25943 lines
942 KiB
Text
25943 lines
942 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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/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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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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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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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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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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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: 817
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PASS: my_features_df and aa_df successfully combined
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nrows: 817
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ncols: 269
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count of NULL values before imputation
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or_mychisq 244
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log10_or_mychisq 244
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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: 174
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PASS: ML data with input features, training and test generated...
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Total no. of input features: 174
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--------No. of numerical features: 168
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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: 5
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--------Common affinity cols: 3
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--------Gene specific affinity cols: 2
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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: 467
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Train data size: (312, 174)
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y_train numbers: Counter({1: 206, 0: 106})
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Test data size: (155, 174)
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y_test_numbers: Counter({1: 103, 0: 52})
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y_train ratio: 0.5145631067961165
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y_test ratio: 0.5048543689320388
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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({1: 206, 0: 106}) Data dim: (312, 174)
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Simple Random OverSampling
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Counter({1: 206, 0: 206})
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(412, 174)
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Simple Random UnderSampling
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Counter({0: 106, 1: 106})
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(212, 174)
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Simple Combined Over and UnderSampling
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Counter({0: 206, 1: 206})
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(412, 174)
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SMOTE_NC OverSampling
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Counter({1: 206, 0: 206})
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(412, 174)
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#####################################################################
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Running ML analysis: feature groups
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Gene name: katG
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Drug name: isoniazid
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Output directory: /home/tanu/git/Data/isoniazid/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=168)),
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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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|
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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/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
|
|
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(
|
|
/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(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer 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/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
|
|
key: fit_time
|
|
value: [0.03077984 0.03297734 0.03392029 0.03392172 0.03496599 0.03346992
|
|
0.03359389 0.03486037 0.03373337 0.03370595]
|
|
|
|
mean value: 0.03359286785125733
|
|
|
|
key: score_time
|
|
value: [0.0124476 0.01199484 0.01336312 0.01245666 0.0124948 0.01370454
|
|
0.01242924 0.01259065 0.012465 0.01205707]
|
|
|
|
mean value: 0.012600350379943847
|
|
|
|
key: test_mcc
|
|
value: [0.79772404 0.78959188 0.77484502 0.77484502 0.77484502 0.70992957
|
|
0.78625916 0.64203411 0.79524277 0.79524277]
|
|
|
|
mean value: 0.7640559345359147
|
|
|
|
key: train_mcc
|
|
value: [0.86371491 0.89599932 0.8564766 0.87250517 0.88150779 0.86449998
|
|
0.87995543 0.85567237 0.85594873 0.90448686]
|
|
|
|
mean value: 0.8730767160189149
|
|
|
|
key: test_fscore
|
|
value: [0.93333333 0.93023256 0.93023256 0.93023256 0.93023256 0.91304348
|
|
0.92682927 0.87804878 0.93023256 0.93023256]
|
|
|
|
mean value: 0.9232650209211899
|
|
|
|
key: train_fscore
|
|
value: [0.95514512 0.96551724 0.95238095 0.95744681 0.96062992 0.95466667
|
|
0.96042216 0.95263158 0.95287958 0.96842105]
|
|
|
|
mean value: 0.9580141085250089
|
|
|
|
key: test_precision
|
|
value: [0.875 0.90909091 0.90909091 0.90909091 0.90909091 0.84
|
|
0.9047619 0.85714286 0.86956522 0.86956522]
|
|
|
|
mean value: 0.8852398833051007
|
|
|
|
key: train_precision
|
|
value: [0.93298969 0.94791667 0.93264249 0.94240838 0.93367347 0.94210526
|
|
0.94300518 0.93298969 0.92857143 0.94845361]
|
|
|
|
mean value: 0.93847558628316
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 0.95238095 0.95238095 0.95238095 1.
|
|
0.95 0.9 1. 1. ]
|
|
|
|
mean value: 0.9659523809523808
|
|
|
|
key: train_recall
|
|
value: [0.97837838 0.98378378 0.97297297 0.97297297 0.98918919 0.96756757
|
|
0.97849462 0.97311828 0.97849462 0.98924731]
|
|
|
|
mean value: 0.9784219703574543
|
|
|
|
key: test_accuracy
|
|
value: [0.90625 0.90625 0.90322581 0.90322581 0.90322581 0.87096774
|
|
0.90322581 0.83870968 0.90322581 0.90322581]
|
|
|
|
mean value: 0.8941532258064514
|
|
|
|
key: train_accuracy
|
|
value: [0.93928571 0.95357143 0.93594306 0.9430605 0.94661922 0.93950178
|
|
0.94661922 0.93594306 0.93594306 0.95729537]
|
|
|
|
mean value: 0.9433782409761058
|
|
|
|
key: test_roc_auc
|
|
value: [0.86363636 0.88528139 0.87619048 0.87619048 0.87619048 0.8
|
|
0.88409091 0.81363636 0.86363636 0.86363636]
|
|
|
|
mean value: 0.8602489177489178
|
|
|
|
key: train_roc_auc
|
|
value: [0.92076814 0.93926031 0.91877815 0.92919482 0.92688626 0.92649212
|
|
0.93135257 0.91813809 0.9155631 0.94199208]
|
|
|
|
mean value: 0.9268425641260956
|
|
|
|
key: test_jcc
|
|
value: [0.875 0.86956522 0.86956522 0.86956522 0.86956522 0.84
|
|
0.86363636 0.7826087 0.86956522 0.86956522]
|
|
|
|
mean value: 0.8578636363636363
|
|
|
|
key: train_jcc
|
|
value: [0.91414141 0.93333333 0.90909091 0.91836735 0.92424242 0.91326531
|
|
0.92385787 0.90954774 0.91 0.93877551]
|
|
|
|
mean value: 0.9194621850787158
|
|
|
|
key: TN
|
|
value: 80
|
|
|
|
mean value: 80.0
|
|
|
|
key: FP
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: FN
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: TP
|
|
value: 199
|
|
|
|
mean value: 199.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.79
|
|
|
|
Accuracy on Blind test: 0.91
|
|
|
|
Running classifier: 2
|
|
Model_name: Logistic RegressionCV
|
|
Model func: LogisticRegressionCV(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegressionCV(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.70210862 0.69636774 0.83434844 0.7121985 0.71469593 0.83674455
|
|
0.72348166 0.69391394 0.79157877 0.70643449]
|
|
|
|
mean value: 0.7411872625350953
|
|
|
|
key: score_time
|
|
value: [0.01284766 0.01272511 0.01273108 0.01270127 0.0127151 0.01370049
|
|
0.0127027 0.01273274 0.01376271 0.01266599]
|
|
|
|
mean value: 0.012928485870361328
|
|
|
|
key: test_mcc
|
|
value: [1. 0.93154098 0.86831345 0.69695062 1. 0.78625916
|
|
0.65635466 0.73603286 0.78625916 0.86243936]
|
|
|
|
mean value: 0.8324150242970061
|
|
|
|
key: train_mcc
|
|
value: [0.98411246 1. 1. 0.98422269 0.99210029 1.
|
|
1. 1. 1. 0.98414076]
|
|
|
|
mean value: 0.9944576204129685
|
|
|
|
key: test_fscore
|
|
value: [1. 0.97674419 0.95 0.90909091 1. 0.92682927
|
|
0.87179487 0.89473684 0.92682927 0.95238095]
|
|
|
|
mean value: 0.9408406298003875
|
|
|
|
key: train_fscore
|
|
value: [0.99462366 1. 1. 0.99462366 0.99730458 1.
|
|
1. 1. 1. 0.99465241]
|
|
|
|
mean value: 0.9981204300455312
|
|
|
|
key: test_precision
|
|
value: [1. 0.95454545 1. 0.86956522 1. 0.95
|
|
0.89473684 0.94444444 0.9047619 0.90909091]
|
|
|
|
mean value: 0.9427144772339279
|
|
|
|
key: train_precision
|
|
value: [0.98930481 1. 1. 0.98930481 0.99462366 1.
|
|
1. 1. 1. 0.9893617 ]
|
|
|
|
mean value: 0.9962594983710087
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.9047619 0.95238095 1. 0.9047619
|
|
0.85 0.85 0.95 1. ]
|
|
|
|
mean value: 0.9411904761904761
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.96875 0.93548387 0.87096774 1. 0.90322581
|
|
0.83870968 0.87096774 0.90322581 0.93548387]
|
|
|
|
mean value: 0.9226814516129032
|
|
|
|
key: train_accuracy
|
|
value: [0.99285714 1. 1. 0.99288256 0.99644128 1.
|
|
1. 1. 1. 0.99288256]
|
|
|
|
mean value: 0.9975063548551093
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.95454545 0.95238095 0.82619048 1. 0.90238095
|
|
0.83409091 0.87954545 0.88409091 0.90909091]
|
|
|
|
mean value: 0.9142316017316017
|
|
|
|
key: train_roc_auc
|
|
value: [0.98947368 1. 1. 0.98958333 0.99479167 1.
|
|
1. 1. 1. 0.98947368]
|
|
|
|
mean value: 0.9963322368421054
|
|
|
|
key: test_jcc
|
|
value: [1. 0.95454545 0.9047619 0.83333333 1. 0.86363636
|
|
0.77272727 0.80952381 0.86363636 0.90909091]
|
|
|
|
mean value: 0.8911255411255411
|
|
|
|
key: train_jcc
|
|
value: [0.98930481 1. 1. 0.98930481 0.99462366 1.
