 0.4 0.33333333 0.57142857 0.8
|
|
0.66666667 0.8 0. 0.85714286]
|
|
|
|
mean value: 0.6228571428571429
|
|
|
|
key: train_fscore
|
|
value: [1. 0.92307692 1. 0.98039216 0.98113208 0.98113208
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9865733230883065
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.5 0.33333333 0.5 1.
|
|
0.66666667 1. 0. 0.75 ]
|
|
|
|
mean value: 0.675
|
|
|
|
key: train_precision
|
|
value: [1. 0.92307692 1. 1. 0.96296296 0.96296296
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9849002849002849
|
|
|
|
key: test_recall
|
|
value: [0.66666667 1. 0.33333333 0.33333333 0.66666667 0.66666667
|
|
0.66666667 0.66666667 0. 1. ]
|
|
|
|
mean value: 0.6
|
|
|
|
key: train_recall
|
|
value: [1. 0.92307692 1. 0.96153846 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9884615384615385
|
|
|
|
key: test_accuracy
|
|
value: [0.83333333 1. 0.5 0.33333333 0.5 0.83333333
|
|
0.66666667 0.83333333 0.6 0.8 ]
|
|
|
|
mean value: 0.69
|
|
|
|
key: train_accuracy
|
|
value: [1. 0.92307692 1. 0.98076923 0.98076923 0.98076923
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9865384615384617
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 1. 0.5 0.33333333 0.5 0.83333333
|
|
0.66666667 0.83333333 0.5 0.75 ]
|
|
|
|
mean value: 0.675
|
|
|
|
key: train_roc_auc
|
|
value: [1. 0.92307692 1. 0.98076923 0.98076923 0.98076923
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9865384615384617
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 1. 0.25 0.2 0.4 0.66666667
|
|
0.5 0.66666667 0. 0.75 ]
|
|
|
|
mean value: 0.51
|
|
|
|
key: train_jcc
|
|
value: [1. 0.85714286 1. 0.96153846 0.96296296 0.96296296
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9744607244607245
|
|
|
|
key: TN
|
|
value: 22
|
|
|
|
mean value: 22.0
|
|
|
|
key: FP
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 18
|
|
|
|
mean value: 18.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.24
|
|
|
|
Accuracy on Blind test: 0.6
|
|
|
|
Running classifier: 15
|
|
Model_name: Multinomial
|
|
Model func: MultinomialNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MultinomialNB())])
|
|
|
|
key: fit_time
|
|
value: [0.0219574 0.00874662 0.00845957 0.00799561 0.00798368 0.00791121
|
|
0.00849724 0.00794101 0.00807071 0.00823379]
|
|
|
|
mean value: 0.009579682350158691
|
|
|
|
key: score_time
|
|
value: [0.00983024 0.00915194 0.00822258 0.0083971 0.00818539 0.00826788
|
|
0.00891376 0.00871086 0.00815988 0.00857592]
|
|
|
|
mean value: 0.008641552925109864
|
|
|
|
key: test_mcc
|
|
value: [-0.33333333 0.70710678 0. 0. -0.4472136 0.
|
|
-0.70710678 0.33333333 -0.61237244 0.40824829]
|
|
|
|
mean value: -0.06513377407318895
|
|
|
|
key: train_mcc
|
|
value: [0.34848139 0.54006172 0.4233902 0.4259217 0.46709937 0.50336201
|
|
0.4259217 0.50037023 0.39888558 0.43447293]
|
|
|
|
mean value: 0.44679668423593266
|
|
|
|
key: test_fscore
|
|
value: [0.33333333 0.8 0.66666667 0.4 0.5 0.4
|
|
0.28571429 0.66666667 0.33333333 0.5 ]
|
|
|
|
mean value: 0.48857142857142855
|
|
|
|
key: train_fscore
|
|
value: [0.69090909 0.76 0.70588235 0.72727273 0.70833333 0.73469388
|
|
0.72727273 0.75471698 0.72413793 0.71698113]
|
|
|
|
mean value: 0.7250200153522106
|
|
|
|
key: test_precision
|
|
value: [0.33333333 1. 0.5 0.5 0.4 0.5
|
|
0.25 0.66666667 0.25 1. ]
|
|
|
|
mean value: 0.5399999999999999
|
|
|
|
key: train_precision
|
|
value: [0.65517241 0.79166667 0.72 0.68965517 0.77272727 0.7826087
|
|
0.68965517 0.74074074 0.67741935 0.7037037 ]
|
|
|
|
mean value: 0.7223349192949957
|
|
|
|
key: test_recall
|
|
value: [0.33333333 0.66666667 1. 0.33333333 0.66666667 0.33333333
|
|
0.33333333 0.66666667 0.5 0.33333333]
|
|
|
|
mean value: 0.5166666666666666
|
|
|
|
key: train_recall
|
|
value: [0.73076923 0.73076923 0.69230769 0.76923077 0.65384615 0.69230769
|
|
0.76923077 0.76923077 0.77777778 0.73076923]
|
|
|
|
mean value: 0.7316239316239317
|
|
|
|
key: test_accuracy
|
|
value: [0.33333333 0.83333333 0.5 0.5 0.33333333 0.5
|
|
0.16666667 0.66666667 0.2 0.6 ]
|
|
|
|
mean value: 0.46333333333333326
|
|
|
|
key: train_accuracy
|
|
value: [0.67307692 0.76923077 0.71153846 0.71153846 0.73076923 0.75
|
|
0.71153846 0.75 0.69811321 0.71698113]
|
|
|
|
mean value: 0.722278664731495
|
|
|
|
key: test_roc_auc
|
|
value: [0.33333333 0.83333333 0.5 0.5 0.33333333 0.5
|
|
0.16666667 0.66666667 0.25 0.66666667]
|
|
|
|
mean value: 0.475
|
|
|
|
key: train_roc_auc
|
|
value: [0.67307692 0.76923077 0.71153846 0.71153846 0.73076923 0.75
|
|
0.71153846 0.75 0.6965812 0.71723647]
|
|
|
|
mean value: 0.7221509971509972
|
|
|
|
key: test_jcc
|
|
value: [0.2 0.66666667 0.5 0.25 0.33333333 0.25
|
|
0.16666667 0.5 0.2 0.33333333]
|
|
|
|
mean value: 0.34
|
|
|
|
key: train_jcc
|
|
value: [0.52777778 0.61290323 0.54545455 0.57142857 0.5483871 0.58064516
|
|
0.57142857 0.60606061 0.56756757 0.55882353]
|
|
|
|
mean value: 0.5690476653000371
|
|
|
|
key: TN
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: FP
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: FN
|
|
value: 17
|
|
|
|
mean value: 17.0
|
|
|
|
key: TP
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.21
|
|
|
|
Accuracy on Blind test: 0.57
|
|
|
|
Running classifier: 16
|
|
Model_name: Passive Aggresive
|
|
Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
PassiveAggressiveClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00844765 0.0117445 0.01213598 0.01206732 0.01215124 0.01197219
|
|
0.01199651 0.01190472 0.01303363 0.01203942]
|
|
|
|
mean value: 0.01174931526184082
|
|
|
|
key: score_time
|
|
value: [0.00813985 0.01085377 0.0108459 0.01131415 0.01135468 0.01144958
|
|
0.01139641 0.01133323 0.01140904 0.01141429]
|
|
|
|
mean value: 0.010951089859008788
|
|
|
|
key: test_mcc
|
|
value: [ 0.4472136 1. 0.4472136 0. -0.33333333 1.
|
|
-0.33333333 0.4472136 0. 1. ]
|
|
|
|
mean value: 0.3674974119833207
|
|
|
|
key: train_mcc
|
|
value: [0.89056356 0.9258201 0.96225045 0.75878691 0.89056356 0.89056356
|
|
0.82305489 0.92307692 0.76178523 0.85164138]
|
|
|
|
mean value: 0.8678106557240634
|
|
|
|
key: test_fscore
|
|
value: [0.5 1. 0.75 0.57142857 0.33333333 1.
|
|
0.33333333 0.75 0.57142857 1. ]
|
|
|
|
mean value: 0.6809523809523809
|
|
|
|
key: train_fscore
|
|
value: [0.93877551 0.96 0.98113208 0.88135593 0.93877551 0.93877551
|
|
0.9122807 0.96153846 0.8852459 0.92592593]
|
|
|
|
mean value: 0.9323805529145449
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.6 0.5 0.33333333 1.
|
|
0.33333333 0.6 0.4 1. ]
|
|
|
|
mean value: 0.6766666666666667
|
|
|
|
key: train_precision
|
|
value: [1. 1. 0.96296296 0.78787879 1. 1.
|
|
0.83870968 0.96153846 0.79411765 0.89285714]
|
|
|
|
mean value: 0.9238064679715533
|
|
|
|
key: test_recall
|
|
value: [0.33333333 1. 1. 0.66666667 0.33333333 1.
|
|
0.33333333 1. 1. 1. ]
|
|
|
|
mean value: 0.7666666666666666
|
|
|
|
key: train_recall
|
|
value: [0.88461538 0.92307692 1. 1. 0.88461538 0.88461538
|
|
1. 0.96153846 1. 0.96153846]
|
|
|
|
mean value: 0.95
|
|
|
|
key: test_accuracy
|
|
value: [0.66666667 1. 0.66666667 0.5 0.33333333 1.
|
|
0.33333333 0.66666667 0.4 1. ]
|
|
|
|
mean value: 0.6566666666666666
|
|
|
|
key: train_accuracy
|
|
value: [0.94230769 0.96153846 0.98076923 0.86538462 0.94230769 0.94230769
|
|
0.90384615 0.96153846 0.86792453 0.9245283 ]
|
|
|
|
mean value: 0.929245283018868
|
|
|
|
key: test_roc_auc
|
|
value: [0.66666667 1. 0.66666667 0.5 0.33333333 1.
|
|
0.33333333 0.66666667 0.5 1. ]
|
|
|
|
mean value: 0.6666666666666666
|
|
|
|
key: train_roc_auc
|
|
value: [0.94230769 0.96153846 0.98076923 0.86538462 0.94230769 0.94230769
|
|
0.90384615 0.96153846 0.86538462 0.92521368]
|
|
|
|
mean value: 0.929059829059829
|
|
|
|
key: test_jcc
|
|
value: [0.33333333 1. 0.6 0.4 0.2 1.
|
|
0.2 0.6 0.4 1. ]
|
|
|
|
mean value: 0.5733333333333334
|
|
|
|
key: train_jcc
|
|
value: [0.88461538 0.92307692 0.96296296 0.78787879 0.88461538 0.88461538
|
|
0.83870968 0.92592593 0.79411765 0.86206897]
|
|
|
|
mean value: 0.8748587043686173
|
|
|
|
key: TN
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: FP
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: FN
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: TP
|
|
value: 22
|
|
|
|
mean value: 22.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.03
|
|
|
|
Accuracy on Blind test: 0.4
|
|
|
|
Running classifier: 17
|
|
Model_name: Stochastic GDescent
|
|
Model func: SGDClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SGDClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00824594 0.00882649 0.01191044 0.01159239 0.01171517 0.01183748
|
|
0.01176429 0.011832 0.01183248 0.01164579]
|
|
|
|
mean value: 0.011120247840881347
|
|
|
|
key: score_time
|
|
value: [0.00841475 0.00876427 0.01136231 0.01142454 0.01130295 0.0113647
|
|
0.01149154 0.01136804 0.01151252 0.01137519]
|
|
|
|
mean value: 0.010838079452514648
|
|
|
|
key: test_mcc
|
|
value: [ 0.4472136 0.70710678 0.70710678 0. 0. 1.
|
|
0. 0.70710678 -0.16666667 1. ]
|
|
|
|
mean value: 0.4401867272392934
|
|
|
|
key: train_mcc
|
|
value: [0.28867513 0.89056356 1. 1. 1. 0.80829038
|
|
0.85634884 0.92307692 0.89227454 0.50219975]
|
|
|
|
mean value: 0.8161429111522169
|
|
|
|
key: test_fscore
|
|
value: [0.75 0.8 0.8 0.4 0.57142857 1.
|
|
0.4 0.85714286 0.4 1. ]
|
|
|
|
mean value: 0.697857142857143
|
|
|
|
key: train_fscore
|
|
value: [0.7027027 0.93877551 1. 1. 1. 0.90196078
|
|
0.91666667 0.96153846 0.94736842 0.76470588]
|
|
|
|
mean value: 0.9133718428831212
|
|
|
|
key: test_precision
|
|
value: [0.6 1. 1. 0.5 0.5 1.
|
|
0.5 0.75 0.33333333 1. ]
|
|
|
|
mean value: 0.7183333333333333
|
|
|
|
key: train_precision
|
|
value: [0.54166667 1. 1. 1. 1. 0.92
|
|
1. 0.96153846 0.9 0.61904762]
|
|
|
|
mean value: 0.8942252747252747
|
|
|
|
key: test_recall
|
|
value: [1. 0.66666667 0.66666667 0.33333333 0.66666667 1.