|
|
1. 1. 1. 0.9893617 ]
|
|
|
|
mean value: 0.9962594983710087
|
|
|
|
key: TN
|
|
value: 92
|
|
|
|
mean value: 92.0
|
|
|
|
key: FP
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: FN
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: TP
|
|
value: 194
|
|
|
|
mean value: 194.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.86
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
Running classifier: 3
|
|
Model_name: Gaussian NB
|
|
Model func: GaussianNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianNB())])
|
|
|
|
key: fit_time
|
|
value: [0.0132184 0.01286697 0.00963426 0.00930786 0.00908041 0.00924706
|
|
0.00901842 0.00894332 0.00898361 0.00899673]
|
|
|
|
mean value: 0.009929704666137695
|
|
|
|
key: score_time
|
|
value: [0.01198387 0.01038861 0.00893116 0.00865841 0.00870872 0.00867224
|
|
0.00869894 0.00875521 0.00867367 0.00875235]
|
|
|
|
mean value: 0.009222316741943359
|
|
|
|
key: test_mcc
|
|
value: [0.47306844 0.39072951 0.69695062 0.20935895 0.7047619 0.36059915
|
|
0.29545455 0.27532188 0.48992888 0.40572206]
|
|
|
|
mean value: 0.4301895937784511
|
|
|
|
key: train_mcc
|
|
value: [0.41534408 0.48082512 0.4751815 0.49567937 0.41755413 0.46123131
|
|
0.46864391 0.47740752 0.47255597 0.47217054]
|
|
|
|
mean value: 0.46365934504124107
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.7804878 0.90909091 0.73170732 0.9047619 0.82608696
|
|
0.75 0.7027027 0.84444444 0.81818182]
|
|
|
|
mean value: 0.8067463857654736
|
|
|
|
key: train_fscore
|
|
value: [0.80952381 0.79300292 0.82849604 0.832 0.79891304 0.82228117
|
|
0.81940701 0.81643836 0.82352941 0.82939633]
|
|
|
|
mean value: 0.8172988079253738
|
|
|
|
key: test_precision
|
|
value: [0.84210526 0.8 0.86956522 0.75 0.9047619 0.76
|
|
0.75 0.76470588 0.76 0.75 ]
|
|
|
|
mean value: 0.7951138267664045
|
|
|
|
key: train_precision
|
|
value: [0.79274611 0.86075949 0.80927835 0.82105263 0.80327869 0.80729167
|
|
0.82162162 0.83240223 0.81914894 0.81025641]
|
|
|
|
mean value: 0.8177836147631308
|
|
|
|
key: test_recall
|
|
value: [0.76190476 0.76190476 0.95238095 0.71428571 0.9047619 0.9047619
|
|
0.75 0.65 0.95 0.9 ]
|
|
|
|
mean value: 0.825
|
|
|
|
key: train_recall
|
|
value: [0.82702703 0.73513514 0.84864865 0.84324324 0.79459459 0.83783784
|
|
0.8172043 0.80107527 0.82795699 0.84946237]
|
|
|
|
mean value: 0.8182185411217671
|
|
|
|
key: test_accuracy
|
|
value: [0.75 0.71875 0.87096774 0.64516129 0.87096774 0.74193548
|
|
0.67741935 0.64516129 0.77419355 0.74193548]
|
|
|
|
mean value: 0.7436491935483871
|
|
|
|
key: train_accuracy
|
|
value: [0.74285714 0.74642857 0.76868327 0.77580071 0.7366548 0.76156584
|
|
0.76156584 0.76156584 0.76512456 0.76868327]
|
|
|
|
mean value: 0.7588929842399593
|
|
|
|
key: test_roc_auc
|
|
value: [0.74458874 0.6991342 0.82619048 0.60714286 0.85238095 0.65238095
|
|
0.64772727 0.64318182 0.70227273 0.67727273]
|
|
|
|
mean value: 0.7052272727272728
|
|
|
|
key: train_roc_auc
|
|
value: [0.7029872 0.75177809 0.73161599 0.74453829 0.7097973 0.72621059
|
|
0.73491794 0.7426429 0.73503113 0.72999434]
|
|
|
|
mean value: 0.7309513758240413
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.64 0.83333333 0.57692308 0.82608696 0.7037037
|
|
0.6 0.54166667 0.73076923 0.69230769]
|
|
|
|
mean value: 0.681145732689211
|
|
|
|
key: train_jcc
|
|
value: [0.68 0.65700483 0.70720721 0.71232877 0.66515837 0.6981982
|
|
0.69406393 0.68981481 0.7 0.70852018]
|
|
|
|
mean value: 0.6912296295614943
|
|
|
|
key: TN
|
|
value: 62
|
|
|
|
mean value: 62.0
|
|
|
|
key: FP
|
|
value: 36
|
|
|
|
mean value: 36.0
|
|
|
|
key: FN
|
|
value: 44
|
|
|
|
mean value: 44.0
|
|
|
|
key: TP
|
|
value: 170
|
|
|
|
mean value: 170.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.33
|
|
|
|
Accuracy on Blind test: 0.7
|
|
|
|
Running classifier: 4
|
|
Model_name: Naive Bayes
|
|
Model func: BernoulliNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', BernoulliNB())])
|
|
|
|
key: fit_time
|
|
value: [0.00950885 0.00936079 0.01019502 0.00956726 0.01026011 0.00957608
|
|
0.00969887 0.00971508 0.00942612 0.01114821]
|
|
|
|
mean value: 0.009845638275146484
|
|
|
|
key: score_time
|
|
value: [0.00886631 0.00886059 0.00883245 0.00917625 0.00903559 0.00905657
|
|
0.00917363 0.00900912 0.00908494 0.01070547]
|
|
|
|
mean value: 0.009180092811584472
|
|
|
|
key: test_mcc
|
|
value: [ 0.4133805 0.15803489 0.01471225 0.35192842 0.45253757 0.09967105
|
|
-0.07802347 0.14863011 0.22469871 0.14863011]
|
|
|
|
mean value: 0.19342001287335014
|
|
|
|
key: train_mcc
|
|
value: [0.38595876 0.40659146 0.40169892 0.42341022 0.35612129 0.33100274
|
|
0.36505417 0.35193106 0.33525911 0.35908657]
|
|
|
|
mean value: 0.3716114301409626
|
|
|
|
key: test_fscore
|
|
value: [0.83333333 0.76595745 0.69767442 0.83333333 0.84444444 0.8
|
|
0.68181818 0.75555556 0.7826087 0.75555556]
|
|
|
|
mean value: 0.775028096510574
|
|
|
|
key: train_fscore
|
|
value: [0.83018868 0.82758621 0.82640587 0.82793017 0.80604534 0.81339713
|
|
0.82380952 0.81372549 0.81622912 0.81751825]
|
|
|
|
mean value: 0.8202835777038959
|
|
|
|
key: test_precision
|
|
value: [0.74074074 0.69230769 0.68181818 0.74074074 0.79166667 0.68965517
|
|
0.625 0.68 0.69230769 0.68 ]
|
|
|
|
mean value: 0.7014236886995507
|
|
|
|
key: train_precision
|
|
value: [0.73640167 0.760181 0.75446429 0.76851852 0.75471698 0.72961373
|
|
0.73931624 0.74774775 0.73390558 0.74666667]
|
|
|
|
mean value: 0.7471532421515537
|
|
|
|
key: test_recall
|
|
value: [0.95238095 0.85714286 0.71428571 0.95238095 0.9047619 0.95238095
|
|
0.75 0.85 0.9 0.85 ]
|
|
|
|
mean value: 0.8683333333333334
|
|
|
|
key: train_recall
|
|
value: [0.95135135 0.90810811 0.91351351 0.8972973 0.86486486 0.91891892
|
|
0.93010753 0.89247312 0.91935484 0.90322581]
|
|
|
|
mean value: 0.9099215344376634
|
|
|
|
key: test_accuracy
|
|
value: [0.75 0.65625 0.58064516 0.74193548 0.77419355 0.67741935
|
|
0.5483871 0.64516129 0.67741935 0.64516129]
|
|
|
|
mean value: 0.6696572580645161
|
|
|
|
key: train_accuracy
|
|
value: [0.74285714 0.75 0.74733096 0.7544484 0.72597865 0.72241993
|
|
0.7366548 0.72953737 0.72597865 0.73309609]
|
|
|
|
mean value: 0.7368301982714794
|
|
|
|
key: test_roc_auc
|
|
value: [0.65800866 0.56493506 0.50714286 0.62619048 0.70238095 0.52619048
|
|
0.46590909 0.56136364 0.58636364 0.56136364]
|
|
|
|
mean value: 0.5759848484848484
|
|
|
|
key: train_roc_auc
|
|
value: [0.64409673 0.67510669 0.67029842 0.68823198 0.6615991 0.63133446
|
|
0.64400113 0.65149972 0.63336163 0.6516129 ]
|
|
|
|
mean value: 0.6551142759907616
|
|
|
|
key: test_jcc
|
|
value: [0.71428571 0.62068966 0.53571429 0.71428571 0.73076923 0.66666667
|
|
0.51724138 0.60714286 0.64285714 0.60714286]
|
|
|
|
mean value: 0.6356795503347227
|
|
|
|
key: train_jcc
|
|
value: [0.70967742 0.70588235 0.70416667 0.70638298 0.67510549 0.68548387
|
|
0.70040486 0.68595041 0.68951613 0.69135802]
|
|
|
|
mean value: 0.6953928199132248
|
|
|
|
key: TN
|
|
value: 30
|
|
|
|
mean value: 30.0
|
|
|
|
key: FP
|
|
value: 27
|
|
|
|
mean value: 27.0
|
|
|
|
key: FN
|
|
value: 76
|
|
|
|
mean value: 76.0
|
|
|
|
key: TP
|
|
value: 179
|
|
|
|
mean value: 179.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.31
|
|
|
|
Accuracy on Blind test: 0.7
|
|
|
|
Running classifier: 5
|
|
Model_name: K-Nearest Neighbors
|
|
Model func: KNeighborsClassifier()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', KNeighborsClassifier())])
|
|
|
|
key: fit_time
|
|
value: [0.00896025 0.01062131 0.00921178 0.01032758 0.0118413 0.01038861
|
|
0.00920725 0.01012421 0.00997972 0.01029325]
|
|
|
|
mean value: 0.010095524787902831
|
|
|
|
key: score_time
|
|
value: [0.04918337 0.01206517 0.01528883 0.01191854 0.01781154 0.01341414
|
|
0.01296878 0.01386476 0.01706791 0.01706243]
|
|
|
|
mean value: 0.018064546585083007
|
|
|
|
key: test_mcc
|
|
value: [ 0.13261933 0.12434118 0.01471225 0.1667388 0.44786837 0.12245134
|
|
0.11978324 -0.02874798 0.41684569 0.17892962]
|
|
|
|
mean value: 0.16955418600116953
|
|
|
|
key: train_mcc
|
|
value: [0.48779683 0.58110662 0.51362016 0.50738455 0.48727637 0.52197598
|
|
0.46868404 0.52014954 0.50198868 0.52763661]
|
|
|
|
mean value: 0.5117619381388325
|
|
|
|
key: test_fscore
|
|
value: [0.72727273 0.7755102 0.69767442 0.74418605 0.85106383 0.75555556
|
|
0.71428571 0.66666667 0.80952381 0.74418605]
|
|
|
|
mean value: 0.7485925018801247
|
|
|
|
key: train_fscore
|
|
value: [0.84020619 0.86821705 0.85063291 0.84615385 0.84343434 0.85353535
|
|
0.83756345 0.85279188 0.84848485 0.85642317]
|
|
|
|
mean value: 0.8497443046584138
|
|
|
|
key: test_precision
|
|
value: [0.69565217 0.67857143 0.68181818 0.72727273 0.76923077 0.70833333
|
|
0.68181818 0.63636364 0.77272727 0.69565217]
|
|
|
|
mean value: 0.7047439878961617
|
|
|
|
key: train_precision
|
|
value: [0.80295567 0.83168317 0.8 0.80487805 0.79146919 0.80094787
|
|
0.79326923 0.80769231 0.8 0.8056872 ]
|
|
|
|
mean value: 0.8038582685986333
|
|
|
|
key: test_recall
|
|
value: [0.76190476 0.9047619 0.71428571 0.76190476 0.95238095 0.80952381
|
|