|
|
0.33333333 1. 0.5 1. ]
|
|
|
|
mean value: 0.7166666666666666
|
|
|
|
key: train_recall
|
|
value: [1. 0.88461538 1. 1. 1. 0.88461538
|
|
0.84615385 0.96153846 1. 1. ]
|
|
|
|
mean value: 0.9576923076923076
|
|
|
|
key: test_accuracy
|
|
value: [0.66666667 0.83333333 0.83333333 0.5 0.5 1.
|
|
0.5 0.83333333 0.4 1. ]
|
|
|
|
mean value: 0.7066666666666668
|
|
|
|
key: train_accuracy
|
|
value: [0.57692308 0.94230769 1. 1. 1. 0.90384615
|
|
0.92307692 0.96153846 0.94339623 0.69811321]
|
|
|
|
mean value: 0.8949201741654571
|
|
|
|
key: test_roc_auc
|
|
value: [0.66666667 0.83333333 0.83333333 0.5 0.5 1.
|
|
0.5 0.83333333 0.41666667 1. ]
|
|
|
|
mean value: 0.7083333333333333
|
|
|
|
key: train_roc_auc
|
|
value: [0.57692308 0.94230769 1. 1. 1. 0.90384615
|
|
0.92307692 0.96153846 0.94230769 0.7037037 ]
|
|
|
|
mean value: 0.8953703703703704
|
|
|
|
key: test_jcc
|
|
value: [0.6 0.66666667 0.66666667 0.25 0.4 1.
|
|
0.25 0.75 0.25 1. ]
|
|
|
|
mean value: 0.5833333333333333
|
|
|
|
key: train_jcc
|
|
value: [0.54166667 0.88461538 1. 1. 1. 0.82142857
|
|
0.84615385 0.92592593 0.9 0.61904762]
|
|
|
|
mean value: 0.8538838013838014
|
|
|
|
key: TN
|
|
value: 20
|
|
|
|
mean value: 20.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: TP
|
|
value: 21
|
|
|
|
mean value: 21.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.06
|
|
|
|
Accuracy on Blind test: 0.52
|
|
|
|
Running classifier: 18
|
|
Model_name: AdaBoost Classifier
|
|
Model func: AdaBoostClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', AdaBoostClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.06980157 0.07130361 0.07226229 0.06922984 0.06929612 0.07238531
|
|
0.0756197 0.07484674 0.07620382 0.07381821]
|
|
|
|
mean value: 0.07247672080993653
|
|
|
|
key: score_time
|
|
value: [0.01429987 0.01539707 0.01418209 0.01416135 0.01432991 0.01474619
|
|
0.01519608 0.01451135 0.01416588 0.01511621]
|
|
|
|
mean value: 0.014610600471496583
|
|
|
|
key: test_mcc
|
|
value: [0.70710678 1. 0.33333333 0.4472136 0.33333333 0.70710678
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.752809382453972
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.8 1. 0.66666667 0.5 0.66666667 0.85714286
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.849047619047619
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.66666667 1. 0.66666667 0.75
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9083333333333332
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 1. 0.66666667 0.33333333 0.66666667 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.8333333333333333
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.8666666666666668
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.8666666666666668
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 1. 0.5 0.33333333 0.5 0.75
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.775
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: FP
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: FN
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: TP
|
|
value: 24
|
|
|
|
mean value: 24.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 1.0
|
|
|
|
Accuracy on Blind test: 1.0
|
|
|
|
Running classifier: 19
|
|
Model_name: Bagging Classifier
|
|
Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
BaggingClassifier(n_jobs=10, oob_score=True,
|
|
random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02761078 0.0292685 0.03312254 0.02849674 0.03523755 0.0358634
|
|
0.02830291 0.02811313 0.03103304 0.03638864]
|
|
|
|
mean value: 0.031343722343444826
|
|
|
|
key: score_time
|
|
value: [0.02042437 0.02199721 0.02151895 0.03123069 0.02501988 0.02050543
|
|
0.02155209 0.03574848 0.02209949 0.02571082]
|
|
|
|
mean value: 0.024580740928649904
|
|
|
|
key: test_mcc
|
|
value: [0.70710678 1. 0.33333333 0.4472136 0.33333333 0.70710678
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.752809382453972
|
|
|
|
key: train_mcc
|
|
value: [1. 0.96225045 1. 0.96225045 1. 1.
|
|
1. 0.96225045 0.96296296 1. ]
|
|
|
|
mean value: 0.9849714308911093
|
|
|
|
key: test_fscore
|
|
value: [0.8 1. 0.66666667 0.5 0.66666667 0.85714286
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.849047619047619
|
|
|
|
key: train_fscore
|
|
value: [1. 0.98039216 1. 0.98039216 1. 1.
|
|
1. 0.98039216 0.98113208 1. ]
|
|
|
|
mean value: 0.9922308546059935
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.66666667 1. 0.66666667 0.75
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9083333333333332
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 1. 0.66666667 0.33333333 0.66666667 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.8333333333333333
|
|
|
|
key: train_recall
|
|
value: [1. 0.96153846 1. 0.96153846 1. 1.
|
|
1. 0.96153846 0.96296296 1. ]
|
|
|
|
mean value: 0.9847578347578348
|
|
|
|
key: test_accuracy
|
|
value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.8666666666666668
|
|
|
|
key: train_accuracy
|
|
value: [1. 0.98076923 1. 0.98076923 1. 1.
|
|
1. 0.98076923 0.98113208 1. ]
|
|
|
|
mean value: 0.9923439767779391
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 1. 0.66666667 0.66666667 0.66666667 0.83333333
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.8666666666666668
|
|
|
|
key: train_roc_auc
|
|
value: [1. 0.98076923 1. 0.98076923 1. 1.
|
|
1. 0.98076923 0.98148148 1. ]
|
|
|
|
mean value: 0.9923789173789175
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 1. 0.5 0.33333333 0.5 0.75
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.775
|
|
|
|
key: train_jcc
|
|
value: [1. 0.96153846 1. 0.96153846 1. 1.
|
|
1. 0.96153846 0.96296296 1. ]
|
|
|
|
mean value: 0.9847578347578348
|
|
|
|
key: TN
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: FP
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: FN
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: TP
|
|
value: 24
|
|
|
|
mean value: 24.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.95
|
|
|
|
Accuracy on Blind test: 0.98
|
|
|
|
Running classifier: 20
|
|
Model_name: Gaussian Process
|
|
Model func: GaussianProcessClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianProcessClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01054978 0.01045513 0.00980616 0.00982642 0.00995708 0.0100565
|
|
0.00980353 0.00985241 0.00990748 0.00987434]
|
|
|
|
mean value: 0.010008883476257325
|
|
|
|
key: score_time
|
|
value: [0.00911832 0.00907159 0.00849557 0.00847793 0.00844884 0.00848508
|
|
0.00845075 0.00847316 0.00876307 0.00851512]
|
|
|
|
mean value: 0.008629941940307617
|
|
|
|
key: test_mcc
|
|
value: [ 0.33333333 0.70710678 0. -0.4472136 -0.4472136 0.4472136
|
|
0. -0.4472136 0.40824829 0.40824829]
|
|
|
|
mean value: 0.09625095044476913
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.66666667 0.8 0.57142857 0. 0.5 0.5
|
|
0.57142857 0.5 0.66666667 0.5 ]
|
|
|
|
mean value: 0.5276190476190477
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
[0.66666667 1. 0.5 0. 0.4 1.
|
|
0.5 0.4 0.5 1. ]
|
|
|
|
mean value: 0.5966666666666667
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.66666667 0.66666667 0. 0.66666667 0.33333333
|
|
0.66666667 0.66666667 1. 0.33333333]
|
|
|
|
mean value: 0.5666666666666667
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.66666667 0.83333333 0.5 0.33333333 0.33333333 0.66666667
|
|
0.5 0.33333333 0.6 0.6 ]
|
|
|
|
mean value: 0.5366666666666665
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.66666667 0.83333333 0.5 0.33333333 0.33333333 0.66666667
|
|
0.5 0.33333333 0.66666667 0.66666667]
|
|
|
|
mean value: 0.55
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.5 0.66666667 0.4 0. 0.33333333 0.33333333
|
|
0.4 0.33333333 0.5 0.33333333]
|
|
|
|
mean value: 0.38
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: FP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: FN
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: TP
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.28
|
|
|
|
Accuracy on Blind test: 0.62
|
|
|
|
Running classifier: 21
|
|
Model_name: Gradient Boosting
|
|
Model func: GradientBoostingClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GradientBoostingClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.10271955 0.10487604 0.08293653 0.10259652 0.08842254 0.10237932
|
|
0.10384226 0.10312128 0.10364127 0.10579896]
|
|
|
|
mean value: 0.10003342628479003
|
|
|
|
key: score_time
|
|
value: [0.00865936 0.00893712 0.00901771 0.0088377 0.00872445 0.0088408
|
|
0.0089922 0.00864983 0.0091002 0.00891113]
|
|
|
|
mean value: 0.00886704921722412
|
|
|
|
key: test_mcc
|
|
value: [0.70710678 1. 0. 0.4472136 0. 0.70710678
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.6861427157873052
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.8 1. 0.57142857 0.5 0.57142857 0.85714286
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.8300000000000001
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.5 1. 0.5 0.75 1. 1. 1. 1. ]
|
|
|
|
mean value: 0.875
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.66666667 1. 0.66666667 0.33333333 0.66666667 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.8333333333333333
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.83333333 1. 0.5 0.66666667 0.5 0.83333333
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.8333333333333334
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 1. 0.5 0.66666667 0.5 0.83333333
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.8333333333333334
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 1. 0.4 0.33333333 0.4 0.75
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.755
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 24
|
|
|
|
mean value: 24.0
|
|
|
|
key: FP
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: FN
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: TP
|
|
value: 24
|
|
|
|
mean value: 24.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.95
|
|
|
|
Accuracy on Blind test: 0.98
|
|
|
|
Running classifier: 22
|
|
Model_name: QDA
|
|
Model func: QuadraticDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', QuadraticDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.00854087 0.00976038 0.00851274 0.00856066 0.00975871 0.00889325
|
|
0.00963783 0.00880289 0.00930786 0.01002502]
|
|
|
|
mean value: 0.009180021286010743
|
|
|
|
key: score_time
|
|
value: [0.00873137 0.00865483 0.0095017 0.00902653 0.00855947 0.00916696
|
|
0.00919914 0.00919485 0.00843978 0.00921845]
|
|
|
|
mean value: 0.008969306945800781
|
|
|
|
key: test_mcc
|
|
value: [ 0.4472136 0.4472136 -0.70710678 0. -0.33333333 -0.4472136
|
|
-0.4472136 0.4472136 -0.40824829 -0.61237244]
|
|
|
|
mean value: -0.16138472451795804
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.75 0.5 0. 0.57142857 0.33333333 0.5
|
|
0.5 0.5 0. 0. ]
|
|
|
|
mean value: 0.3654761904761905
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.6 1. 0. 0.5 0.33333333 0.4
|
|
0.4 1. 0. 0. ]
|
|
|
|
mean value: 0.42333333333333334
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.33333333 0. 0.66666667 0.33333333 0.66666667
|
|
0.66666667 0.33333333 0. 0. ]
|
|
|
|
mean value: 0.4
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.66666667 0.66666667 0.16666667 0.5 0.33333333 0.33333333
|
|
0.33333333 0.66666667 0.4 0.2 ]
|
|
|
|
mean value: 0.42666666666666664
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.66666667 0.66666667 0.16666667 0.5 0.33333333 0.33333333
|
|
0.33333333 0.66666667 0.33333333 0.25 ]
|
|
|
|
mean value: 0.425
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.6 0.33333333 0. 0.4 0.2 0.33333333
|
|
0.33333333 0.33333333 0. 0. ]
|
|
|
|
mean value: 0.2533333333333333
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: FP
|
|
value: 17
|
|
|
|
mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:356: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rus_CV['Data_source'] = 'CV'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:357: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rus_CV['Resampling'] = rs_rus
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:362: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rus_BT['Data_source'] = 'BT'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:363: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rus_BT['Resampling'] = rs_rus
|
|
17.0
|
|
|
|
key: FN
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: TP
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.14
|
|
|
|
Accuracy on Blind test: 0.5
|
|
|
|
Running classifier: 23
|
|
Model_name: Ridge Classifier
|
|
Model func: RidgeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01034141 0.0121088 0.01195264 0.01200914 0.02302814 0.01293015
|
|
0.0122416 0.01224279 0.01262784 0.01239896]
|
|
|
|
mean value: 0.01318814754486084
|
|
|
|
key: score_time
|
|
value: [0.01122975 0.01138377 0.01147366 0.01134562 0.02079654 0.01142693
|
|
0.01140499 0.01142883 0.01149654 0.01142836]
|
|
|
|
mean value: 0.012341499328613281
|
|
|
|
key: test_mcc
|
|
value: [0. 1. 1. 0. 0. 1. 0.