0.75 0.7 0.85 0.8 ]
|
|
|
|
mean value: 0.8004761904761905
|
|
|
|
key: train_recall
|
|
value: [0.88108108 0.90810811 0.90810811 0.89189189 0.9027027 0.91351351
|
|
0.88709677 0.90322581 0.90322581 0.91397849]
|
|
|
|
mean value: 0.9012932287125835
|
|
|
|
key: test_accuracy
|
|
value: [0.625 0.65625 0.58064516 0.64516129 0.77419355 0.64516129
|
|
0.61290323 0.5483871 0.74193548 0.64516129]
|
|
|
|
mean value: 0.6474798387096775
|
|
|
|
key: train_accuracy
|
|
value: [0.77857143 0.81785714 0.79003559 0.78647687 0.77935943 0.79359431
|
|
0.77224199 0.79359431 0.78647687 0.79715302]
|
|
|
|
mean value: 0.7895360955770209
|
|
|
|
key: test_roc_auc
|
|
value: [0.56277056 0.54329004 0.50714286 0.58095238 0.67619048 0.5547619
|
|
0.55681818 0.48636364 0.69772727 0.58181818]
|
|
|
|
mean value: 0.5747835497835497
|
|
|
|
key: train_roc_auc
|
|
value: [0.73001422 0.77510669 0.73530405 0.73761261 0.72218468 0.73800676
|
|
0.7172326 0.74108659 0.73056027 0.74119977]
|
|
|
|
mean value: 0.7368308248826076
|
|
|
|
key: test_jcc
|
|
value: [0.57142857 0.63333333 0.53571429 0.59259259 0.74074074 0.60714286
|
|
0.55555556 0.5 0.68 0.59259259]
|
|
|
|
mean value: 0.6009100529100528
|
|
|
|
key: train_jcc
|
|
value: [0.72444444 0.76712329 0.74008811 0.73333333 0.72925764 0.74449339
|
|
0.72052402 0.74336283 0.73684211 0.74889868]
|
|
|
|
mean value: 0.7388367838170675
|
|
|
|
key: TN
|
|
value: 37
|
|
|
|
mean value: 37.0
|
|
|
|
key: FP
|
|
value: 41
|
|
|
|
mean value: 41.0
|
|
|
|
key: FN
|
|
value: 69
|
|
|
|
mean value: 69.0
|
|
|
|
key: TP
|
|
value: 165
|
|
|
|
mean value: 165.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.29
|
|
|
|
Accuracy on Blind test: 0.7
|
|
|
|
Running classifier: 6
|
|
Model_name: SVC
|
|
Model func: SVC(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SVC(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01540208 0.01376987 0.01407671 0.01378703 0.0139339 0.01437092
|
|
0.01446509 0.01371384 0.01400924 0.01391745]
|
|
|
|
mean value: 0.014144611358642579
|
|
|
|
key: score_time
|
|
value: [0.01087761 0.0103426 0.0101378 0.01008773 0.01020432 0.01038265
|
|
0.01006603 0.01003528 0.01035857 0.01014781]
|
|
|
|
mean value: 0.010264039039611816
|
|
|
|
key: test_mcc
|
|
value: [0.57196944 0.49517597 0.55777335 0.47079191 0.53526436 0.26024784
|
|
0.4870862 0.31927949 0.44136741 0.56697057]
|
|
|
|
mean value: 0.4705926550629849
|
|
|
|
key: train_mcc
|
|
value: [0.61185172 0.67361652 0.61915671 0.63587521 0.61345251 0.62176784
|
|
0.60608952 0.6620156 0.60390614 0.58699634]
|
|
|
|
mean value: 0.6234728084389831
|
|
|
|
key: test_fscore
|
|
value: [0.86956522 0.85106383 0.875 0.8372093 0.86956522 0.80851064
|
|
0.8372093 0.8 0.83333333 0.86363636]
|
|
|
|
mean value: 0.8445093204488575
|
|
|
|
key: train_fscore
|
|
value: [0.88235294 0.89876543 0.88279302 0.88721805 0.8817734 0.88395062
|
|
0.88135593 0.8960396 0.88077859 0.87651332]
|
|
|
|
mean value: 0.8851540894305963
|
|
|
|
key: test_precision
|
|
value: [0.8 0.76923077 0.77777778 0.81818182 0.8 0.73076923
|
|
0.7826087 0.72 0.71428571 0.79166667]
|
|
|
|
mean value: 0.7704520672564151
|
|
|
|
key: train_precision
|
|
value: [0.80717489 0.82727273 0.81944444 0.8271028 0.80995475 0.81363636
|
|
0.80176211 0.83027523 0.80444444 0.79735683]
|
|
|
|
mean value: 0.8138424594648971
|
|
|
|
key: test_recall
|
|
value: [0.95238095 0.95238095 1. 0.85714286 0.95238095 0.9047619
|
|
0.9 0.9 1. 0.95 ]
|
|
|
|
mean value: 0.9369047619047619
|
|
|
|
key: train_recall
|
|
value: [0.97297297 0.98378378 0.95675676 0.95675676 0.96756757 0.96756757
|
|
0.97849462 0.97311828 0.97311828 0.97311828]
|
|
|
|
mean value: 0.9703254867770996
|
|
|
|
key: test_accuracy
|
|
value: [0.8125 0.78125 0.80645161 0.77419355 0.80645161 0.70967742
|
|
0.77419355 0.70967742 0.74193548 0.80645161]
|
|
|
|
mean value: 0.7722782258064516
|
|
|
|
key: train_accuracy
|
|
value: [0.82857143 0.85357143 0.83274021 0.83985765 0.82918149 0.83274021
|
|
0.82562278 0.85053381 0.82562278 0.81850534]
|
|
|
|
mean value: 0.8336947127605491
|
|
|
|
key: test_roc_auc
|
|
value: [0.74891775 0.7034632 0.7 0.72857143 0.72619048 0.60238095
|
|
0.72272727 0.63181818 0.63636364 0.74772727]
|
|
|
|
mean value: 0.6948160173160174
|
|
|
|
key: train_roc_auc
|
|
value: [0.7601707 0.79189189 0.77525338 0.78567005 0.76503378 0.77024212
|
|
0.75240521 0.7918223 0.75498019 0.74445388]
|
|
|
|
mean value: 0.7691923486517078
|
|
|
|
key: test_jcc
|
|
value: [0.76923077 0.74074074 0.77777778 0.72 0.76923077 0.67857143
|
|
0.72 0.66666667 0.71428571 0.76 ]
|
|
|
|
mean value: 0.7316503866503867
|
|
|
|
key: train_jcc
|
|
value: [0.78947368 0.8161435 0.79017857 0.7972973 0.78854626 0.7920354
|
|
0.78787879 0.81165919 0.78695652 0.78017241]
|
|
|
|
mean value: 0.7940341620667072
|
|
|
|
key: TN
|
|
value: 48
|
|
|
|
mean value: 48.0
|
|
|
|
key: FP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: FN
|
|
value: 58
|
|
|
|
mean value: 58.0
|
|
|
|
key: TP
|
|
value: 193
|
|
|
|
mean value: 193.0
|
|
|
|
key: trainingY_neg
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.55
|
|
|
|
Accuracy on Blind test: 0.81
|
|
|
|
Running classifier: 7
|
|
Model_name: MLP
|
|
Model func: MLPClassifier(max_iter=500, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MLPClassifier(max_iter=500, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [1.16458178 1.28447604 1.16362858 1.27272463 1.20178652 1.13651323
|
|
1.22490501 1.13492298 1.25703454 1.13588333]
|
|
|
|
mean value: 1.197645664215088
|
|
|
|
key: score_time
|
|
value: [0.01479721 0.01402593 0.01684952 0.01273608 0.01289916 0.01262927
|
|
0.01277757 0.01286125 0.01281571 0.01280928]
|
|
|
|
mean value: 0.013520097732543946
|
|
|
|
key: test_mcc
|
|
value: [0.79772404 0.6457766 0.85238095 0.53924646 1. 0.61758068
|
|
0.71818182 0.65635466 0.93048421 0.79524277]
|
|
|
|
mean value: 0.7552972189336914
|
|
|
|
key: train_mcc
|
|
value: [0.99204533 0.99204533 1. 0.99210029 1. 0.99210029
|
|
1. 1. 1. 0.99205967]
|
|
|
|
mean value: 0.9960350909981239
|
|
|
|
key: test_fscore
|
|
value: [0.93333333 0.88888889 0.95238095 0.86363636 1. 0.88888889
|
|
0.9 0.87179487 0.97560976 0.93023256]
|
|
|
|
mean value: 0.9204765613160395
|
|
|
|
key: train_fscore
|
|
value: [0.99730458 0.99730458 1. 0.99730458 1. 0.99730458
|
|
1. 1. 1. 0.99731903]
|
|
|
|
mean value: 0.9986537363693516
|
|
|
|
key: test_precision
|
|
value: [0.875 0.83333333 0.95238095 0.82608696 1. 0.83333333
|
|
0.9 0.89473684 0.95238095 0.86956522]
|
|
|
|
mean value: 0.8936817587446878
|
|
|
|
key: train_precision
|
|
value: [0.99462366 0.99462366 1. 0.99462366 1. 0.99462366
|
|
1. 1. 1. 0.99465241]
|
|
|
|
mean value: 0.9973147030073026
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 0.95238095 0.9047619 1. 0.95238095
|
|
0.9 0.85 1. 1. ]
|
|
|
|
mean value: 0.9511904761904763
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.90625 0.84375 0.93548387 0.80645161 1. 0.83870968
|
|
0.87096774 0.83870968 0.96774194 0.90322581]
|
|
|
|
mean value: 0.8911290322580644
|
|
|
|
key: train_accuracy
|
|
value: [0.99642857 0.99642857 1. 0.99644128 1. 0.99644128
|
|
1. 1. 1. 0.99644128]
|
|
|
|
mean value: 0.9982180986273512
|
|
|
|
key: test_roc_auc
|
|
value: [0.86363636 0.79437229 0.92619048 0.75238095 1. 0.77619048
|
|
0.85909091 0.83409091 0.95454545 0.86363636]
|
|
|
|
mean value: 0.8624134199134199
|
|
|
|
key: train_roc_auc
|
|
value: [0.99473684 0.99473684 1. 0.99479167 1. 0.99479167
|
|
1. 1. 1. 0.99473684]
|
|
|
|
mean value: 0.9973793859649123
|
|
|
|
key: test_jcc
|
|
value: [0.875 0.8 0.90909091 0.76 1. 0.8
|
|
0.81818182 0.77272727 0.95238095 0.86956522]
|
|
|
|
mean value: 0.8556946169772257
|
|
|
|
key: train_jcc
|
|
value: [0.99462366 0.99462366 1. 0.99462366 1. 0.99462366
|
|
1. 1. 1. 0.99465241]
|
|
|
|
mean value: 0.9973147030073026
|
|
|
|
key: TN
|
|
value: 82
|
|
|
|
mean value: 82.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 24
|
|
|
|
mean value: 24.0
|
|
|
|
key: TP
|
|
value: 196
|
|
|
|
mean value: 196.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.77
|
|
|
|
Accuracy on Blind test: 0.9
|
|
|
|
Running classifier: 8
|
|
Model_name: Decision Tree
|
|
Model func: DecisionTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', DecisionTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02068901 0.02152038 0.01389027 0.01603794 0.01353812 0.01289558
|
|
0.015836 0.01678038 0.01749015 0.01312494]
|
|
|
|
mean value: 0.01618027687072754
|
|
|
|
key: score_time
|
|
value: [0.01225066 0.00925589 0.00909591 0.00866771 0.00862551 0.00864029
|
|
0.00862956 0.00868511 0.00961447 0.00894046]
|
|
|
|
mean value: 0.009240555763244628
|
|
|
|
key: test_mcc
|
|
value: [0.87496729 0.93435318 0.93048421 0.78625916 1. 0.86831345
|
|
0.93048421 0.79476958 0.85909091 0.72821908]
|
|
|
|
mean value: 0.8706941085318691
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.95 0.97560976 0.97560976 0.92682927 1. 0.95
|
|
0.97560976 0.92307692 0.95 0.90909091]
|
|
|
|
mean value: 0.9535826368753197
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 0.95 1. 1.