|
|
0.4472136 1. 1. ]
|
|
|
|
mean value: 0.5447213595499958
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.96225045 1. 1. ]
|
|
|
|
mean value: 0.9962250448649377
|
|
|
|
key: test_fscore
|
|
value: [0.4 1. 1. 0.4 0.57142857 1.
|
|
0.4 0.75 1. 1. ]
|
|
|
|
mean value: 0.7521428571428571
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.98113208 1. 1. ]
|
|
|
|
mean value: 0.9981132075471699
|
|
|
|
key: test_precision
|
|
value: [0.5 1. 1. 0.5 0.5 1. 0.5 0.6 1. 1. ]
|
|
|
|
mean value: 0.76
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.96296296 1. 1. ]
|
|
|
|
mean value: 0.9962962962962962
|
|
|
|
key: test_recall
|
|
value: [0.33333333 1. 1. 0.33333333 0.66666667 1.
|
|
0.33333333 1. 1. 1. ]
|
|
|
|
mean value: 0.7666666666666666
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.5 1. 1. 0.5 0.5 1.
|
|
0.5 0.66666667 1. 1. ]
|
|
|
|
mean value: 0.7666666666666666
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.98076923 1. 1. ]
|
|
|
|
mean value: 0.998076923076923
|
|
|
|
key: test_roc_auc
|
|
value: [0.5 1. 1. 0.5 0.5 1.
|
|
0.5 0.66666667 1. 1. ]
|
|
|
|
mean value: 0.7666666666666667
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.98076923 1. 1. ]
|
|
|
|
mean value: 0.998076923076923
|
|
|
|
key: test_jcc
|
|
value: [0.25 1. 1. 0.25 0.4 1. 0.25 0.6 1. 1. ]
|
|
|
|
mean value: 0.675
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.96296296 1. 1. ]
|
|
|
|
mean value: 0.9962962962962962
|
|
|
|
key: TN
|
|
value: 22
|
|
|
|
mean value: 22.0
|
|
|
|
key: FP
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 22
|
|
|
|
mean value: 22.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.14
|
|
|
|
Accuracy on Blind test: 0.57
|
|
|
|
Running classifier: 24
|
|
Model_name: Ridge ClassifierCV
|
|
Model func: RidgeClassifierCV(cv=10)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifierCV(cv=10))])
|
|
|
|
key: fit_time
|
|
value: [0.07401347 0.07836699 0.078619 0.07845759 0.07878184 0.07833385
|
|
0.07831335 0.07814956 0.07846451 0.07824159]
|
|
|
|
mean value: 0.07797417640686036
|
|
|
|
key: score_time
|
|
value: [0.01146913 0.01154041 0.01154804 0.01152658 0.01153302 0.01152396
|
|
0.0114727 0.01152277 0.01153612 0.01147652]
|
|
|
|
mean value: 0.011514925956726074
|
|
|
|
key: test_mcc
|
|
value: [0.33333333 1. 1. 0. 0. 1.
|
|
0. 0.4472136 0.66666667 1. ]
|
|
|
|
mean value: 0.5447213595499958
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.96225045 1. 1. ]
|
|
|
|
mean value: 0.9962250448649377
|
|
|
|
key: test_fscore
|
|
value: [0.66666667 1. 1. 0.4 0.57142857 1.
|
|
0.4 0.75 0.8 1. ]
|
|
|
|
mean value: 0.7588095238095238
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.98113208 1. 1. ]
|
|
|
|
mean value: 0.9981132075471699
|
|
|
|
key: test_precision
|
|
value: [0.66666667 1. 1. 0.5 0.5 1.
|
|
0.5 0.6 0.66666667 1. ]
|
|
|
|
mean value: 0.7433333333333334
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.96296296 1. 1. ]
|
|
|
|
mean value: 0.9962962962962962
|
|
|
|
key: test_recall
|
|
value: [0.66666667 1. 1. 0.33333333 0.66666667 1.
|
|
0.33333333 1. 1. 1. ]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.66666667 1. 1. 0.5 0.5 1.
|
|
0.5 0.66666667 0.8 1. ]
|
|
|
|
mean value: 0.7633333333333333
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.98076923 1. 1. ]
|
|
|
|
mean value: 0.998076923076923
|
|
|
|
key: test_roc_auc
|
|
value: [0.66666667 1. 1. 0.5 0.5 1.
|
|
0.5 0.66666667 0.83333333 1. ]
|
|
|
|
mean value: 0.7666666666666667
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.98076923 1. 1. ]
|
|
|
|
mean value: 0.998076923076923
|
|
|
|
key: test_jcc
|
|
value: [0.5 1. 1. 0.25 0.4 1.
|
|
0.25 0.6 0.66666667 1. ]
|
|
|
|
mean value: 0.6666666666666667
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.96296296 1. 1. ]
|
|
|
|
mean value: 0.9962962962962962
|
|
|
|
key: TN
|
|
value: 21
|
|
|
|
mean value: 21.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 23
|
|
|
|
mean value: 23.0
|
|
|
|
key: trainingY_neg
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: trainingY_pos
|
|
value: 29
|
|
|
|
mean value: 29.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.18
|
|
|
|
Accuracy on Blind test: 0.6
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
|
|
PASS: sorting df by score that is mapped onto the order I want
|
|
|
|
==============================================================
|
|
|
|
Running several classification models (n): 24
|
|
List of models:
|
|
('Logistic Regression', LogisticRegression(random_state=42))
|
|
('Logistic RegressionCV', LogisticRegressionCV(random_state=42))
|
|
('Gaussian NB', GaussianNB())
|
|
('Naive Bayes', BernoulliNB())
|
|
('K-Nearest Neighbors', KNeighborsClassifier())
|
|
('SVC', SVC(random_state=42))
|
|
('MLP', MLPClassifier(max_iter=500, random_state=42))
|
|
('Decision Tree', DecisionTreeClassifier(random_state=42))
|
|
('Extra Trees', ExtraTreesClassifier(random_state=42))
|
|
('Extra Tree', ExtraTreeClassifier(random_state=42))
|
|
('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42))
|
|
('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42))
|
|
('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0))
|
|
('LDA', LinearDiscriminantAnalysis())
|
|
('Multinomial', MultinomialNB())
|
|
('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42))
|
|
('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42))
|
|
('AdaBoost Classifier', AdaBoostClassifier(random_state=42))
|
|
('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))
|
|
('Gaussian Process', GaussianProcessClassifier(random_state=42))
|
|
('Gradient Boosting', GradientBoostingClassifier(random_state=42))
|
|
('QDA', QuadraticDiscriminantAnalysis())
|
|
('Ridge Classifier', RidgeClassifier(random_state=42))
|
|
('Ridge ClassifierCV', RidgeClassifierCV(cv=10))
|
|
|
|
================================================================
|
|
|
|
|
|
Running classifier: 1
|
|
Model_name: Logistic Regression
|
|
Model func: LogisticRegression(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegression(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02776575 0.02869368 0.02271485 0.02522039 0.02290487 0.02502799
|
|
0.02245021 0.05590749 0.02390695 0.03170252]
|
|
|
|
mean value: 0.028629469871520995
|
|
|
|
key: score_time
|
|
value: [0.01155543 0.01159883 0.01160884 0.01166868 0.01157451 0.01157188
|
|
0.01154232 0.01169586 0.01163626 0.01184702]
|
|
|
|
mean value: 0.011629962921142578
|
|
|
|
key: test_mcc
|
|
value: [1. 0.65465367 0.81649658 0.65465367 0.81649658 0.81649658
|
|
0.40824829 0.81649658 0.65465367 0. ]
|
|
|
|
mean value: 0.6638195626298699
|
|
|
|
key: train_mcc
|
|
value: [0.93541435 0.93541435 0.93541435 0.95650071 0.95555556 0.91201231
|
|
0.88910845 0.93541435 0.95555556 0.95555556]
|
|
|
|
mean value: 0.9365945526249924
|
|
|
|
key: test_fscore
|
|
value: [1. 0.83333333 0.88888889 0.83333333 0.88888889 0.90909091
|
|
0.72727273 0.90909091 0.83333333 0.54545455]
|
|
|
|
mean value: 0.8368686868686869
|
|
|
|
key: train_fscore
|
|
value: [0.96774194 0.96774194 0.96774194 0.97826087 0.97777778 0.95652174
|
|
0.94382022 0.96774194 0.97777778 0.97777778]
|
|
|
|
mean value: 0.9682903908683571
|
|
|
|
key: test_precision
|
|
value: [1. 0.71428571 1. 0.71428571 1. 0.83333333
|
|
0.66666667 0.83333333 0.71428571 0.5 ]
|
|
|
|
mean value: 0.7976190476190477
|
|
|
|
key: train_precision
|
|
value: [0.9375 0.9375 0.9375 0.95744681 0.97777778 0.93617021
|
|
0.95454545 0.9375 0.97777778 0.97777778]
|
|
|
|
mean value: 0.9531495809155384
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 1. 0.8 1. 0.8 1. 1. 0.6]
|
|
|
|
mean value: 0.9
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 0.97777778 0.97777778
|
|
0.93333333 1. 0.97777778 0.97777778]
|
|
|
|
mean value: 0.9844444444444445
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.8 0.9 0.8 0.9 0.9 0.7 0.9 0.8 0.5]
|
|
|
|
mean value: 0.82
|
|
|
|
key: train_accuracy
|
|
value: [0.96666667 0.96666667 0.96666667 0.97777778 0.97777778 0.95555556
|
|
0.94444444 0.96666667 0.97777778 0.97777778]
|
|
|
|
mean value: 0.9677777777777777
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.8 0.9 0.8 0.9 0.9 0.7 0.9 0.8 0.5]
|
|
|
|
mean value: 0.82
|
|
|
|
key: train_roc_auc
|
|
value: [0.96666667 0.96666667 0.96666667 0.97777778 0.97777778 0.95555556
|
|
0.94444444 0.96666667 0.97777778 0.97777778]
|
|
|
|
mean value: 0.9677777777777778
|
|
|
|
key: test_jcc
|
|
value: [1. 0.71428571 0.8 0.71428571 0.8 0.83333333
|
|
0.57142857 0.83333333 0.71428571 0.375 ]
|
|
|
|
mean value: 0.7355952380952381
|
|
|
|
key: train_jcc
|
|
value: [0.9375 0.9375 0.9375 0.95744681 0.95652174 0.91666667
|
|
0.89361702 0.9375 0.95652174 0.95652174]
|
|
|
|
mean value: 0.9387295713845207
|
|
|
|
key: TN
|
|
value: 37
|
|
|
|
mean value: 37.0
|
|
|
|
key: FP
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: FN
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: TP
|
|
value: 45
|
|
|
|
mean value: 45.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.37
|
|
|
|
Accuracy on Blind test: 0.72
|
|
|
|
Running classifier: 2
|
|
Model_name: Logistic RegressionCV
|
|
Model func: LogisticRegressionCV(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegressionCV(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.56633496 0.55252409 0.53593993 0.68272257 0.54451585 0.53557205
|
|
0.54859138 0.74416137 0.54470611 0.56081605]
|
|
|
|
mean value: 0.5815884351730347
|
|
|
|
key: score_time
|
|
value: [0.01307201 0.01323223 0.01380658 0.01179194 0.01315022 0.01324105
|
|
0.01309943 0.0132587 0.01182222 0.01328754]
|
|
|
|
mean value: 0.01297619342803955
|
|
|
|
key: test_mcc
|
|
value: [1. 0.81649658 0.6 0.65465367 0.81649658 0.65465367
|
|
1. 1. 0.40824829 0.21821789]
|
|
|
|
mean value: 0.7168766683971262
|
|
|
|
key: train_mcc
|
|
value: [0.97801929 1. 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9978019293843652
|
|
|
|
key: test_fscore
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
[1. 0.90909091 0.8 0.83333333 0.88888889 0.83333333
|
|
1. 1. 0.72727273 0.66666667]
|
|
|
|
mean value: 0.8658585858585859
|
|
|
|
key: train_fscore
|
|
value: [0.98901099 1. 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9989010989010989
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 0.8 0.71428571 1. 0.71428571
|
|
1. 1. 0.66666667 0.57142857]
|
|
|
|
mean value: 0.8300000000000001
|
|
|
|
key: train_precision
|
|
value: [0.97826087 1. 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9978260869565216
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 1. 0.8 1. 1. 1. 0.8 0.8]
|
|
|
|
mean value: 0.9200000000000002
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9 0.8 0.8 0.9 0.8 1. 1. 0.7 0.6]
|
|
|
|
mean value: 0.85
|
|
|
|
key: train_accuracy
|
|
value: [0.98888889 1. 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9988888888888889
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.9 0.8 0.8 0.9 0.8 1. 1. 0.7 0.6]
|
|
|
|
mean value: 0.85
|
|
|
|
key: train_roc_auc
|
|
value: [0.98888889 1. 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9988888888888889
|
|
|
|