|
|
0.95238095 0.94736842 0.95 0.83333333]
|
|
|
|
mean value: 0.9633082706766917
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.9047619 0.95238095 0.95238095 0.9047619 1. 0.9047619
|
|
1. 0.9 0.95 1. ]
|
|
|
|
mean value: 0.9469047619047618
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.9375 0.96875 0.96774194 0.90322581 1. 0.93548387
|
|
0.96774194 0.90322581 0.93548387 0.87096774]
|
|
|
|
mean value: 0.9390120967741936
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.95238095 0.97619048 0.97619048 0.90238095 1. 0.95238095
|
|
0.95454545 0.90454545 0.92954545 0.81818182]
|
|
|
|
mean value: 0.936634199134199
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.9047619 0.95238095 0.95238095 0.86363636 1. 0.9047619
|
|
0.95238095 0.85714286 0.9047619 0.83333333]
|
|
|
|
mean value: 0.9125541125541126
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 98
|
|
|
|
mean value: 98.0
|
|
|
|
key: FP
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 195
|
|
|
|
mean value: 195.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.87
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
Running classifier: 9
|
|
Model_name: Extra Trees
|
|
Model func: ExtraTreesClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreesClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.10506797 0.1029923 0.10362291 0.10310459 0.10460377 0.10338831
|
|
0.10361099 0.10267019 0.105057 0.10298061]
|
|
|
|
mean value: 0.10370986461639405
|
|
|
|
key: score_time
|
|
value: [0.01752758 0.01787734 0.01751494 0.01744723 0.01748729 0.01749158
|
|
0.01751399 0.01741982 0.01745582 0.01747465]
|
|
|
|
mean value: 0.017521023750305176
|
|
|
|
key: test_mcc
|
|
value: [0.78959188 0.57163505 0.61758068 0.36059915 0.47079191 0.36059915
|
|
0.56537691 0.49780905 0.5913124 0.57727273]
|
|
|
|
mean value: 0.5402568904601063
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.93023256 0.86363636 0.88888889 0.82608696 0.8372093 0.82608696
|
|
0.85714286 0.82926829 0.86956522 0.85 ]
|
|
|
|
mean value: 0.8578117393250937
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.90909091 0.82608696 0.83333333 0.76 0.81818182 0.76
|
|
0.81818182 0.80952381 0.76923077 0.85 ]
|
|
|
|
mean value: 0.8153629414064196
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.95238095 0.9047619 0.95238095 0.9047619 0.85714286 0.9047619
|
|
0.9 0.85 1. 0.85 ]
|
|
|
|
mean value: 0.9076190476190475
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.90625 0.8125 0.83870968 0.74193548 0.77419355 0.74193548
|
|
0.80645161 0.77419355 0.80645161 0.80645161]
|
|
|
|
mean value: 0.8009072580645162
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.88528139 0.77056277 0.77619048 0.65238095 0.72857143 0.65238095
|
|
0.76818182 0.74318182 0.72727273 0.78863636]
|
|
|
|
mean value: 0.7492640692640691
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.86956522 0.76 0.8 0.7037037 0.72 0.7037037
|
|
0.75 0.70833333 0.76923077 0.73913043]
|
|
|
|
mean value: 0.7523667162145423
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 63
|
|
|
|
mean value: 63.0
|
|
|
|
key: FP
|
|
value: 19
|
|
|
|
mean value: 19.0
|
|
|
|
key: FN
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: TP
|
|
value: 187
|
|
|
|
mean value: 187.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.53
|
|
|
|
Accuracy on Blind test: 0.79
|
|
|
|
Running classifier: 10
|
|
Model_name: Extra Tree
|
|
Model func: ExtraTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00934625 0.00909376 0.00920677 0.00956249 0.00934076 0.00916028
|
|
0.00926018 0.00925374 0.00932503 0.00914741]
|
|
|
|
mean value: 0.00926966667175293
|
|
|
|
key: score_time
|
|
value: [0.00877452 0.00852442 0.00861669 0.00886846 0.0085597 0.00864863
|
|
0.00862837 0.00879931 0.00861359 0.00859666]
|
|
|
|
mean value: 0.008663034439086914
|
|
|
|
key: test_mcc
|
|
value: [ 0.52663543 0.50569367 -0.20763488 -0.09157015 0.62281846 0.28749445
|
|
0.51793973 0.3261463 0.24110987 0.15454545]
|
|
|
|
mean value: 0.2883178338157986
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.82926829 0.8372093 0.66666667 0.68181818 0.88372093 0.8
|
|
0.82051282 0.73684211 0.71794872 0.7 ]
|
|
|
|
mean value: 0.7673987017450612
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.85 0.81818182 0.625 0.65217391 0.86363636 0.75
|
|
0.84210526 0.77777778 0.73684211 0.7 ]
|
|
|
|
mean value: 0.761571724106049
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.80952381 0.85714286 0.71428571 0.71428571 0.9047619 0.85714286
|
|
0.8 0.7 0.7 0.7 ]
|
|
|
|
mean value: 0.7757142857142857
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.78125 0.78125 0.51612903 0.5483871 0.83870968 0.70967742
|
|
0.77419355 0.67741935 0.64516129 0.61290323]
|
|
|
|
mean value: 0.6885080645161291
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.76839827 0.74675325 0.40714286 0.45714286 0.80238095 0.62857143
|
|
0.76363636 0.66818182 0.62272727 0.57727273]
|
|
|
|
mean value: 0.6442207792207792
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.70833333 0.72 0.5 0.51724138 0.79166667 0.66666667
|
|
0.69565217 0.58333333 0.56 0.53846154]
|
|
|
|
mean value: 0.6281355091684926
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 55
|
|
|
|
mean value: 55.0
|
|
|
|
key: FP
|
|
value: 46
|
|
|
|
mean value: 46.0
|
|
|
|
key: FN
|
|
value: 51
|
|
|
|
mean value: 51.0
|
|
|
|
key: TP
|
|
value: 160
|
|
|
|
mean value: 160.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.29
|
|
|
|
Accuracy on Blind test: 0.68
|
|
|
|
Running classifier: 11
|
|
Model_name: Random Forest
|
|
Model func: RandomForestClassifier(n_estimators=1000, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(n_estimators=1000, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
[1.42102885 1.41763186 1.43122268 1.39822125 1.40961742 1.39854026
|
|
1.42449594 1.49090695 1.44167662 1.44908071]
|
|
|
|
mean value: 1.4282422542572022
|
|
|
|
key: score_time
|
|
value: [0.094944 0.09802151 0.15033007 0.09156108 0.09057498 0.09051704
|
|
0.09883428 0.09939861 0.09202886 0.09227395]
|
|
|
|
mean value: 0.09984843730926514
|
|
|
|
key: test_mcc
|
|
value: [0.93154098 0.71797362 0.92687157 0.69695062 0.85238095 0.77484502
|
|
0.78625916 0.71390814 0.86243936 0.66057826]
|
|
|
|
mean value: 0.7923747679070615
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.97674419 0.90909091 0.97674419 0.90909091 0.95238095 0.93023256
|
|
0.92682927 0.9047619 0.95238095 0.88888889]
|
|
|
|
mean value: 0.9327144715119757
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.95454545 0.86956522 0.95454545 0.86956522 0.95238095 0.90909091
|
|
0.9047619 0.86363636 0.90909091 0.8 ]
|
|
|
|
mean value: 0.8987182382834555
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 1. 0.95238095 0.95238095 0.95238095
|
|
0.95 0.95 1. 1. ]
|
|
|
|
mean value: 0.9709523809523809
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.96875 0.875 0.96774194 0.87096774 0.93548387 0.90322581
|
|
0.90322581 0.87096774 0.93548387 0.83870968]
|
|
|
|
mean value: 0.9069556451612902
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.95454545 0.83982684 0.95 0.82619048 0.92619048 0.87619048
|
|
0.88409091 0.83863636 0.90909091 0.77272727]
|
|
|
|
mean value: 0.8777489177489176
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.95454545 0.83333333 0.95454545 0.83333333 0.90909091 0.86956522
|
|
0.86363636 0.82608696 0.90909091 0.8 ]
|
|
|
|
mean value: 0.8753227931488802
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 83
|
|
|
|
mean value: 83.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 23
|
|
|
|
mean value: 23.0
|
|
|
|
key: TP
|
|
value: 200
|
|
|
|
mean value: 200.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.84
|
|
|
|
Accuracy on Blind test: 0.93
|
|
|
|
Running classifier: 12
|
|
Model_name: Random Forest2
|
|
Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...age_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10,
|
|
oob_score=True, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.89417028 0.92537498 0.97537518 0.91410828 0.87176323 0.92093062
|
|
0.91085744 0.92615676 0.87906098 0.91220188]
|
|
|
|
mean value: 0.912999963760376
|
|
|
|
key: score_time
|
|
value: [0.21468759 0.18967795 0.22059464 0.181077 0.24509573 0.19168997
|
|
0.2172718 0.18608809 0.19562244 0.18895364]
|
|
|
|
mean value: 0.20307588577270508
|
|
|
|
key: test_mcc
|
|
value: [0.8643122 0.6457766 0.78262379 0.61758068 0.77484502 0.61758068
|
|
0.78625916 0.71390814 0.66057826 0.66057826]
|
|
|
|
mean value: 0.7124042779002867
|
|
|
|
key: train_mcc
|
|
value: [0.95258202 0.94474539 0.94513672 0.95291644 0.93737406 0.94513672
|
|
0.95266247 0.96836384 0.95266247 0.95266247]
|
|
|
|
mean value: 0.9504242616765979
|
|
|
|
key: test_fscore
|
|
value: [0.95454545 0.88888889 0.93333333 0.88888889 0.93023256 0.88888889
|
|
0.92682927 0.9047619 0.88888889 0.88888889]
|
|
|
|
mean value: 0.9094146963517356
|
|
|
|
key: train_fscore
|
|
value: [0.98404255 0.98143236 0.98143236 0.98404255 0.97883598 0.98143236
|
|
0.98412698 0.9893617 0.98412698 0.98412698]
|
|
|
|
mean value: 0.9832960821955685
|
|
|
|
key: test_precision
|
|
value: [0.91304348 0.83333333 0.875 0.83333333 0.90909091 0.83333333
|
|
0.9047619 0.86363636 0.8 0.8 ]
|
|
|
|
mean value: 0.8565532655750048
|
|
|
|
key: train_precision
|
|
value: [0.96858639 0.96354167 0.96354167 0.96858639 0.95854922 0.96354167
|
|
0.96875 0.97894737 0.96875 0.96875 ]
|
|
|
|
mean value: 0.9671544366088091
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 1. 0.95238095 0.95238095 0.95238095
|
|
0.95 0.95 1. 1. ]
|
|
|
|
mean value: 0.9709523809523809
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.9375 0.84375 0.90322581 0.83870968 0.90322581 0.83870968
|
|
0.90322581 0.87096774 0.83870968 0.83870968]
|
|
|
|
mean value: 0.8716733870967742
|
|
|
|
key: train_accuracy
|
|
value: [0.97857143 0.975 0.97508897 0.97864769 0.97153025 0.97508897
|
|
0.97864769 0.98576512 0.97864769 0.97864769]
|
|
|
|
mean value: 0.9775635485510931
|
|
|
|
key: test_roc_auc
|
|
value: [0.90909091 0.79437229 0.85 0.77619048 0.87619048 0.77619048
|
|
0.88409091 0.83863636 0.77272727 0.77272727]
|
|
|
|
mean value: 0.8250216450216449
|
|
|
|
key: train_roc_auc
|
|
value: [0.96842105 0.96315789 0.96354167 0.96875 0.95833333 0.96354167
|
|
0.96842105 0.97894737 0.96842105 0.96842105]
|
|
|
|
mean value: 0.9669956140350877
|
|
|
|
key: test_jcc
|
|
value: [0.91304348 0.8 0.875 0.8 0.86956522 0.8
|
|
0.86363636 0.82608696 0.8 0.8 ]
|
|
|
|
mean value: 0.8347332015810277
|
|
|
|
key: train_jcc
|
|
value: [0.96858639 0.96354167 0.96354167 0.96858639 0.95854922 0.96354167
|
|
0.96875 0.97894737 0.96875 0.96875 ]
|
|
|
|
mean value: 0.9671544366088091
|
|
|
|
key: TN
|
|
value: 72
|
|
|
|
mean value: 72.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 34
|
|
|
|
mean value: 34.0
|
|
|
|
key: TP
|
|
value: 200
|
|
|
|
mean value: 200.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.77
|
|
|
|
Accuracy on Blind test: 0.9
|
|
|
|
Running classifier: 13
|
|
Model_name: XGBoost
|
|
Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0)
|
|
|
|
Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
|
|
from pandas import MultiIndex, Int64Index
|
|
Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...