key: test_jcc
|
|
value: [1. 0.83333333 0.66666667 0.71428571 0.8 0.71428571
|
|
1. 1. 0.57142857 0.5 ]
|
|
|
|
mean value: 0.78
|
|
|
|
key: train_jcc
|
|
value: [0.97826087 1. 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9978260869565216
|
|
|
|
key: TN
|
|
value: 39
|
|
|
|
mean value: 39.0
|
|
|
|
key: FP
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: FN
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: TP
|
|
value: 46
|
|
|
|
mean value: 46.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.25
|
|
|
|
Accuracy on Blind test: 0.68
|
|
|
|
Running classifier: 3
|
|
Model_name: Gaussian NB
|
|
Model func: GaussianNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianNB())])
|
|
|
|
key: fit_time
|
|
value: [0.01261568 0.01144147 0.00931716 0.00832486 0.00855613 0.00928593
|
|
0.00807047 0.00803423 0.00799537 0.00809693]
|
|
|
|
mean value: 0.009173822402954102
|
|
|
|
key: score_time
|
|
value: [0.01160741 0.00956869 0.00901723 0.00961471 0.00909519 0.00840259
|
|
0.00830054 0.00824618 0.00833321 0.00828719]
|
|
|
|
mean value: 0.009047293663024902
|
|
|
|
key: test_mcc
|
|
value: [ 0.5 0. -0.33333333 0.5 0. 0.40824829
|
|
-0.33333333 0.40824829 0. 0. ]
|
|
|
|
mean value: 0.11498299142610595
|
|
|
|
key: train_mcc
|
|
value: [0.45226702 0.53931937 0.53931937 0.57906602 0.43808583 0.69509522
|
|
0.48001536 0.56568542 0.51066218 0.68957028]
|
|
|
|
mean value: 0.5489086080136931
|
|
|
|
key: test_fscore
|
|
value: [0.76923077 0.66666667 0.57142857 0.76923077 0.54545455 0.72727273
|
|
0.57142857 0.72727273 0.54545455 0.54545455]
|
|
|
|
mean value: 0.6438894438894438
|
|
|
|
key: train_fscore
|
|
value: [0.75675676 0.78899083 0.78899083 0.79569892 0.74509804 0.85416667
|
|
0.76635514 0.8 0.77669903 0.84782609]
|
|
|
|
mean value: 0.792058229501609
|
|
|
|
key: test_precision
|
|
value: [0.625 0.5 0.44444444 0.625 0.5 0.66666667
|
|
0.44444444 0.66666667 0.5 0.5 ]
|
|
|
|
mean value: 0.5472222222222223
|
|
|
|
key: train_precision
|
|
value: [0.63636364 0.671875 0.671875 0.77083333 0.66666667 0.80392157
|
|
0.66129032 0.7 0.68965517 0.82978723]
|
|
|
|
mean value: 0.7102267934028078
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 1. 0.6 0.8 0.8 0.8 0.6 0.6]
|
|
|
|
mean value: 0.7999999999999999
|
|
|
|
key: train_recall
|
|
value: [0.93333333 0.95555556 0.95555556 0.82222222 0.84444444 0.91111111
|
|
0.91111111 0.93333333 0.88888889 0.86666667]
|
|
|
|
mean value: 0.9022222222222223
|
|
|
|
key: test_accuracy
|
|
value: [0.7 0.5 0.4 0.7 0.5 0.7 0.4 0.7 0.5 0.5]
|
|
|
|
mean value: 0.5599999999999999
|
|
|
|
key: train_accuracy
|
|
value: [0.7 0.74444444 0.74444444 0.78888889 0.71111111 0.84444444
|
|
0.72222222 0.76666667 0.74444444 0.84444444]
|
|
|
|
mean value: 0.7611111111111111
|
|
|
|
key: test_roc_auc
|
|
value: [0.7 0.5 0.4 0.7 0.5 0.7 0.4 0.7 0.5 0.5]
|
|
|
|
mean value: 0.5599999999999999
|
|
|
|
key: train_roc_auc
|
|
value: [0.7 0.74444444 0.74444444 0.78888889 0.71111111 0.84444444
|
|
0.72222222 0.76666667 0.74444444 0.84444444]
|
|
|
|
mean value: 0.7611111111111111
|
|
|
|
key: test_jcc
|
|
value: [0.625 0.5 0.4 0.625 0.375 0.57142857
|
|
0.4 0.57142857 0.375 0.375 ]
|
|
|
|
mean value: 0.4817857142857143
|
|
|
|
key: train_jcc
|
|
value: [0.60869565 0.65151515 0.65151515 0.66071429 0.59375 0.74545455
|
|
0.62121212 0.66666667 0.63492063 0.73584906]
|
|
|
|
mean value: 0.6570293265776243
|
|
|
|
key: TN
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 34
|
|
|
|
mean value: 34.0
|
|
|
|
key: TP
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.39
|
|
|
|
Accuracy on Blind test: 0.65
|
|
|
|
Running classifier: 4
|
|
Model_name: Naive Bayes
|
|
Model func: BernoulliNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', BernoulliNB())])
|
|
|
|
key: fit_time
|
|
value: [0.0094583 0.00919294 0.00911546 0.00833702 0.00844932 0.00869441
|
|
0.00868297 0.00884938 0.00957847 0.00917697]
|
|
|
|
mean value: 0.008953523635864259
|
|
|
|
key: score_time
|
|
value: [0.00851822 0.00911808 0.01154637 0.009516 0.00891685 0.00901747
|
|
0.0085547 0.00905323 0.00843143 0.00888371]
|
|
|
|
mean value: 0.009155607223510743
|
|
|
|
key: test_mcc
|
|
value: [0.81649658 0.65465367 0.2 0.2 0.5 0.40824829
|
|
0.65465367 0.2 0. 0. ]
|
|
|
|
mean value: 0.3634052212807543
|
|
|
|
key: train_mcc
|
|
value: [0.49897013 0.58137767 0.53346507 0.55555556 0.57792049 0.53346507
|
|
0.53346507 0.51111111 0.68888889 0.80178373]
|
|
|
|
mean value: 0.5816002788156436
|
|
|
|
key: test_fscore
|
|
value: [0.88888889 0.83333333 0.6 0.6 0.57142857 0.66666667
|
|
0.83333333 0.6 0.28571429 0.44444444]
|
|
|
|
mean value: 0.6323809523809524
|
|
|
|
key: train_fscore
|
|
value: [0.76767677 0.8 0.76923077 0.77777778 0.79120879 0.76404494
|
|
0.76404494 0.75555556 0.84444444 0.89655172]
|
|
|
|
mean value: 0.7930535717672486
|
|
|
|
key: test_precision
|
|
value: [1. 0.71428571 0.6 0.6 1. 0.75
|
|
0.71428571 0.6 0.5 0.5 ]
|
|
|
|
mean value: 0.6978571428571428
|
|
|
|
key: train_precision
|
|
value: [0.7037037 0.76 0.76086957 0.77777778 0.7826087 0.77272727
|
|
0.77272727 0.75555556 0.84444444 0.92857143]
|
|
|
|
mean value: 0.7858985716377022
|
|
|
|
key: test_recall
|
|
value: [0.8 1. 0.6 0.6 0.4 0.6 1. 0.6 0.2 0.4]
|
|
|
|
mean value: 0.62
|
|
|
|
key: train_recall
|
|
value: [0.84444444 0.84444444 0.77777778 0.77777778 0.8 0.75555556
|
|
0.75555556 0.75555556 0.84444444 0.86666667]
|
|
|
|
mean value: 0.8022222222222222
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.8 0.6 0.6 0.7 0.7 0.8 0.6 0.5 0.5]
|
|
|
|
mean value: 0.67
|
|
|
|
key: train_accuracy
|
|
value: [0.74444444 0.78888889 0.76666667 0.77777778 0.78888889 0.76666667
|
|
0.76666667 0.75555556 0.84444444 0.9 ]
|
|
|
|
mean value: 0.7899999999999999
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.8 0.6 0.6 0.7 0.7 0.8 0.6 0.5 0.5]
|
|
|
|
mean value: 0.67
|
|
|
|
key: train_roc_auc
|
|
value: [0.74444444 0.78888889 0.76666667 0.77777778 0.78888889 0.76666667
|
|
0.76666667 0.75555556 0.84444444 0.9 ]
|
|
|
|
mean value: 0.7899999999999999
|
|
|
|
key: test_jcc
|
|
value: [0.8 0.71428571 0.42857143 0.42857143 0.4 0.5
|
|
0.71428571 0.42857143 0.16666667 0.28571429]
|
|
|
|
mean value: 0.4866666666666667
|
|
|
|
key: train_jcc
|
|
value: [0.62295082 0.66666667 0.625 0.63636364 0.65454545 0.61818182
|
|
0.61818182 0.60714286 0.73076923 0.8125 ]
|
|
|
|
mean value: 0.6592302301523614
|
|
|
|
key: TN
|
|
value: 36
|
|
|
|
mean value: 36.0
|
|
|
|
key: FP
|
|
value: 19
|
|
|
|
mean value: 19.0
|
|
|
|
key: FN
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: TP
|
|
value: 31
|
|
|
|
mean value: 31.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.06
|
|
|
|
Accuracy on Blind test: 0.5
|
|
|
|
Running classifier: 5
|
|
Model_name: K-Nearest Neighbors
|
|
Model func: KNeighborsClassifier()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', KNeighborsClassifier())])
|
|
|
|
key: fit_time
|
|
value: [0.01051545 0.00867343 0.0085032 0.00867558 0.00853562 0.00811458
|
|
0.00812817 0.00796604 0.0099206 0.00903177]
|
|
|
|
mean value: 0.008806443214416504
|
|
|
|
key: score_time
|
|
value: [0.01103592 0.00978136 0.00917578 0.00924969 0.01515198 0.01001072
|
|
0.01416135 0.00911379 0.01416039 0.01609468]
|
|
|
|
mean value: 0.011793565750122071
|
|
|
|
key: test_mcc
|
|
value: [ 0.81649658 0.65465367 0.5 0. -0.2 0.2
|
|
0.5 0.40824829 0.40824829 -0.21821789]
|
|
|
|
mean value: 0.3069428942327437
|
|
|
|
key: train_mcc
|
|
value: [0.47863442 0.49897013 0.56056066 0.52421865 0.49103499 0.47087096
|
|
0.47863442 0.53990552 0.54433105 0.56056066]
|
|
|
|
mean value: 0.5147721476862103
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.83333333 0.76923077 0.61538462 0.4 0.6
|
|
0.76923077 0.72727273 0.72727273 0.5 ]
|
|
|
|
mean value: 0.6850815850815851
|
|
|
|
key: train_fscore
|
|
value: [0.76 0.76767677 0.79166667 0.78 0.76923077 0.75
|
|
0.76 0.78350515 0.78787879 0.79166667]
|
|
|
|
mean value: 0.7741624812758834
|
|
|
|
key: test_precision
|
|
value: [0.83333333 0.71428571 0.625 0.5 0.4 0.6
|
|
0.625 0.66666667 0.66666667 0.42857143]
|
|
|
|
mean value: 0.6059523809523809
|
|
|
|
key: train_precision
|
|
value: [0.69090909 0.7037037 0.74509804 0.70909091 0.6779661 0.70588235
|
|
0.69090909 0.73076923 0.72222222 0.74509804]
|
|
|
|
mean value: 0.7121648780671712
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 0.8 0.4 0.6 1. 0.8 0.8 0.6]
|
|
|
|
mean value: 0.7999999999999999
|
|
|
|
key: train_recall
|
|
value: [0.84444444 0.84444444 0.84444444 0.86666667 0.88888889 0.8
|
|
0.84444444 0.84444444 0.86666667 0.84444444]
|
|
|
|
mean value: 0.848888888888889
|
|
|
|
key: test_accuracy
|
|
value: [0.9 0.8 0.7 0.5 0.4 0.6 0.7 0.7 0.7 0.4]
|
|
|
|
mean value: 0.6400000000000001
|
|
|
|
key: train_accuracy
|
|
value: [0.73333333 0.74444444 0.77777778 0.75555556 0.73333333 0.73333333
|
|
0.73333333 0.76666667 0.76666667 0.77777778]
|
|
|
|
mean value: 0.7522222222222222
|
|
|
|
key: test_roc_auc
|
|
value: [0.9 0.8 0.7 0.5 0.4 0.6 0.7 0.7 0.7 0.4]
|
|
|
|
mean value: 0.6400000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [0.73333333 0.74444444 0.77777778 0.75555556 0.73333333 0.73333333
|
|
0.73333333 0.76666667 0.76666667 0.77777778]
|
|
|
|
mean value: 0.7522222222222222
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.71428571 0.625 0.44444444 0.25 0.42857143
|
|
0.625 0.57142857 0.57142857 0.33333333]
|
|
|
|
mean value: 0.5396825396825397
|
|
|
|
key: train_jcc
|
|
value: [0.61290323 0.62295082 0.65517241 0.63934426 0.625 0.6
|
|
0.61290323 0.6440678 0.65 0.65517241]
|
|
|
|
mean value: 0.6317514157776494
|
|
|
|
key: TN
|
|
value: 24
|
|
|
|
mean value: 24.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: TP
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.07
|
|
|
|
Accuracy on Blind test: 0.48
|
|
|
|
Running classifier: 6
|
|
Model_name: SVC
|
|
Model func: SVC(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SVC(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00882292 0.00860882 0.00860405 0.00861382 0.00862885 0.00865412
|
|
0.00877261 0.00858068 0.00863194 0.00862861]
|
|
|
|
mean value: 0.008654642105102538
|
|
|
|
key: score_time
|
|
value: [0.00841212 0.0083425 0.00839615 0.00844121 0.00848246 0.00840759
|
|
0.00846624 0.00836325 0.00846195 0.00850344]
|
|
|
|
mean value: 0.008427691459655762
|
|
|
|
key: test_mcc
|
|
value: [0.5 0.81649658 0.65465367 0.40824829 0.5 0.65465367
|
|
0.40824829 0.6 0.40824829 0.2 ]
|
|
|
|
mean value: 0.515054879373527
|
|
|
|
key: train_mcc
|
|
value: [0.84465303 0.8001976 0.75724019 0.78086881 0.80498447 0.75724019
|
|
0.73624773 0.75724019 0.79036782 0.80498447]
|
|
|
|
mean value: 0.7834024490981437
|
|
|
|
key: test_fscore
|
|
value: [0.57142857 0.90909091 0.75 0.66666667 0.57142857 0.75
|
|
0.72727273 0.8 0.66666667 0.6 ]
|
|
|
|
mean value: 0.7012554112554111
|
|
|
|
key: train_fscore
|
|
value: [0.92134831 0.8988764 0.87356322 0.88372093 0.89411765 0.87356322
|
|
0.86046512 0.87356322 0.87804878 0.89411765]
|
|
|
|
mean value: 0.8851384495390617
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 1. 0.75 1. 1.
|
|
0.66666667 0.8 0.75 0.6 ]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_precision
|
|
value: [0.93181818 0.90909091 0.9047619 0.92682927 0.95 0.9047619
|
|
0.90243902 0.9047619 0.97297297 0.95 ]
|
|
|
|
mean value: 0.9257436070850705
|
|
|
|
key: test_recall
|
|
value: [0.4 1. 0.6 0.6 0.4 0.6 0.8 0.8 0.6 0.6]
|
|
|
|
mean value: 0.6399999999999999
|
|
|
|
key: train_recall
|
|
value: [0.91111111 0.88888889 0.84444444 0.84444444 0.84444444 0.84444444
|
|
0.82222222 0.84444444 0.8 0.84444444]
|
|
|
|
mean value: 0.8488888888888889
|
|
|
|
key: test_accuracy
|
|
value: [0.7 0.9 0.8 0.7 0.7 0.8 0.7 0.8 0.7 0.6]
|
|
|
|
mean value: 0.74
|
|
|
|
key: train_accuracy
|
|
value: [0.92222222 0.9 0.87777778 0.88888889 0.9 0.87777778
|
|
0.86666667 0.87777778 0.88888889 0.9 ]
|
|
|
|
mean value: 0.89
|
|
|
|
key: test_roc_auc
|
|
value: [0.7 0.9 0.8 0.7 0.7 0.8 0.7 0.8 0.7 0.6]
|
|
|
|
mean value: 0.74
|
|
|
|
key: train_roc_auc
|
|
value: [0.92222222 0.9 0.87777778 0.88888889 0.9 0.87777778
|
|
0.86666667 0.87777778 0.88888889 0.9 ]
|
|
|
|
mean value: 0.89
|
|
|
|
key: test_jcc
|
|
value: [0.4 0.83333333 0.6 0.5 0.4 0.6
|
|
0.57142857 0.66666667 0.5 0.42857143]
|
|
|
|
mean value: 0.55
|
|
|
|
key: train_jcc
|
|
value: [0.85416667 0.81632653 0.7755102 0.79166667 0.80851064 0.7755102
|
|
0.75510204 0.7755102 0.7826087 0.80851064]
|
|
|
|
mean value: 0.7943422489254721
|
|
|
|
key: TN
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: FP
|
|
value: 18
|
|
|
|
mean value: 18.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 32
|
|
|
|
mean value: 32.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.04
|
|
|
|
Accuracy on Blind test: 0.62
|
|
|
|
Running classifier: 7
|
|
Model_name: MLP
|
|
Model func: MLPClassifier(max_iter=500, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MLPClassifier(max_iter=500, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.46989155 0.45708156 0.47301435 0.51050806 0.4922812 0.58298516
|
|
0.6829381 0.55199766 0.33458424 0.63565779]
|
|
|
|
mean value: 0.5190939664840698
|
|
|
|
key: score_time
|
|
value: [0.01193571 0.0119791 0.01196957 0.01409888 0.01202154 0.01195478
|
|
0.01197529 0.01233053 0.01202965 0.01204038]
|
|
|
|
mean value: 0.012233543395996093
|
|
|
|
key: test_mcc
|
|
value: [1. 0.65465367 0.6 0.65465367 0.81649658 0.65465367
|
|
0.81649658 1. 0.65465367 0.21821789]
|
|
|
|
mean value: 0.7069825734923353
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.83333333 0.8 0.83333333 0.88888889 0.83333333
|
|
0.90909091 1. 0.83333333 0.66666667]
|
|
|
|
mean value: 0.8597979797979798
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.71428571 0.8 0.71428571 1. 0.71428571
|
|
0.83333333 1. 0.71428571 0.57142857]
|
|
|
|
mean value: 0.8061904761904763
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 1. 0.8 1. 1. 1. 1. 0.8]
|
|
|
|
mean value: 0.9400000000000001
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.8 0.8 0.8 0.9 0.8 0.9 1. 0.8 0.6]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.8 0.8 0.8 0.9 0.8 0.9 1. 0.8 0.6]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.71428571 0.66666667 0.71428571 0.8 0.71428571
|
|
0.83333333 1. 0.71428571 0.5 ]
|
|
|
|
mean value: 0.7657142857142858
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 37
|
|
|
|
mean value: 37.0
|
|
|
|
key: FP
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: FN
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: TP
|
|
value: 47
|
|
|
|
mean value: 47.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.12
|
|
|
|
Accuracy on Blind test: 0.6
|
|
|
|
Running classifier: 8
|
|
Model_name: Decision Tree
|
|
Model func: DecisionTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', DecisionTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01784778 0.01463175 0.00978041 0.00983381 0.00975132 0.00979137
|
|
0.01004624 0.00982881 0.00951791 0.00948429]
|
|
|
|
mean value: 0.011051368713378907
|
|
|
|
key: score_time
|
|
value: [0.01210833 0.01203465 0.0088129 0.00876093 0.00856209 0.00864077
|
|
0.00851083 0.00841141 0.00842595 0.00868106]
|
|
|
|
mean value: 0.009294891357421875
|
|
|
|
key: test_mcc
|
|
value: [1. 1. 0.81649658 1. 0.6 0.65465367
|
|
0.81649658 0.6 0.6 0.81649658]
|
|
|
|
mean value: 0.7904143413491156
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 1. 0.88888889 1. 0.8 0.83333333
|
|
0.90909091 0.8 0.8 0.90909091]
|
|
|
|
mean value: 0.894040404040404
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 1. 0.8 0.71428571
|
|
0.83333333 0.8 0.8 0.83333333]
|
|
|
|
mean value: 0.878095238095238
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 1. 0.8 1. 1. 0.8 0.8 1. ]
|
|
|
|
mean value: 0.9200000000000002
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 1. 0.9 1. 0.8 0.8 0.9 0.8 0.8 0.9]
|
|
|
|
mean value: 0.89
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 1. 0.9 1. 0.8 0.8 0.9 0.8 0.8 0.9]
|
|
|
|
mean value: 0.89
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 1. 0.8 1. 0.66666667 0.71428571
|
|
0.83333333 0.66666667 0.66666667 0.83333333]
|
|
|
|
mean value: 0.818095238095238
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: FP
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 46
|
|
|
|
mean value: 46.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.89
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 9
|
|
Model_name: Extra Trees
|
|
Model func: ExtraTreesClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreesClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.08306623 0.08243489 0.08078671 0.08000588 0.08049583 0.08101439
|
|
0.08041883 0.08083487 0.07945704 0.0797565 ]
|
|
|
|
mean value: 0.08082711696624756
|
|
|
|
key: score_time
|
|
value: [0.01788282 0.01666999 0.017452 0.01670957 0.01666594 0.01666021
|
|
0.01663876 0.01678586 0.01667166 0.01662517]
|
|
|
|
mean value: 0.01687619686126709
|
|
|
|
key: test_mcc
|
|
value: [0.5 0.81649658 0.40824829 0.65465367 0.65465367 0.81649658
|
|
0.33333333 0.6 0.40824829 0.2 ]
|
|
|
|
mean value: 0.5392130417532466
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.57142857 0.90909091 0.72727273 0.83333333 0.75 0.88888889
|
|
0.71428571 0.8 0.66666667 0.6 ]
|
|
|
|
mean value: 0.7460966810966811
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 0.66666667 0.71428571 1. 1.
|
|
0.55555556 0.8 0.75 0.6 ]
|
|
|
|
mean value: 0.7919841269841269
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.4 1. 0.8 1. 0.6 0.8 1. 0.8 0.6 0.6]
|
|
|
|
mean value: 0.76
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.7 0.9 0.7 0.8 0.8 0.9 0.6 0.8 0.7 0.6]
|
|
|
|
mean value: 0.75
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.7 0.9 0.7 0.8 0.8 0.9 0.6 0.8 0.7 0.6]
|
|
|
|
mean value: 0.75
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.4 0.83333333 0.57142857 0.71428571 0.6 0.8
|
|
0.55555556 0.66666667 0.5 0.42857143]
|
|
|
|
mean value: 0.606984126984127
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 37
|
|
|
|
mean value: 37.0
|
|
|
|
key: FP
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: FN
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: TP
|
|
value: 38
|
|
|
|
mean value: 38.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.49
|
|
|
|
Accuracy on Blind test: 0.78
|
|
|
|
Running classifier: 10
|
|
Model_name: Extra Tree
|
|
Model func: ExtraTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00919604 0.00881147 0.00983024 0.00988555 0.00845003 0.00822234
|
|
0.00817466 0.00906372 0.0087781 0.00858617]
|
|
|
|
mean value: 0.008899831771850586
|
|
|
|
key: score_time
|
|
value: [0.00908685 0.00836706 0.00952148 0.01046968 0.00823426 0.00821829
|
|
0.00818181 0.00913 0.00822067 0.00824046]
|
|
|
|
mean value: 0.008767056465148925
|
|
|
|
key: test_mcc
|
|
value: [-0.21821789 0.81649658 0.40824829 0.65465367 0.6 0.81649658
|
|
0.81649658 0.6 0.2 0.40824829]
|
|
|
|
mean value: 0.5102422104182889
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.5 0.90909091 0.72727273 0.83333333 0.8 0.90909091
|
|
0.90909091 0.8 0.6 0.66666667]
|
|
|
|
mean value: 0.7654545454545454
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.42857143 0.83333333 0.66666667 0.71428571 0.8 0.83333333
|
|
0.83333333 0.8 0.6 0.75 ]
|
|
|
|
mean value: 0.7259523809523809
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.6 1. 0.8 1. 0.8 1. 1. 0.8 0.6 0.6]
|
|
|
|
mean value: 0.82
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.4 0.9 0.7 0.8 0.8 0.9 0.9 0.8 0.6 0.7]
|
|
|
|
mean value: 0.75
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.4 0.9 0.7 0.8 0.8 0.9 0.9 0.8 0.6 0.7]
|
|
|
|
mean value: 0.75
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.33333333 0.83333333 0.57142857 0.71428571 0.66666667 0.83333333
|
|
0.83333333 0.66666667 0.42857143 0.5 ]
|
|
|
|
mean value: 0.6380952380952382
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 34
|
|
|
|
mean value: 34.0
|
|
|
|
key: FP
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: FN
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: TP
|
|
value: 41
|
|
|
|
mean value: 41.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.56
|
|
|
|
Accuracy on Blind test: 0.8
|
|
|
|
Running classifier: 11
|
|
Model_name: Random Forest
|
|
Model func: RandomForestClassifier(n_estimators=1000, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(n_estimators=1000, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [1.12863898 1.05638361 1.04797721 1.22669673 1.21202326 1.01976037
|
|
1.10352778 1.02443147 1.04925394 1.07379055]
|
|
|
|
mean value: 1.094248390197754
|
|
|
|
key: score_time
|
|
value: [0.10170126 0.09575319 0.09421945 0.10475135 0.0917902 0.09088731
|
|
0.0953505 0.08893514 0.09012246 0.09573841]
|
|
|
|
mean value: 0.0949249267578125
|
|
|
|
key: test_mcc
|
|
value: [0.65465367 0.81649658 0.6 0.81649658 0.65465367 1.
|
|
0.81649658 0.81649658 0.6 0.2 ]
|
|
|
|
mean value: 0.6975293665126858
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
[0.75 0.90909091 0.8 0.90909091 0.75 1.
|
|
0.90909091 0.88888889 0.8 0.6 ]
|
|
|
|
mean value: 0.8316161616161617
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 0.8 0.83333333 1. 1.