|
|
interaction_constraints=None, learning_rate=None,
|
|
max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan,
|
|
monotone_constraints=None, n_estimators=100,
|
|
n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None,
|
|
reg_lambda=None, scale_pos_weight=None,
|
|
subsample=None, tree_method=None,
|
|
use_label_encoder=False,
|
|
validate_parameters=None, verbosity=0))])
|
|
|
|
key: fit_time
|
|
value: [0.13504362 0.05386043 0.17046118 0.04772305 0.05267906 0.05315638
|
|
0.05455399 0.05298352 0.05491114 0.05289721]
|
|
|
|
mean value: 0.07282695770263672
|
|
|
|
key: score_time
|
|
value: [0.01084018 0.01215386 0.01093411 0.01053691 0.01060319 0.0104351
|
|
0.01052928 0.01054263 0.0104084 0.01039553]
|
|
|
|
mean value: 0.010737919807434082
|
|
|
|
key: test_mcc
|
|
value: [1. 1. 1. 0.85238095 0.93048421 0.93048421
|
|
0.79476958 0.93048421 0.85909091 0.72821908]
|
|
|
|
mean value: 0.902591315825731
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 1. 1. 0.95238095 0.97560976 0.97560976
|
|
0.92307692 0.97560976 0.95 0.90909091]
|
|
|
|
mean value: 0.9661378052841467
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 0.95238095 1. 1.
|
|
0.94736842 0.95238095 0.95 0.83333333]
|
|
|
|
mean value: 0.963546365914787
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 0.95238095 0.95238095 0.95238095
|
|
0.9 1. 0.95 1. ]
|
|
|
|
mean value: 0.9707142857142858
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 1. 1. 0.93548387 0.96774194 0.96774194
|
|
0.90322581 0.96774194 0.93548387 0.87096774]
|
|
|
|
mean value: 0.9548387096774194
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 1. 1. 0.92619048 0.97619048 0.97619048
|
|
0.90454545 0.95454545 0.92954545 0.81818182]
|
|
|
|
mean value: 0.9485389610389611
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 1. 1. 0.90909091 0.95238095 0.95238095
|
|
0.85714286 0.95238095 0.9047619 0.83333333]
|
|
|
|
mean value: 0.9361471861471863
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 98
|
|
|
|
mean value: 98.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 200
|
|
|
|
mean value: 200.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.9
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 14
|
|
Model_name: LDA
|
|
Model func: LinearDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LinearDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.03230834 0.04915643 0.02894878 0.03100872 0.06014895 0.04913616
|
|
0.02832913 0.02888393 0.03995156 0.07339001]
|
|
|
|
mean value: 0.04212620258331299
|
|
|
|
key: score_time
|
|
value: [0.02405763 0.01220226 0.01222754 0.01217437 0.02302003 0.01225495
|
|
0.01228237 0.01220059 0.02282333 0.01247096]
|
|
|
|
mean value: 0.015571403503417968
|
|
|
|
key: test_mcc
|
|
value: [0.8643122 0.74100101 0.64203411 0.55714286 0.78625916 0.55714286
|
|
0.68174942 0.599404 0.79524277 0.56697057]
|
|
|
|
mean value: 0.6791258953421403
|
|
|
|
key: train_mcc
|
|
value: [0.96809668 0.96809668 0.96831892 0.97623798 0.96057359 0.97636634
|
|
0.96815373 0.96819468 0.97611544 0.97611544]
|
|
|
|
mean value: 0.9706269497086195
|
|
|
|
key: test_fscore
|
|
value: [0.95454545 0.9 0.87804878 0.85714286 0.92682927 0.85714286
|
|
0.86486486 0.84210526 0.93023256 0.86363636]
|
|
|
|
mean value: 0.8874548267410315
|
|
|
|
key: train_fscore
|
|
value: [0.98924731 0.98924731 0.98924731 0.99191375 0.98644986 0.9919571
|
|
0.98930481 0.98924731 0.9919571 0.9919571 ]
|
|
|
|
mean value: 0.9900528984948347
|
|
|
|
key: test_precision
|
|
value: [0.91304348 0.94736842 0.9 0.85714286 0.95 0.85714286
|
|
0.94117647 0.88888889 0.86956522 0.79166667]
|
|
|
|
mean value: 0.891599485713431
|
|
|
|
key: train_precision
|
|
value: [0.98395722 0.98395722 0.98395722 0.98924731 0.98913043 0.98404255
|
|
0.98404255 0.98924731 0.98930481 0.98930481]
|
|
|
|
mean value: 0.9866191448243959
|
|
|
|
key: test_recall
|
|
value: [1. 0.85714286 0.85714286 0.85714286 0.9047619 0.85714286
|
|
0.8 0.8 1. 0.95 ]
|
|
|
|
mean value: 0.8883333333333333
|
|
|
|
key: train_recall
|
|
value: [0.99459459 0.99459459 0.99459459 0.99459459 0.98378378 1.