|
|
0.83333333 1. 0.8 0.6 ]
|
|
|
|
mean value: 0.8700000000000001
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.6 1. 0.8 1. 0.6 1. 1. 0.8 0.8 0.6]
|
|
|
|
mean value: 0.8200000000000001
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.8 0.9 0.8 0.9 0.8 1. 0.9 0.9 0.8 0.6]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.8 0.9 0.8 0.9 0.8 1. 0.9 0.9 0.8 0.6]
|
|
|
|
mean value: 0.8400000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.6 0.83333333 0.66666667 0.83333333 0.6 1.
|
|
0.83333333 0.8 0.66666667 0.42857143]
|
|
|
|
mean value: 0.7261904761904763
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: FP
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 41
|
|
|
|
mean value: 41.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.55
|
|
|
|
Accuracy on Blind test: 0.8
|
|
|
|
Running classifier: 12
|
|
Model_name: Random Forest2
|
|
Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...age_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10,
|
|
oob_score=True, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.83751202 0.89611864 0.86344433 0.82031369 0.8383913 0.87373209
|
|
0.85556459 0.87743616 0.84090114 0.83468747]
|
|
|
|
mean value: 0.8538101434707641
|
|
|
|
key: score_time
|
|
value: [0.1183486 0.17216015 0.1454649 0.1703856 0.16838622 0.20413017
|
|
0.18524504 0.20958233 0.19292188 0.14789653]
|
|
|
|
mean value: 0.17145214080810547
|
|
|
|
key: test_mcc
|
|
value: [1. 0.81649658 0.81649658 0.81649658 0.81649658 1.
|
|
0.81649658 0.6 0.6 0.2 ]
|
|
|
|
mean value: 0.748248290463863
|
|
|
|
key: train_mcc
|
|
value: [0.97801929 1. 1. 1. 0.95555556 0.95650071
|
|
0.95555556 0.95555556 0.93356387 0.93356387]
|
|
|
|
mean value: 0.9668314417898023
|
|
|
|
key: test_fscore
|
|
value: [1. 0.90909091 0.88888889 0.90909091 0.88888889 1.
|
|
0.90909091 0.8 0.8 0.6 ]
|
|
|
|
mean value: 0.8705050505050507
|
|
|
|
key: train_fscore
|
|
value: [0.98876404 1. 1. 1. 0.97777778 0.97727273
|
|
0.97777778 0.97777778 0.96629213 0.96629213]
|
|
|
|
mean value: 0.9831954375212801
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 1. 0.83333333 1. 1.
|
|
0.83333333 0.8 0.8 0.6 ]
|
|
|
|
mean value: 0.8700000000000001
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 0.97777778 1.
|
|
0.97777778 0.97777778 0.97727273 0.97727273]
|
|
|
|
mean value: 0.9887878787878787
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 1. 0.8 1. 1. 0.8 0.8 0.6]
|
|
|
|
mean value: 0.8800000000000001
|
|
|
|
key: train_recall
|
|
value: [0.97777778 1. 1. 1. 0.97777778 0.95555556
|
|
0.97777778 0.97777778 0.95555556 0.95555556]
|
|
|
|
mean value: 0.9777777777777779
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9 0.9 0.9 0.9 1. 0.9 0.8 0.8 0.6]
|
|
|
|
mean value: 0.8700000000000001
|
|
|
|
key: train_accuracy
|
|
value: [0.98888889 1. 1. 1. 0.97777778 0.97777778
|
|
0.97777778 0.97777778 0.96666667 0.96666667]
|
|
|
|
mean value: 0.9833333333333334
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.9 0.9 0.9 0.9 1. 0.9 0.8 0.8 0.6]
|
|
|
|
mean value: 0.8700000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [0.98888889 1. 1. 1. 0.97777778 0.97777778
|
|
0.97777778 0.97777778 0.96666667 0.96666667]
|
|
|
|
mean value: 0.9833333333333334
|
|
|
|
key: test_jcc
|
|
value: [1. 0.83333333 0.8 0.83333333 0.8 1.
|
|
0.83333333 0.66666667 0.66666667 0.42857143]
|
|
|
|
mean value: 0.7861904761904762
|
|
|
|
key: train_jcc
|
|
value: [0.97777778 1. 1. 1. 0.95652174 0.95555556
|
|
0.95652174 0.95652174 0.93478261 0.93478261]
|
|
|
|
mean value: 0.9672463768115943
|
|
|
|
key: TN
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 44
|
|
|
|
mean value: 44.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.67
|
|
|
|
Accuracy on Blind test: 0.85
|
|
|
|
Running classifier: 13
|
|
Model_name: XGBoost
|
|
Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...
|
|
interaction_constraints=None, learning_rate=None,
|
|
max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan,
|
|
monotone_constraints=None, n_estimators=100,
|
|
n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None,
|
|
reg_lambda=None, scale_pos_weight=None,
|
|
subsample=None, tree_method=None,
|
|
use_label_encoder=False,
|
|
validate_parameters=None, verbosity=0))])
|
|
|
|
key: fit_time
|
|
value: [0.10435414 0.03400135 0.04029894 0.03453851 0.03732896 0.03765655
|
|
0.0378046 0.0363245 0.03529716 0.03576374]
|
|
|
|
mean value: 0.0433368444442749
|
|
|
|
key: score_time
|
|
value: [0.01012611 0.00994301 0.01033235 0.01006889 0.01035452 0.01092505
|
|
0.01093435 0.01010299 0.01055551 0.01002288]
|
|
|
|
mean value: 0.010336565971374511
|
|
|
|
key: test_mcc
|
|
value: [1. 0.81649658 0.81649658 0.81649658 1. 0.81649658
|
|
0.81649658 0.81649658 1. 1. ]
|
|
|
|
mean value: 0.8898979485566356
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.90909091 0.88888889 0.90909091 1. 0.90909091
|
|
0.90909091 0.90909091 1. 1. ]
|
|
|
|
mean value: 0.9434343434343434
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 1. 0.83333333 1. 0.83333333
|
|
0.83333333 0.83333333 1. 1. ]
|
|
|
|
mean value: 0.9166666666666667
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 1. 1. 1. 1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9800000000000001
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9 0.9 0.9 1. 0.9 0.9 0.9 1. 1. ]
|
|
|
|
mean value: 0.9400000000000001
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.9 0.9 0.9 1. 0.9 0.9 0.9 1. 1. ]
|
|
|
|
mean value: 0.9400000000000001
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.83333333 0.8 0.83333333 1. 0.83333333
|
|
0.83333333 0.83333333 1. 1. ]
|
|
|
|
mean value: 0.8966666666666667
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 45
|
|
|
|
mean value: 45.0
|
|
|
|
key: FP
|
|
value: 1
|
|
|
|
mean value: 1.0
|
|
|
|
key: FN
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: TP
|
|
value: 49
|
|
|
|
mean value: 49.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.89
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 14
|
|
Model_name: LDA
|
|
Model func: LinearDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LinearDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.0220542 0.03752089 0.05885172 0.03988481 0.04153132 0.04049897
|
|
0.04043698 0.04112816 0.04124546 0.03902507]
|
|
|
|
mean value: 0.04021775722503662
|
|
|
|
key: score_time
|
|
value: [0.01765323 0.01775956 0.02387667 0.0141654 0.0216701 0.02373743
|
|
0.0230279 0.02206993 0.0226841 0.01399088]
|
|
|
|
mean value: 0.02006351947784424
|
|
|
|
key: test_mcc
|
|
value: [0.40824829 0.33333333 0.40824829 0.6 0.6 0.6
|
|
0.81649658 0.40824829 0.21821789 0.40824829]
|
|
|
|
mean value: 0.48010409663525044
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 0.97801929 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9978019293843652
|
|
|
|
key: test_fscore
|
|
value: [0.66666667 0.71428571 0.72727273 0.8 0.8 0.8
|
|
0.90909091 0.72727273 0.66666667 0.66666667]
|
|
|
|
mean value: 0.7477922077922079
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 0.98876404 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9988764044943821
|
|
|
|
key: test_precision
|
|
value: [0.75 0.55555556 0.66666667 0.8 0.8 0.8
|
|
0.83333333 0.66666667 0.57142857 0.75 ]
|
|
|
|
mean value: 0.7193650793650793
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.6 1. 0.8 0.8 0.8 0.8 1. 0.8 0.8 0.6]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 0.97777778 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9977777777777778
|
|
|
|
key: test_accuracy
|
|
value: [0.7 0.6 0.7 0.8 0.8 0.8 0.9 0.7 0.6 0.7]
|
|
|
|
mean value: 0.73
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 0.98888889 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9988888888888889
|
|
|
|
key: test_roc_auc
|
|
value: [0.7 0.6 0.7 0.8 0.8 0.8 0.9 0.7 0.6 0.7]
|
|
|
|
mean value: 0.73
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 0.98888889 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9988888888888889
|
|
|
|
key: test_jcc
|
|
value: [0.5 0.55555556 0.57142857 0.66666667 0.66666667 0.66666667
|
|
0.83333333 0.57142857 0.5 0.5 ]
|
|
|
|
mean value: 0.6031746031746031
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 0.97777778 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9977777777777778
|
|
|
|
key: TN
|
|
value: 33
|
|
|
|
mean value: 33.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 17
|
|
|
|
mean value: 17.0
|
|
|
|
key: TP
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.0
|
|
|
|
Accuracy on Blind test: 0.5
|
|
|
|
Running classifier: 15
|
|
Model_name: Multinomial
|
|
Model func: MultinomialNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MultinomialNB())])
|
|
|
|
key: fit_time
|
|
value: [0.0215857 0.00902247 0.00941372 0.00821018 0.00910091 0.00885034
|
|
0.00864315 0.00913239 0.00845718 0.00905919]
|
|
|
|
mean value: 0.010147523880004884
|
|
|
|
key: score_time
|
|
value: [0.01546955 0.00928783 0.00828052 0.00871253 0.00915241 0.00861788
|
|
0.00836587 0.00900817 0.00822067 0.00899863]
|
|
|
|
mean value: 0.00941140651702881
|
|
|
|
key: test_mcc
|
|
value: [ 1. 0.2 0.21821789 0. 0.40824829 0.21821789
|
|
0.21821789 -0.2 -0.6 -0.40824829]
|
|
|
|
mean value: 0.10546536707079768
|
|
|
|
key: train_mcc
|
|
value: [0.51161666 0.4260261 0.35564338 0.42263985 0.4260261 0.44992127
|
|
0.4472136 0.40249224 0.42222222 0.4454354 ]
|
|
|
|
mean value: 0.4309236824763807
|
|
|
|
key: test_fscore
|
|
value: [1. 0.6 0.5 0.54545455 0.72727273 0.5
|
|
0.66666667 0.4 0.2 0.36363636]
|
|
|
|
mean value: 0.5503030303030303
|
|
|
|
key: train_fscore
|
|
value: [0.75 0.69047619 0.6741573 0.70454545 0.69047619 0.69879518
|
|
0.70588235 0.68235294 0.71111111 0.71264368]
|
|
|
|
mean value: 0.7020440402981192
|
|
|
|
key: test_precision
|
|
value: [1. 0.6 0.66666667 0.5 0.66666667 0.66666667
|
|
0.57142857 0.4 0.2 0.33333333]
|
|
|
|
mean value: 0.5604761904761905
|
|
|
|
key: train_precision
|
|
value: [0.76744186 0.74358974 0.68181818 0.72093023 0.74358974 0.76315789
|
|
0.75 0.725 0.71111111 0.73809524]
|
|
|
|
mean value: 0.7344734005964116
|
|
|
|
key: test_recall
|
|
value: [1. 0.6 0.4 0.6 0.8 0.4 0.8 0.4 0.2 0.4]
|
|
|
|
mean value: 0.56
|
|
|
|
key: train_recall
|
|
value: [0.73333333 0.64444444 0.66666667 0.68888889 0.64444444 0.64444444
|
|
0.66666667 0.64444444 0.71111111 0.68888889]
|
|
|
|
mean value: 0.6733333333333335
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.6 0.6 0.5 0.7 0.6 0.6 0.4 0.2 0.3]
|
|
|
|
mean value: 0.55
|
|
|
|
key: train_accuracy
|
|
value: [0.75555556 0.71111111 0.67777778 0.71111111 0.71111111 0.72222222
|
|
0.72222222 0.7 0.71111111 0.72222222]
|
|
|
|
mean value: 0.7144444444444444
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.6 0.6 0.5 0.7 0.6 0.6 0.4 0.2 0.3]
|
|
|
|
mean value: 0.55
|
|
|
|
key: train_roc_auc
|
|
value: [0.75555556 0.71111111 0.67777778 0.71111111 0.71111111 0.72222222
|
|
0.72222222 0.7 0.71111111 0.72222222]
|
|
|
|
mean value: 0.7144444444444444
|
|
|
|
key: test_jcc
|
|
value: [1. 0.42857143 0.33333333 0.375 0.57142857 0.33333333
|
|
0.5 0.25 0.11111111 0.22222222]
|
|
|
|
mean value: 0.4125
|
|
|
|
key: train_jcc
|
|
value: [0.6 0.52727273 0.50847458 0.54385965 0.52727273 0.53703704
|
|
0.54545455 0.51785714 0.55172414 0.55357143]
|
|
|
|
mean value: 0.5412523971790637
|
|
|
|
key: TN
|
|
value: 27
|
|
|
|