|
|
0.99462366 0.98924731 0.99462366 0.99462366]
|
|
|
|
mean value: 0.9935280441732054
|
|
|
|
key: test_accuracy
|
|
value: [0.9375 0.875 0.83870968 0.80645161 0.90322581 0.80645161
|
|
0.83870968 0.80645161 0.90322581 0.80645161]
|
|
|
|
mean value: 0.8522177419354838
|
|
|
|
key: train_accuracy
|
|
value: [0.98571429 0.98571429 0.98576512 0.98932384 0.98220641 0.98932384
|
|
0.98576512 0.98576512 0.98932384 0.98932384]
|
|
|
|
mean value: 0.9868225724453483
|
|
|
|
key: test_roc_auc
|
|
value: [0.90909091 0.88311688 0.82857143 0.77857143 0.90238095 0.77857143
|
|
0.85454545 0.80909091 0.86363636 0.74772727]
|
|
|
|
mean value: 0.8355303030303031
|
|
|
|
key: train_roc_auc
|
|
value: [0.98150782 0.98150782 0.9816723 0.98688063 0.98147523 0.984375
|
|
0.98152235 0.98409734 0.98678551 0.98678551]
|
|
|
|
mean value: 0.983660951911164
|
|
|
|
key: test_jcc
|
|
value: [0.91304348 0.81818182 0.7826087 0.75 0.86363636 0.75
|
|
0.76190476 0.72727273 0.86956522 0.76 ]
|
|
|
|
mean value: 0.7996213062300018
|
|
|
|
key: train_jcc
|
|
value: [0.9787234 0.9787234 0.9787234 0.98395722 0.97326203 0.98404255
|
|
0.97883598 0.9787234 0.98404255 0.98404255]
|
|
|
|
mean value: 0.9803076506768622
|
|
|
|
key: TN
|
|
value: 83
|
|
|
|
mean value: 83.0
|
|
|
|
key: FP
|
|
value: 23
|
|
|
|
mean value: 23.0
|
|
|
|
key: FN
|
|
value: 23
|
|
|
|
mean value: 23.0
|
|
|
|
key: TP
|
|
value: 183
|
|
|
|
mean value: 183.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.81
|
|
|
|
Accuracy on Blind test: 0.92
|
|
|
|
Running classifier: 15
|
|
Model_name: Multinomial
|
|
Model func: MultinomialNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MultinomialNB())])
|
|
|
|
key: fit_time
|
|
value: [0.0126524 0.0095458 0.00931358 0.0091176 0.00899625 0.00920296
|
|
0.00903535 0.00899458 0.00897455 0.00904608]
|
|
|
|
mean value: 0.0094879150390625
|
|
|
|
key: score_time
|
|
value: [0.02371144 0.00912809 0.00880551 0.00858068 0.00853944 0.00854635
|
|
0.00848079 0.00854325 0.00861645 0.00864959]
|
|
|
|
mean value: 0.010160160064697266
|
|
|
|
key: test_mcc
|
|
value: [0.33910216 0.13261933 0.53526436 0.06513389 0.49780905 0.35192842
|
|
0.33300791 0.01363636 0.40800555 0.33300791]
|
|
|
|
mean value: 0.30095149600575316
|
|
|
|
key: train_mcc
|
|
value: [0.31603146 0.3455391 0.3767181 0.39130863 0.34815112 0.3313273
|
|
0.29256975 0.35900377 0.37052503 0.33144271]
|
|
|
|
mean value: 0.3462616963153834
|
|
|
|
key: test_fscore
|
|
value: [0.75 0.72727273 0.86956522 0.68292683 0.82926829 0.83333333
|
|
0.79069767 0.65 0.82608696 0.79069767]
|
|
|
|
mean value: 0.7749848705307533
|
|
|
|
key: train_fscore
|
|
value: [0.78974359 0.79586563 0.81683168 0.80927835 0.79691517 0.79177378
|
|
0.77720207 0.79792746 0.80512821 0.79695431]
|
|
|
|
mean value: 0.7977620256045503
|
|
|
|
key: test_precision
|
|
value: [0.78947368 0.69565217 0.8 0.7 0.85 0.74074074
|
|
0.73913043 0.65 0.73076923 0.73913043]
|
|
|
|
mean value: 0.7434896699198758
|
|
|
|
key: train_precision
|
|
value: [0.75121951 0.76237624 0.75342466 0.77339901 0.75980392 0.75490196
|
|
0.75 0.77 0.76960784 0.75480769]
|
|
|
|
mean value: 0.7599540839929344
|
|
|
|
key: test_recall
|
|
value: [0.71428571 0.76190476 0.95238095 0.66666667 0.80952381 0.95238095
|
|
0.85 0.65 0.95 0.85 ]
|
|
|
|
mean value: 0.8157142857142858
|
|
|
|
key: train_recall
|
|
value: [0.83243243 0.83243243 0.89189189 0.84864865 0.83783784 0.83243243
|
|
0.80645161 0.82795699 0.84408602 0.84408602]
|
|
|
|
mean value: 0.8398256320836965
|
|
|
|
key: test_accuracy
|
|
value: [0.6875 0.625 0.80645161 0.58064516 0.77419355 0.74193548
|
|
0.70967742 0.5483871 0.74193548 0.70967742]
|
|
|
|
mean value: 0.6925403225806452
|
|
|
|
key: train_accuracy
|
|
value: [0.70714286 0.71785714 0.7366548 0.7366548 0.71886121 0.71174377
|
|
0.69395018 0.72241993 0.72953737 0.71530249]
|
|
|
|
mean value: 0.7190124555160142
|
|
|
|
key: test_roc_auc
|
|
value: [0.67532468 0.56277056 0.72619048 0.53333333 0.7547619 0.62619048
|
|
0.65227273 0.50681818 0.65681818 0.65227273]
|
|
|
|
mean value: 0.6346753246753247
|
|
|
|
key: train_roc_auc
|
|
value: [0.64779516 0.66358464 0.66469595 0.68474099 0.66371059 0.65579955
|
|
0.64006791 0.67187323 0.67467459 0.65362196]
|
|
|
|
mean value: 0.6620564563927254
|
|
|
|
key: test_jcc
|
|
value: [0.6 0.57142857 0.76923077 0.51851852 0.70833333 0.71428571
|
|
0.65384615 0.48148148 0.7037037 0.65384615]
|
|
|
|
mean value: 0.63746743996744
|
|
|
|
key: train_jcc
|
|
value: [0.65254237 0.66094421 0.69037657 0.67965368 0.66239316 0.65531915
|
|
0.63559322 0.6637931 0.67381974 0.66244726]
|
|
|
|
mean value: 0.6636882462571104
|
|
|
|
key: TN
|
|
value: 48
|
|
|
|
mean value: 48.0
|
|
|
|
key: FP
|
|
value: 38
|
|
|
|
mean value: 38.0
|
|
|
|
key: FN
|
|
value: 58
|
|
|
|
mean value: 58.0
|
|
|
|
key: TP
|
|
value: 168
|
|
|
|
mean value: 168.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.33
|
|
|
|
Accuracy on Blind test: 0.71
|
|
|
|
Running classifier: 16
|
|
Model_name: Passive Aggresive
|
|
Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
PassiveAggressiveClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01832533 0.01673365 0.02225184 0.02035999 0.02189851 0.02132702
|
|
0.01896715 0.02250695 0.01972532 0.02366352]
|
|
|
|
mean value: 0.020575928688049316
|
|
|
|
key: score_time
|
|
value: [0.01003265 0.01122832 0.01144624 0.01167583 0.0117116 0.01166582
|
|
0.01175475 0.01174879 0.01169896 0.01169562]
|
|
|
|
mean value: 0.011465859413146973
|
|
|
|
key: test_mcc
|
|
value: [1. 0.79772404 0.93048421 0.77484502 1. 0.85238095
|
|
0.6310315 0.79524277 0.93048421 0.85909091]
|
|
|
|
mean value: 0.8571283609822942
|
|
|
|
key: train_mcc
|
|
value: [0.96009907 0.93692544 0.98422269 0.96835586 0.97635661 0.97623798
|
|
0.88905141 0.97624243 0.96819468 0.96847885]
|
|
|
|
mean value: 0.9604165025930188
|
|
|
|
key: test_fscore
|
|
value: [1. 0.93333333 0.97560976 0.93023256 1. 0.95238095
|
|
0.83333333 0.93023256 0.97560976 0.95 ]
|
|
|
|
mean value: 0.948073224752181
|
|
|
|
key: train_fscore
|
|
value: [0.98652291 0.97883598 0.99462366 0.98918919 0.99186992 0.99191375
|
|
0.95821727 0.992 0.98924731 0.98918919]
|
|
|
|
mean value: 0.9861609171532406
|
|
|
|
key: test_precision
|
|
value: [1. 0.875 1. 0.90909091 1. 0.95238095
|
|
0.9375 0.86956522 0.95238095 0.95 ]
|
|
|
|
mean value: 0.9445918031244117
|
|
|
|
key: train_precision
|
|
value: [0.98387097 0.95854922 0.98930481 0.98918919 0.99456522 0.98924731
|
|
0.99421965 0.98412698 0.98924731 0.99456522]
|
|
|
|
mean value: 0.9866885888307972
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.95238095 0.95238095 1. 0.95238095
|
|
0.75 1. 1. 0.95 ]
|
|
|
|
mean value: 0.9557142857142857
|
|
|
|
key: train_recall
|
|
value: [0.98918919 1. 1. 0.98918919 0.98918919 0.99459459
|
|
0.92473118 1. 0.98924731 0.98387097]
|
|
|
|
mean value: 0.9860011624527754
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.90625 0.96774194 0.90322581 1. 0.93548387
|
|
0.80645161 0.90322581 0.96774194 0.93548387]
|
|
|
|
mean value: 0.9325604838709678
|
|
|
|
key: train_accuracy
|
|
value: [0.98214286 0.97142857 0.99288256 0.98576512 0.98932384 0.98932384
|
|
0.94661922 0.98932384 0.98576512 0.98576512]
|
|
|
|
mean value: 0.9818340111845449
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.86363636 0.97619048 0.87619048 1. 0.92619048
|
|
0.82954545 0.86363636 0.95454545 0.92954545]
|
|
|
|
mean value: 0.921948051948052
|
|
|
|
key: train_roc_auc
|
|
value: [0.97880512 0.95789474 0.98958333 0.98417793 0.98938626 0.98688063
|
|
0.95710243 0.98421053 0.98409734 0.98667233]
|
|
|
|
mean value: 0.979881063682528
|
|
|
|
key: test_jcc
|
|
value: [1. 0.875 0.95238095 0.86956522 1. 0.90909091
|
|
0.71428571 0.86956522 0.95238095 0.9047619 ]
|
|
|
|
mean value: 0.9047030867683041
|
|
|
|
key: train_jcc
|
|
value: [0.97340426 0.95854922 0.98930481 0.97860963 0.98387097 0.98395722
|
|
0.9197861 0.98412698 0.9787234 0.97860963]
|
|
|
|
mean value: 0.972894221392046
|
|
|
|
key: TN
|
|
value: 94
|
|
|
|
mean value: 94.0
|
|
|
|
key: FP
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: FN
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: TP
|
|
value: 197
|
|
|
|
mean value: 197.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.64
|
|
|
|
Accuracy on Blind test: 0.8
|
|
|
|
Running classifier: 17
|
|
Model_name: Stochastic GDescent
|
|
Model func: SGDClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SGDClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01601458 0.01648951 0.01605535 0.01551676 0.01533937 0.01700306
|
|
0.01540875 0.01559615 0.01406908 0.01540995]
|
|
|
|
mean value: 0.015690255165100097
|
|
|
|
key: score_time
|
|
value: [0.01169801 0.01167488 0.01167607 0.01165557 0.011724 0.01172376
|
|
0.01167727 0.01172972 0.01162696 0.01171517]
|
|
|
|
mean value: 0.011690139770507812
|
|
|
|
key: test_mcc
|
|
value: [0.6798418 0.8643122 0.81199794 0.47434165 0.93048421 0.69695062
|
|
0.599404 0.58316015 0.35410712 0.79476958]
|
|
|
|
mean value: 0.6789369269903351
|
|
|
|
key: train_mcc
|
|
value: [0.73826562 0.96830875 0.9213983 0.42421821 0.90645458 0.9218965
|
|
0.94451335 0.94577703 0.52778998 0.86687445]
|
|
|
|
mean value: 0.8165496770553766
|
|
|
|
key: test_fscore
|
|
value: [0.83333333 0.95454545 0.92307692 0.85714286 0.97560976 0.90909091
|
|
0.84210526 0.8 0.81632653 0.92307692]
|
|
|
|
mean value: 0.88343079501341
|
|
|
|
key: train_fscore
|
|
value: [0.87951807 0.98930481 0.97282609 0.83710407 0.96721311 0.97368421
|
|
0.98113208 0.98092643 0.86111111 0.94972067]
|
|
|
|
mean value: 0.9392540657250088
|
|
|
|
key: test_precision
|
|
value: [1. 0.91304348 1. 0.75 1. 0.86956522
|
|
0.88888889 0.93333333 0.68965517 0.94736842]
|
|
|
|
mean value: 0.8991854511340822
|
|
|
|
key: train_precision
|
|
value: [0.99319728 0.97883598 0.97814208 0.71984436 0.97790055 0.94871795
|
|
0.98378378 0.99447514 0.75609756 0.98837209]
|
|
|
|
mean value: 0.9319366769335261
|
|
|
|
key: test_recall
|
|
value: [0.71428571 1. 0.85714286 1. 0.95238095 0.95238095
|
|
0.8 0.7 1. 0.9 ]
|
|
|
|
mean value: 0.8876190476190476
|
|
|
|
key: train_recall
|
|
value: [0.78918919 1. 0.96756757 1. 0.95675676 1.