mean value: 27.0
|
|
|
|
key: FP
|
|
value: 22
|
|
|
|
mean value: 22.0
|
|
|
|
key: FN
|
|
value: 23
|
|
|
|
mean value: 23.0
|
|
|
|
key: TP
|
|
value: 28
|
|
|
|
mean value: 28.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.14
|
|
|
|
Accuracy on Blind test: 0.48
|
|
|
|
Running classifier: 16
|
|
Model_name: Passive Aggresive
|
|
Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
PassiveAggressiveClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01105547 0.01381016 0.01291037 0.01365805 0.01394439 0.01359439
|
|
0.01429915 0.01413941 0.01459932 0.01359248]
|
|
|
|
mean value: 0.013560318946838379
|
|
|
|
key: score_time
|
|
value: [0.00817347 0.0110867 0.01157665 0.01226854 0.01200342 0.01189685
|
|
0.01202917 0.01192331 0.01188517 0.01133728]
|
|
|
|
mean value: 0.01141805648803711
|
|
|
|
key: test_mcc
|
|
value: [1. 0.65465367 0.6 0.65465367 0.81649658 0.65465367
|
|
0.33333333 0.81649658 0.65465367 0. ]
|
|
|
|
mean value: 0.6184941178020693
|
|
|
|
key: train_mcc
|
|
value: [0.97801929 0.93541435 0.97801929 0.89442719 0.97801929 0.97801929
|
|
0.79772404 0.97801929 1. 0.97801929]
|
|
|
|
mean value: 0.9495681335972777
|
|
|
|
key: test_fscore
|
|
value: [1. 0.83333333 0.8 0.83333333 0.88888889 0.83333333
|
|
0.71428571 0.90909091 0.83333333 0.54545455]
|
|
|
|
mean value: 0.8191053391053391
|
|
|
|
key: train_fscore
|
|
value: [0.98876404 0.96774194 0.98901099 0.94736842 0.98876404 0.98901099
|
|
0.9 0.98901099 1. 0.98901099]
|
|
|
|
mean value: 0.9748682402468098
|
|
|
|
key: test_precision
|
|
value: [1. 0.71428571 0.8 0.71428571 1. 0.71428571
|
|
0.55555556 0.83333333 0.71428571 0.5 ]
|
|
|
|
mean value: 0.7546031746031747
|
|
|
|
key: train_precision
|
|
value: [1. 0.9375 0.97826087 0.9 1. 0.97826087
|
|
0.81818182 0.97826087 1. 0.97826087]
|
|
|
|
mean value: 0.9568725296442688
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 1. 0.8 1. 1. 1. 1. 0.6]
|
|
|
|
mean value: 0.9199999999999999
|
|
|
|
key: train_recall
|
|
value: [0.97777778 1. 1. 1. 0.97777778 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9955555555555555
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.8 0.8 0.8 0.9 0.8 0.6 0.9 0.8 0.5]
|
|
|
|
mean value: 0.79
|
|
|
|
key: train_accuracy
|
|
value: [0.98888889 0.96666667 0.98888889 0.94444444 0.98888889 0.98888889
|
|
0.88888889 0.98888889 1. 0.98888889]
|
|
|
|
mean value: 0.9733333333333334
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.8 0.8 0.8 0.9 0.8 0.6 0.9 0.8 0.5]
|
|
|
|
mean value: 0.79
|
|
|
|
key: train_roc_auc
|
|
value: [0.98888889 0.96666667 0.98888889 0.94444444 0.98888889 0.98888889
|
|
0.88888889 0.98888889 1. 0.98888889]
|
|
|
|
mean value: 0.9733333333333333
|
|
|
|
key: test_jcc
|
|
value: [1. 0.71428571 0.66666667 0.71428571 0.8 0.71428571
|
|
0.55555556 0.83333333 0.71428571 0.375 ]
|
|
|
|
mean value: 0.7087698412698413
|
|
|
|
key: train_jcc
|
|
value: [0.97777778 0.9375 0.97826087 0.9 0.97777778 0.97826087
|
|
0.81818182 0.97826087 1. 0.97826087]
|
|
|
|
mean value: 0.9524280851998244
|
|
|
|
key: TN
|
|
value: 33
|
|
|
|
mean value: 33.0
|
|
|
|
key: FP
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: FN
|
|
value: 17
|
|
|
|
mean value: 17.0
|
|
|
|
key: TP
|
|
value: 46
|
|
|
|
mean value: 46.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.31
|
|
|
|
Accuracy on Blind test: 0.65
|
|
|
|
Running classifier: 17
|
|
Model_name: Stochastic GDescent
|
|
Model func: SGDClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SGDClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00869608 0.01205468 0.0119195 0.01242995 0.01249123 0.01260424
|
|
0.0124619 0.01284695 0.01233792 0.01319003]
|
|
|
|
mean value: 0.01210324764251709
|
|
|
|
key: score_time
|
|
value: [0.00816989 0.01123571 0.0111959 0.01133227 0.0114491 0.01152349
|
|
0.01132083 0.01165223 0.01228309 0.01179123]
|
|
|
|
mean value: 0.01119537353515625
|
|
|
|
key: test_mcc
|
|
value: [1. 0.5 0.81649658 0.65465367 0.65465367 0.65465367
|
|
1. 0.33333333 0.40824829 0.5 ]
|
|
|
|
mean value: 0.6522039216848854
|
|
|
|
key: train_mcc
|
|
value: [0.95650071 0.91201231 1. 1. 0.93541435 1.
|
|
0.91201231 0.39223227 0.82962978 0.72486118]
|
|
|
|
mean value: 0.8662662908352459
|
|
|
|
key: test_fscore
|
|
value: [1. 0.76923077 0.88888889 0.83333333 0.75 0.83333333
|
|
1. 0.33333333 0.72727273 0.76923077]
|
|
|
|
mean value: 0.7904623154623154
|
|
|
|
key: train_fscore
|
|
value: [0.97826087 0.95652174 1. 1. 0.96551724 1.
|
|
0.95454545 0.42105263 0.9047619 0.86538462]
|
|
|
|
mean value: 0.9046044456345884
|
|
|
|
key: test_precision
|
|
value: [1. 0.625 1. 0.71428571 1. 0.71428571
|
|
1. 1. 0.66666667 0.625 ]
|
|
|
|
mean value: 0.8345238095238094
|
|
|
|
key: train_precision
|
|
value: [0.95744681 0.93617021 1. 1. 1. 1.
|
|
0.97674419 1. 0.97435897 0.76271186]
|
|
|
|
mean value: 0.9607432046088862
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 1. 0.6 1. 1. 0.2 0.8 1. ]
|
|
|
|
mean value: 0.8399999999999999
|
|
|
|
key: train_recall
|
|
value: [1. 0.97777778 1. 1. 0.93333333 1.
|
|
0.93333333 0.26666667 0.84444444 1. ]
|
|
|
|
mean value: 0.8955555555555555
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.7 0.9 0.8 0.8 0.8 1. 0.6 0.7 0.7]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_accuracy
|
|
value: [0.97777778 0.95555556 1. 1. 0.96666667 1.
|
|
0.95555556 0.63333333 0.91111111 0.84444444]
|
|
|
|
mean value: 0.9244444444444445
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.7 0.9 0.8 0.8 0.8 1. 0.6 0.7 0.7]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_roc_auc
|
|
value: [0.97777778 0.95555556 1. 1. 0.96666667 1.
|
|
0.95555556 0.63333333 0.91111111 0.84444444]
|
|
|
|
mean value: 0.9244444444444445
|
|
|
|
key: test_jcc
|
|
value: [1. 0.625 0.8 0.71428571 0.6 0.71428571
|
|
1. 0.2 0.57142857 0.625 ]
|
|
|
|
mean value: 0.6849999999999999
|
|
|
|
key: train_jcc
|
|
value: [0.95744681 0.91666667 1. 1. 0.93333333 1.
|
|
0.91304348 0.26666667 0.82608696 0.76271186]
|
|
|
|
mean value: 0.8575955774366694
|
|
|
|
key: TN
|
|
value: 38
|
|
|
|
mean value: 38.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: TP
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.26
|
|
|
|
Accuracy on Blind test: 0.65
|
|
|
|
Running classifier: 18
|
|
Model_name: AdaBoost Classifier
|
|
Model func: AdaBoostClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', AdaBoostClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.0937829 0.08539081 0.08131576 0.08085942 0.081738 0.07984424
|
|
0.08620453 0.08208632 0.08311081 0.0840354 ]
|
|
|
|
mean value: 0.08383681774139404
|
|
|
|
key: score_time
|
|
value: [0.01697683 0.01498771 0.01476073 0.0150857 0.01448298 0.01486278
|
|
0.01463366 0.01593375 0.0157733 0.01465559]
|
|
|
|
mean value: 0.015215301513671875
|
|
|
|
key: test_mcc
|
|
value: [1. 0.65465367 0.81649658 0.81649658 0.81649658 0.65465367
|
|
1. 0.81649658 0.6 1. ]
|
|
|
|
mean value: 0.8175293665126858
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.83333333 0.88888889 0.90909091 0.90909091 0.83333333
|
|
1. 0.90909091 0.8 1. ]
|
|
|
|
mean value: 0.9082828282828282
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.71428571 1. 0.83333333 0.83333333 0.71428571
|
|
1. 0.83333333 0.8 1. ]
|
|
|
|
mean value: 0.8728571428571428
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 1. 1. 1. 1. 1. 0.8 1. ]
|
|
|
|
mean value: 0.96
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.8 0.9 0.9 0.9 0.8 1. 0.9 0.8 1. ]
|
|
|
|
mean value: 0.9
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.8 0.9 0.9 0.9 0.8 1. 0.9 0.8 1. ]
|
|
|
|
mean value: 0.9
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.71428571 0.8 0.83333333 0.83333333 0.71428571
|
|
1. 0.83333333 0.66666667 1. ]
|
|
|
|
mean value: 0.8395238095238096
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: FP
|
|
value: 2
|
|
|
|
mean value: 2.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 48
|
|
|
|
mean value: 48.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.89
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 19
|
|
Model_name: Bagging Classifier
|
|
Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
BaggingClassifier(n_jobs=10, oob_score=True,
|
|
random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02795291 0.029001 0.03056097 0.02775407 0.02784705 0.02628183
|
|
0.02716231 0.02904415 0.03186536 0.02968788]
|
|
|
|
mean value: 0.028715753555297853
|
|
|
|
key: score_time
|
|
value: [0.01913691 0.01803756 0.01878023 0.01808381 0.01785254 0.02040362
|
|
0.02372098 0.02432561 0.02418256 0.02507067]
|
|
|
|
mean value: 0.02095944881439209
|
|
|
|
key: test_mcc
|
|
value: [1. 0.81649658 0.81649658 1. 0.81649658 0.81649658
|
|
0.81649658 0.81649658 0.81649658 1. ]
|
|
|
|
mean value: 0.8715476066494082
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 0.97801929 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9978019293843652
|
|
|
|
key: test_fscore
|
|
value: [1. 0.90909091 0.88888889 1. 0.88888889 0.90909091
|
|
0.90909091 0.90909091 0.88888889 1. ]
|
|
|
|
mean value: 0.9303030303030303
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 0.98876404 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9988764044943821
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 1. 1. 1. 0.83333333
|
|
0.83333333 0.83333333 1. 1. ]
|
|
|
|
mean value: 0.9333333333333333
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 1. 0.8 1. 1. 1. 0.8 1. ]
|
|
|
|
mean value: 0.9400000000000001
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 0.97777778 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9977777777777778
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9 0.9 1. 0.9 0.9 0.9 0.9 0.9 1. ]
|
|
|
|
mean value: 0.93
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 0.98888889 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9988888888888889
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.9 0.9 1. 0.9 0.9 0.9 0.9 0.9 1. ]
|
|
|
|
mean value: 0.93
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 0.98888889 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9988888888888889
|
|
|
|
key: test_jcc
|
|
value: [1. 0.83333333 0.8 1. 0.8 0.83333333
|
|
0.83333333 0.83333333 0.8 1. ]
|
|
|
|
mean value: 0.8733333333333334
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 0.97777778 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9977777777777778
|
|
|
|
key: TN
|
|
value: 46
|
|
|
|
mean value: 46.0
|
|
|
|
key: FP
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: FN
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: TP
|
|
value: 47
|
|
|
|
mean value: 47.0
|
|
|
|
key: trainingY_neg /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.95
|
|
|
|
Accuracy on Blind test: 0.98
|
|
|
|
Running classifier: 20
|
|
Model_name: Gaussian Process
|
|
Model func: GaussianProcessClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianProcessClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01426291 0.01899099 0.06119943 0.03039908 0.03261042 0.03212762
|
|
0.01616931 0.01589775 0.01599836 0.0489819 ]
|
|
|
|
mean value: 0.028663778305053712
|
|
|
|
key: score_time
|
|
value: [0.01212502 0.01236367 0.01209068 0.01212645 0.01205349 0.01278925
|
|
0.01178861 0.01174092 0.01172686 0.01259255]
|
|
|
|
mean value: 0.01213974952697754
|
|
|
|
key: test_mcc
|
|
value: [0.21821789 0.81649658 0.40824829 0.65465367 0.65465367 1.