|
|
0.97849462 0.96774194 1. 0.91397849]
|
|
|
|
mean value: 0.9573728567276956
|
|
|
|
key: test_accuracy
|
|
value: [0.8125 0.9375 0.90322581 0.77419355 0.96774194 0.87096774
|
|
0.80645161 0.77419355 0.70967742 0.90322581]
|
|
|
|
mean value: 0.8459677419354839
|
|
|
|
key: train_accuracy
|
|
value: [0.85714286 0.98571429 0.96441281 0.74377224 0.95729537 0.96441281
|
|
0.97508897 0.97508897 0.78647687 0.93594306]
|
|
|
|
mean value: 0.9145348246059989
|
|
|
|
key: test_roc_auc
|
|
value: [0.85714286 0.90909091 0.92857143 0.65 0.97619048 0.82619048
|
|
0.80909091 0.80454545 0.59090909 0.90454545]
|
|
|
|
mean value: 0.8256277056277057
|
|
|
|
key: train_roc_auc
|
|
value: [0.88933144 0.97894737 0.96295045 0.625 0.95754505 0.94791667
|
|
0.97345784 0.97860781 0.68421053 0.94646293]
|
|
|
|
mean value: 0.8944430073112162
|
|
|
|
key: test_jcc
|
|
value: [0.71428571 0.91304348 0.85714286 0.75 0.95238095 0.83333333
|
|
0.72727273 0.66666667 0.68965517 0.85714286]
|
|
|
|
mean value: 0.7960923758899772
|
|
|
|
key: train_jcc
|
|
value: [0.78494624 0.97883598 0.94708995 0.71984436 0.93650794 0.94871795
|
|
0.96296296 0.96256684 0.75609756 0.90425532]
|
|
|
|
mean value: 0.89018250936949
|
|
|
|
key: TN
|
|
value: 81
|
|
|
|
mean value: 81.0
|
|
|
|
key: FP
|
|
value: 23
|
|
|
|
mean value: 23.0
|
|
|
|
key: FN
|
|
value: 25
|
|
|
|
mean value: 25.0
|
|
|
|
key: TP
|
|
value: 183
|
|
|
|
mean value: 183.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.76
|
|
|
|
Accuracy on Blind test: 0.89
|
|
|
|
Running classifier: 18
|
|
Model_name: AdaBoost Classifier
|
|
Model func: AdaBoostClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', AdaBoostClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.14809275 0.13004065 0.13075185 0.13108635 0.1305635 0.1305747
|
|
0.12959647 0.13134789 0.1296742 0.1307199 ]
|
|
|
|
mean value: 0.13224482536315918
|
|
|
|
key: score_time
|
|
value: [0.01494169 0.01492929 0.01494741 0.01512766 0.01498914 0.01503229
|
|
0.01501012 0.01628828 0.01516294 0.01494336]
|
|
|
|
mean value: 0.015137219429016113
|
|
|
|
key: test_mcc
|
|
value: [1. 1. 0.93048421 0.92687157 1. 0.86831345
|
|
0.93048421 0.85909091 0.93048421 0.72821908]
|
|
|
|
mean value: 0.9173947646273607
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 1. 0.97560976 0.97674419 1. 0.95
|
|
0.97560976 0.95 0.97560976 0.90909091]
|
|
|
|
mean value: 0.9712664363430102
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 0.95454545 1. 1.
|
|
0.95238095 0.95 0.95238095 0.83333333]
|
|
|
|
mean value: 0.9642640692640694
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.95238095 1. 1. 0.9047619
|
|
1. 0.95 1. 1. ]
|
|
|
|
mean value: 0.9807142857142856
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 1. 0.96774194 0.96774194 1. 0.93548387
|
|
0.96774194 0.93548387 0.96774194 0.87096774]
|
|
|
|
mean value: 0.9612903225806452
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 1. 0.97619048 0.95 1. 0.95238095
|
|
0.95454545 0.92954545 0.95454545 0.81818182]
|
|
|
|
mean value: 0.9535389610389611
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 1. 0.95238095 0.95454545 1. 0.9047619
|
|
0.95238095 0.9047619 0.95238095 0.83333333]
|
|
|
|
mean value: 0.9454545454545455
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 98
|
|
|
|
mean value: 98.0
|
|
|
|
key: FP
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 202
|
|
|
|
mean value: 202.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.9
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 19
|
|
Model_name: Bagging Classifier
|
|
Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
BaggingClassifier(n_jobs=10, oob_score=True,
|
|
random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.04230762 0.04330516 0.04764128 0.036551 0.05268097 0.04338908
|
|
0.04194522 0.05751896 0.04093218 0.03783798]
|
|
|
|
mean value: 0.04441094398498535
|
|
|
|
key: score_time
|
|
value: [0.02528048 0.02137256 0.01939631 0.0182085 0.03135848 0.02629328
|
|
0.02537656 0.02307057 0.02730846 0.0283308 ]
|
|
|
|
mean value: 0.024599599838256835
|
|
|
|
key: test_mcc
|
|
value: [0.93435318 1. 1. 0.92687157 0.93048421 0.93048421
|
|
0.93048421 0.79476958 0.85909091 0.72821908]
|
|
|
|
mean value: 0.9034756961158659
|
|
|
|
key: train_mcc
|
|
value: [0.9920858 0.98411246 0.99213963 1. 1. 0.99210029
|
|
1. 1. 0.99205967 0.99205967]
|
|
|
|
mean value: 0.9944557518725569
|
|
|
|
key: test_fscore
|
|
value: [0.97560976 1. 1. 0.97674419 0.97560976 0.97560976
|
|
0.97560976 0.92307692 0.95 0.90909091]
|
|
|
|
mean value: 0.9661351042604587
|
|
|
|
key: train_fscore
|
|
value: [0.99728997 0.99462366 0.99728997 1. 1. 0.99730458
|
|
1. 1. 0.99731903 0.99731903]
|
|
|
|
mean value: 0.9981146253628772
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 0.95454545 1. 1.
|
|
0.95238095 0.94736842 0.95 0.83333333]
|
|
|
|
mean value: 0.9637628161312373
|
|
|
|
key: train_precision
|
|
value: [1. 0.98930481 1. 1. 1. 0.99462366
|
|
1. 1. 0.99465241 0.99465241]
|
|
|
|
mean value: 0.9973233281582428
|
|
|
|
key: test_recall
|
|
value: [0.95238095 1. 1. 1. 0.95238095 0.95238095
|
|
1. 0.9 0.95 1. ]
|
|
|
|
mean value: 0.9707142857142858
|
|
|
|
key: train_recall
|
|
value: [0.99459459 1. 0.99459459 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.998918918918919
|
|
|
|
key: test_accuracy
|
|
value: [0.96875 1. 1. 0.96774194 0.96774194 0.96774194
|
|
0.96774194 0.90322581 0.93548387 0.87096774]
|
|
|
|
mean value: 0.9549395161290322
|
|
|
|
key: train_accuracy
|
|
value: [0.99642857 0.99285714 0.99644128 1. 1. 0.99644128
|
|
1. 1. 0.99644128 0.99644128]
|
|
|
|
mean value: 0.9975050838840873
|
|
|
|
key: test_roc_auc
|
|
value: [0.97619048 1. 1. 0.95 0.97619048 0.97619048
|
|
0.95454545 0.90454545 0.92954545 0.81818182]
|
|
|
|
mean value: 0.9485389610389611
|
|
|
|
key: train_roc_auc
|
|
value: [0.9972973 0.98947368 0.9972973 1. 1. 0.99479167
|
|
1. 1. 0.99473684 0.99473684]
|
|
|
|
mean value: 0.9968333629682314
|
|
|
|
key: test_jcc
|
|
value: [0.95238095 1. 1. 0.95454545 0.95238095 0.95238095
|
|
0.95238095 0.85714286 0.9047619 0.83333333]
|
|
|
|
mean value: 0.9359307359307361
|
|
|
|
key: train_jcc
|
|
value: [0.99459459 0.98930481 0.99459459 1. 1. 0.99462366
|
|
1. 1. 0.99465241 0.99465241]
|
|
|
|
mean value: 0.9962422470771616
|
|
|
|
key: TN
|
|
value: 98
|
|
|
|
mean value: 98.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 200
|
|
|
|
mean value: 200.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.84
|
|
|
|
Accuracy on Blind test: 0.93
|
|
|
|
Running classifier: 20
|
|
Model_name: Gaussian Process
|
|
Model func: GaussianProcessClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianProcessClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.06104779 0.05568504 0.03996634 0.04052663 0.08026958 0.09496188
|
|
0.0754683 0.08375978 0.0394938 0.04188108]
|
|
|
|
mean value: 0.06130602359771729
|
|
|
|
key: score_time
|
|
value: [0.01644206 0.01353455 0.01360631 0.01330638 0.02197981 0.01357412
|
|
0.02960944 0.01343012 0.01336837 0.02463293]
|
|
|
|
mean value: 0.017348408699035645
|
|
|
|
key: test_mcc
|
|
value: [0.15803489 0.4133805 0.12245134 0.01126872 0.55777335 0.18593394
|
|
0.43636364 0.02485134 0.40800555 0.33300791]
|
|
|
|
mean value: 0.26510711743317406
|
|
|
|
key: train_mcc
|
|
value: [0.88063607 0.92018324 0.85676692 0.84883567 0.87460224 0.88093695
|
|
0.8816558 0.88870395 0.88870395 0.87386742]
|
|
|
|
mean value: 0.8794892204012807
|
|
|
|
key: test_fscore
|
|
value: [0.76595745 0.83333333 0.75555556 0.73913043 0.875 0.7826087
|
|
0.8 0.69767442 0.82608696 0.79069767]
|
|
|
|
mean value: 0.7866044515677177
|
|
|
|
key: train_fscore
|
|
value: [0.96062992 0.97340426 0.95263158 0.95013123 0.95833333 0.96042216
|
|
0.96103896 0.96335079 0.96335079 0.95854922]
|
|
|
|
mean value: 0.9601842240561401
|
|
|
|
key: test_precision
|
|
value: [0.69230769 0.74074074 0.70833333 0.68 0.77777778 0.72
|
|
0.8 0.65217391 0.73076923 0.73913043]
|
|
|
|
mean value: 0.7241233122754862
|
|
|
|
key: train_precision
|
|
value: [0.93367347 0.95811518 0.92820513 0.92346939 0.92462312 0.93814433
|
|
0.92964824 0.93877551 0.93877551 0.925 ]
|
|
|
|
mean value: 0.933842987568305
|
|
|
|
key: test_recall
|
|
value: [0.85714286 0.95238095 0.80952381 0.80952381 1. 0.85714286
|
|
0.8 0.75 0.95 0.85 ]
|
|
|
|
mean value: 0.8635714285714287
|
|
|
|
key: train_recall
|
|
value: [0.98918919 0.98918919 0.97837838 0.97837838 0.99459459 0.98378378