|
|
0.5 0.6 0.2 0.21821789]
|
|
|
|
mean value: 0.5270487993279529
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.5 0.90909091 0.72727273 0.83333333 0.75 1.
|
|
0.76923077 0.8 0.6 0.66666667]
|
|
|
|
mean value: 0.7555594405594406
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.66666667 0.83333333 0.66666667 0.71428571 1. 1.
|
|
0.625 0.8 0.6 0.57142857]
|
|
|
|
mean value: 0.7477380952380951
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.4 1. 0.8 1. 0.6 1. 1. 0.8 0.6 0.8]
|
|
|
|
mean value: 0.8
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.6 0.9 0.7 0.8 0.8 1. 0.7 0.8 0.6 0.6]
|
|
|
|
mean value: 0.7499999999999999
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.6 0.9 0.7 0.8 0.8 1. 0.7 0.8 0.6 0.6]
|
|
|
|
mean value: 0.75
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.33333333 0.83333333 0.57142857 0.71428571 0.6 1.
|
|
0.625 0.66666667 0.42857143 0.5 ]
|
|
|
|
mean value: 0.6272619047619048
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 35
|
|
|
|
mean value: 35.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: TP
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.03
|
|
|
|
Accuracy on Blind test: 0.52
|
|
|
|
Running classifier: 21
|
|
Model_name: Gradient Boosting
|
|
Model func: GradientBoostingClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GradientBoostingClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.19596338 0.1739645 0.1458776 0.17693067 0.1970973 0.17478251
|
|
0.19787335 0.19073057 0.1710403 0.17166138]
|
|
|
|
mean value: 0.17959215641021728
|
|
|
|
key: score_time
|
|
value: [0.00906062 0.0088172 0.00977397 0.00960827 0.00882506 0.00957179
|
|
0.00906038 0.00920892 0.00884843 0.00902319]
|
|
|
|
mean value: 0.009179782867431641
|
|
|
|
key: test_mcc
|
|
value: [1. 0.81649658 0.81649658 0.81649658 1. 0.65465367
|
|
0.81649658 0.81649658 1. 1. ]
|
|
|
|
mean value: 0.8737136575346607
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.90909091 0.88888889 0.90909091 1. 0.83333333
|
|
0.90909091 0.90909091 1. 1. ]
|
|
|
|
mean value: 0.9358585858585858
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 1. 0.83333333 1. 0.71428571
|
|
0.83333333 0.83333333 1. 1. ]
|
|
|
|
mean value: 0.9047619047619048
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 1. 1. 1. 1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9800000000000001
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9 0.9 0.9 1. 0.8 0.9 0.9 1. 1. ]
|
|
|
|
mean value: 0.93
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.9 0.9 0.9 1. 0.8 0.9 0.9 1. 1. ]
|
|
|
|
mean value: 0.93
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.83333333 0.8 0.83333333 1. 0.71428571
|
|
0.83333333 0.83333333 1. 1. ]
|
|
|
|
mean value: 0.8847619047619049
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 44
|
|
|
|
mean value: 44.0
|
|
|
|
key: FP
|
|
value: 1
|
|
|
|
mean value: 1.0
|
|
|
|
key: FN
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: TP
|
|
value: 49
|
|
|
|
mean value: 49.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.95
|
|
|
|
Accuracy on Blind test: 0.98
|
|
|
|
Running classifier: 22
|
|
Model_name: QDA
|
|
Model func: QuadraticDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', QuadraticDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.01014853 0.01743078 0.01398611 0.01395059 0.0140667 0.01543117
|
|
0.01630807 0.03985023 0.01398015 0.01428962]
|
|
|
|
mean value: 0.016944193840026857
|
|
|
|
key: score_time
|
|
value: [0.01163626 0.01192975 0.01302242 0.01280975 0.01169062 0.01553082
|
|
0.01562738 0.0132699 0.01170063 0.01315784]
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
|
|
_warn_prf(average, modifier, msg_start, len(result))
|
|
|
|
mean value: 0.013037538528442383
|
|
|
|
key: test_mcc
|
|
value: [0.5 1. 0.81649658 0.81649658 0.65465367 0.81649658
|
|
1. 0.65465367 0.5 0.65465367]
|
|
|
|
mean value: 0.7413450754907109
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.57142857 1. 0.88888889 0.88888889 0.75 0.88888889
|
|
1. 0.75 0.57142857 0.75 ]
|
|
|
|
mean value: 0.805952380952381
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.4 1. 0.8 0.8 0.6 0.8 1. 0.6 0.4 0.6]
|
|
|
|
mean value: 0.7
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.7 1. 0.9 0.9 0.8 0.9 1. 0.8 0.7 0.8]
|
|
|
|
mean value: 0.85
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.7 1. 0.9 0.9 0.8 0.9 1. 0.8 0.7 0.8]
|
|
|
|
mean value: 0.85
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.4 1. 0.8 0.8 0.6 0.8 1. 0.6 0.4 0.6]
|
|
|
|
mean value: 0.7
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: FP
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: FN
|
|
value: 0
|
|
|
|
mean value: 0.0
|
|
|
|
key: TP
|
|
value: 35
|
|
|
|
mean value: 35.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.0
|
|
|
|
Accuracy on Blind test: 0.65
|
|
|
|
Running classifier: 23
|
|
Model_name: Ridge Classifier
|
|
Model func: RidgeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02866507 0.03145432 0.03152037 0.03154778 0.03143167 0.03134251
|
|
0.03164673 0.03147626 0.03186297 0.03150988]
|
|
|
|
mean value: 0.03124575614929199
|
|
|
|
key: score_time
|
|
value: [0.01959229 0.0194881 0.0223341 0.01981282 0.01142001 0.01322937
|
|
0.01942945 0.02004266 0.02273679 0.02062583]
|
|
|
|
mean value: 0.018871140480041505
|
|
|
|
key: test_mcc
|
|
value: [1. 0.65465367 0.6 0.65465367 0.81649658 0.65465367
|
|
0.81649658 0.81649658 0.6 0. ]
|
|
|
|
mean value: 0.661345075490711
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
0.97801929 1. 1. 0.97801929]
|
|
|
|
mean value: 0.9956038587687303
|
|
|
|
key: test_fscore
|
|
value: [1. 0.83333333 0.8 0.83333333 0.88888889 0.83333333
|
|
0.90909091 0.88888889 0.8 0.54545455]
|
|
|
|
mean value: 0.8332323232323233
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
0.98901099 1. 1. 0.98901099]
|
|
|
|
mean value: 0.9978021978021978
|
|
|
|
key: test_precision
|
|
value: [1. 0.71428571 0.8 0.71428571 1. 0.71428571
|
|
0.83333333 1. 0.8 0.5 ]
|
|
|
|
mean value: 0.8076190476190476
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
0.97826087 1. 1. 0.97826087]
|
|
|
|
mean value: 0.9956521739130434
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 1. 0.8 1. 1. 0.8 0.8 0.6]
|
|
|
|
mean value: 0.8800000000000001
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.8 0.8 0.8 0.9 0.8 0.9 0.9 0.8 0.5]
|
|
|
|
mean value: 0.82
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
0.98888889 1. 1. 0.98888889]
|
|
|
|
mean value: 0.9977777777777778
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.8 0.8 0.8 0.9 0.8 0.9 0.9 0.8 0.5]
|
|
|
|
mean value: 0.82
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
0.98888889 1. 1. 0.98888889]
|
|
|
|
mean value: 0.9977777777777778
|
|
|
|
key: test_jcc
|
|
value: [1. 0.71428571 0.66666667 0.71428571 0.8 0.71428571
|
|
0.83333333 0.8 0.66666667 0.375 ]
|
|
|
|
mean value: 0.728452380952381
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
0.97826087 1. 1. 0.97826087]
|
|
|
|
mean value: 0.9956521739130434
|
|
|
|
key: TN
|
|
value: 38
|
|
|
|
mean value: 38.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: TP
|
|
value: 44
|
|
|
|
mean value: 44.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: 0.16
|
|
|
|
Accuracy on Blind test: 0.62
|
|
|
|
Running classifier: 24
|
|
Model_name: Ridge ClassifierCV
|
|
Model func: RidgeClassifierCV(cv=10)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=167)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifierCV(cv=10))])
|
|
|
|
key: fit_time
|
|
value: [0.18429089 0.17889333 0.2217567 0.20420456 0.27858353 0.26535463
|
|
0.194134 0.18192577 0.20066214 0.18395448]
|
|
|
|
mean value: 0.20937600135803222
|
|
|
|
key: score_time
|
|
value: [0.02141452 0.0196619 0.02247691 0.02075529 0.01194358 0.02149749
|
|
0.02219009 0.02246118 0.0197928 0.0219295 ]
|
|
|
|
mean value: 0.020412325859069824
|
|
|
|
key: test_mcc
|
|
value: [1. 0.81649658 0.6 0.81649658 1. 0.21821789
|
|
0.81649658 0.6 0.6 0. ]
|
|
|
|
mean value: 0.6467707633019171
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 1. 1. 0.97801929]
|
|
|
|
mean value: 0.9978019293843652
|
|
|
|
key: test_fscore
|
|
value: [1. 0.90909091 0.8 0.90909091 1. 0.66666667
|
|
0.90909091 0.8 0.8 0.54545455]
|
|
|
|
mean value: 0.833939393939394
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 1. 1. 0.98901099]
|
|
|
|
mean value: 0.9989010989010989
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 0.8 0.83333333 1. 0.57142857
|
|
0.83333333 0.8 0.8 0.5 ]
|
|
|
|
mean value: 0.7971428571428572
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 1. 1. 0.97826087]
|
|
|
|
mean value: 0.9978260869565216
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.8 1. 1. 0.8 1. 0.8 0.8 0.6]
|
|
|
|
mean value: 0.8800000000000001
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9 0.8 0.9 1. 0.6 0.9 0.8 0.8 0.5]
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:432: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rouC_CV['Data_source'] = 'CV'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:433: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rouC_CV['Resampling'] = rs_rouC
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:438: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rouC_BT['Data_source'] = 'BT'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:439: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rouC_BT['Resampling'] = rs_rouC
|
|
|
|
mean value: 0.82
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 1. 1. 0.98888889]
|
|
|
|
mean value: 0.9988888888888889
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.9 0.8 0.9 1. 0.6 0.9 0.8 0.8 0.5]
|
|
|
|
mean value: 0.82
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 1. 1. 0.98888889]
|
|
|
|
mean value: 0.9988888888888889
|
|
|
|
key: test_jcc
|
|
value: [1. 0.83333333 0.66666667 0.83333333 1. 0.5
|
|
0.83333333 0.66666667 0.66666667 0.375 ]
|
|
|
|
mean value: 0.7375
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 1. 1. 0.97826087]
|
|
|
|
mean value: 0.9978260869565216
|
|
|
|
key: TN
|
|
value: 38
|
|
|
|
mean value: 38.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: TP
|
|
value: 44
|
|
|
|
mean value: 44.0
|
|
|
|
key: trainingY_neg
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: trainingY_pos
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: blindY_neg
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: blindY_pos
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
MCC on Blind test: -0.03
|
|
|
|
Accuracy on Blind test: 0.52
|
|
|
|
PASS: sorting df by score that is mapped onto the order I want
|
|
|
|
Concatenating dfs with different resampling methods [WF]: 70/30
|
|
No. of dfs combining: 10
|
|
The sampling methods are:
|
|
Resampling
|
|
Logistic Regression none
|
|
Logistic Regression smnc
|
|
Logistic Regression ros
|
|
Logistic Regression rus
|
|
Logistic Regression rouC
|
|
|
|
PASS: 10 dfs successfully combined
|
|
nrows in combined_df_wf: 240
|
|
ncols in combined_df_wf: 9
|
|
|
|
Concatenating dfs with different resampling methods: 70/30
|
|
No. of dfs combining: 5
|
|
The sampling methods are:
|
|
Resampling training_size
|
|
0 none 79
|
|
24 smnc 100
|
|
48 ros 100
|
|
72 rus 58
|
|
96 rouC 100
|
|
|
|
PASS: 5 dfs successfully combined
|
|
nrows in combined_df: 120
|
|
ncols in combined_df: 32
|
|
|
|
File successfully written: /home/tanu/git/Data/streptomycin/output/ml/tts_7030/gid_baselineC_7030.csv
|
|
|
|
File successfully written: /home/tanu/git/Data/streptomycin/output/ml/tts_7030/gid_baselineC_ext_7030.csv
|