|
|
0.99462366 0.98924731 0.98924731 0.99462366]
|
|
|
|
mean value: 0.9881255448997385
|
|
|
|
key: test_accuracy
|
|
value: [0.65625 0.75 0.64516129 0.61290323 0.80645161 0.67741935
|
|
0.74193548 0.58064516 0.74193548 0.70967742]
|
|
|
|
mean value: 0.6922379032258065
|
|
|
|
key: train_accuracy
|
|
value: [0.94642857 0.96428571 0.93594306 0.93238434 0.9430605 0.94661922
|
|
0.94661922 0.95017794 0.95017794 0.9430605 ]
|
|
|
|
mean value: 0.945875699034062
|
|
|
|
key: test_roc_auc
|
|
value: [0.56493506 0.65800866 0.5547619 0.5047619 0.7 0.57857143
|
|
0.71818182 0.51136364 0.65681818 0.65227273]
|
|
|
|
mean value: 0.6099675324675324
|
|
|
|
key: train_roc_auc
|
|
value: [0.92617354 0.95248933 0.91627252 0.91106419 0.9191723 0.92939189
|
|
0.92362762 0.93146576 0.93146576 0.91836446]
|
|
|
|
mean value: 0.92594873736215
|
|
|
|
key: test_jcc
|
|
value: [0.62068966 0.71428571 0.60714286 0.5862069 0.77777778 0.64285714
|
|
0.66666667 0.53571429 0.7037037 0.65384615]
|
|
|
|
mean value: 0.650889085371844
|
|
|
|
key: train_jcc
|
|
value: [0.92424242 0.94818653 0.90954774 0.905 0.92 0.92385787
|
|
0.925 0.92929293 0.92929293 0.92039801]
|
|
|
|
mean value: 0.9234818427989714
|
|
|
|
key: TN
|
|
value: 38
|
|
|
|
mean value: 38.0
|
|
|
|
key: FP
|
|
value: 28
|
|
|
|
mean value: 28.0
|
|
|
|
key: FN
|
|
value: 68
|
|
|
|
mean value: 68.0
|
|
|
|
key: TP
|
|
value: 178
|
|
|
|
mean value: 178.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.28
|
|
|
|
Accuracy on Blind test: 0.7
|
|
|
|
Running classifier: 21
|
|
Model_name: Gradient Boosting
|
|
Model func: GradientBoostingClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GradientBoostingClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.46115923 0.44185972 0.44269156 0.44909668 0.44474816 0.43808627
|
|
0.43469429 0.43570971 0.43906617 0.4296422 ]
|
|
|
|
mean value: 0.44167540073394773
|
|
|
|
key: score_time
|
|
value: [0.0092442 0.00915384 0.00920796 0.00919032 0.00921798 0.00926757
|
|
0.00912905 0.00921392 0.00900984 0.00932336]
|
|
|
|
mean value: 0.009195804595947266
|
|
|
|
key: test_mcc
|
|
value: [1. 1. 1. 0.85238095 1. 0.93048421
|
|
0.93048421 0.93048421 0.85909091 0.72821908]
|
|
|
|
mean value: 0.9231143573921694
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 1. 1. 0.95238095 1. 0.97560976
|
|
0.97560976 0.97560976 0.95 0.90909091]
|
|
|
|
mean value: 0.9738301129764544
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 0.95238095 1. 1.
|
|
0.95238095 0.95238095 0.95 0.83333333]
|
|
|
|
mean value: 0.964047619047619
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 0.95238095 1. 0.95238095
|
|
1. 1. 0.95 1. ]
|
|
|
|
mean value: 0.9854761904761904
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 1. 1. 0.93548387 1. 0.96774194
|
|
0.96774194 0.96774194 0.93548387 0.87096774]
|
|
|
|
mean value: 0.964516129032258
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 1. 1. 0.92619048 1. 0.97619048
|
|
0.95454545 0.95454545 0.92954545 0.81818182]
|
|
|
|
mean value: 0.9559199134199134
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 1. 1. 0.90909091 1. 0.95238095
|
|
0.95238095 0.95238095 0.9047619 0.83333333]
|
|
|
|
mean value: 0.9504329004329005
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 98
|
|
|
|
mean value: 98.0
|
|
|
|
key: FP
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 203
|
|
|
|
mean value: 203.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.87
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
Running classifier: 22
|
|
Model_name: QDA
|
|
Model func: QuadraticDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', QuadraticDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.0233779 0.02502084 0.02472115 0.03441906 0.02718186 0.03686452
|
|
0.0269208 0.0285058 0.02501702 0.0264864 ]
|
|
|
|
mean value: 0.02785153388977051
|
|
|
|
key: score_time
|
|
value: [0.01227784 0.01208687 0.01251578 0.01264119 0.01323295 0.0203855
|
|
0.01288342 0.01299381 0.01248217 0.01287174]
|
|
|
|
mean value: 0.013437128067016602
|
|
|
|
key: test_mcc
|
|
value: [ 0.21867346 0.0849412 -0.26560636 0.09967105 -0.05976143 0.00752923
|
|
0.01363636 -0.23927198 0.14863011 0.22469871]
|
|
|
|
mean value: 0.023314035374443605
|
|
|
|
key: train_mcc
|
|
value: [0.34354378 0.33200663 0.37383194 0.35226764 0.35226764 0.34110438
|
|
0.34382047 0.39766525 0.32040778 0.35507261]
|
|
|
|
mean value: 0.3511988107242974
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.78431373 0.70833333 0.8 0.75 0.7755102
|
|
0.65 0.63636364 0.75555556 0.7826087 ]
|
|
|
|
mean value: 0.7442685150476528
|
|
|
|
key: train_fscore
|
|
value: [0.82405345 0.82222222 0.82774049 0.82405345 0.82405345 0.82222222
|
|
0.8248337 0.83408072 0.82119205 0.82666667]
|
|
|
|
mean value: 0.8251118432979977
|
|
|
|
key: test_precision
|
|
value: [0.68965517 0.66666667 0.62962963 0.68965517 0.66666667 0.67857143
|
|
0.65 0.58333333 0.68 0.69230769]
|
|
|
|
mean value: 0.6626485762003004
|
|
|
|
key: train_precision
|
|
value: [0.70075758 0.69811321 0.70610687 0.70075758 0.70075758 0.69811321
|
|
0.70188679 0.71538462 0.69662921 0.70454545]
|
|
|
|
mean value: 0.7023052088462121
|
|
|
|
key: test_recall
|
|
value: [0.95238095 0.95238095 0.80952381 0.95238095 0.85714286 0.9047619
|
|
0.65 0.7 0.85 0.9 ]
|
|
|
|
mean value: 0.8528571428571429
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.6875 0.65625 0.5483871 0.67741935 0.61290323 0.64516129
|
|
0.5483871 0.48387097 0.64516129 0.67741935]
|
|
|
|
mean value: 0.6182459677419355
|
|
|
|
key: train_accuracy
|
|
value: [0.71785714 0.71428571 0.72597865 0.71886121 0.71886121 0.71530249
|
|
0.71886121 0.7366548 0.71174377 0.72241993]
|
|
|
|
mean value: 0.7200826131164211
|
|
|
|
key: test_roc_auc
|
|
value: [0.56709957 0.52164502 0.4047619 0.52619048 0.47857143 0.50238095
|
|
0.50681818 0.39545455 0.56136364 0.58636364]
|
|
|
|
mean value: 0.505064935064935
|
|
|
|
key: train_roc_auc
|
|
value: [0.58421053 0.57894737 0.59895833 0.58854167 0.58854167 0.58333333
|
|
0.58421053 0.61052632 0.57368421 0.58947368]
|
|
|
|
mean value: 0.5880427631578947
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.64516129 0.5483871 0.66666667 0.6 0.63333333
|
|
0.48148148 0.46666667 0.60714286 0.64285714]
|
|
|
|
mean value: 0.5958363201911588
|
|
|
|
key: train_jcc
|
|
value: [0.70075758 0.69811321 0.70610687 0.70075758 0.70075758 0.69811321
|
|
0.70188679 0.71538462 0.69662921 0.70454545]
|
|
|
|
mean value: 0.7023052088462121
|
|
|
|
key: TN
|
|
value: 17
|
|
|
|
mean value: 17.0
|
|
|
|
key: FP
|
|
value: 30
|
|
|
|
mean value: 30.0
|
|
|
|
key: FN
|
|
value: 89
|
|
|
|
mean value: 89.0
|
|
|
|
key: TP
|
|
value: 176
|
|
|
|
mean value: 176.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.17
|
|
|
|
Accuracy on Blind test: 0.68
|
|
|
|
Running classifier: 23
|
|
Model_name: Ridge Classifier
|
|
Model func: RidgeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02277946 0.03577924 0.03494143 0.03556728 0.03561592 0.03502131
|
|
0.03542185 0.03545427 0.03555131 0.03549528]
|
|
|
|
mean value: 0.03416273593902588
|
|
|
|
key: score_time
|
|
value: [0.02342391 0.02337527 0.02280545 0.02236032 0.02176046 0.02052379
|
|
0.02406287 0.0236938 0.0235455 0.02287507]
|
|
|
|
mean value: 0.0228426456451416
|
|
|
|
key: test_mcc
|
|
value: [1. 0.86147186 0.93048421 0.69695062 1. 0.85238095
|
|
0.85909091 0.79476958 0.93048421 0.72821908]
|
|
|
|
mean value: 0.8653851428550935
|
|
|
|
key: train_mcc
|
|
value: [0.94408115 0.95208091 0.95253998 0.95241514 0.95241514 0.95253998
|
|
0.96017122 0.96815373 0.96021134 0.96021134]
|
|
|
|
mean value: 0.9554819935656209
|
|
|
|
key: test_fscore
|
|
value: [1. 0.95238095 0.97560976 0.90909091 1. 0.95238095
|
|
0.95 0.92307692 0.97560976 0.90909091]
|
|
|
|
mean value: 0.9547240158215766
|
|
|
|
key: train_fscore
|
|
value: [0.98123324 0.98387097 0.98395722 0.98387097 0.98387097 0.98395722
|
|
0.98659517 0.98930481 0.98666667 0.98666667]
|
|
|
|
mean value: 0.9849993906126601
|
|
|
|
key: test_precision
|
|
value: [1. 0.95238095 1. 0.86956522 1. 0.95238095
|
|
0.95 0.94736842 0.95238095 0.83333333]
|
|
|
|
mean value: 0.9457409828920126
|
|
|
|
key: train_precision
|
|
value: [0.97340426 0.97860963 0.97354497 0.97860963 0.97860963 0.97354497
|
|
0.98395722 0.98404255 0.97883598 0.97883598]
|
|
|
|
mean value: 0.9781994809529229
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 0.95238095 0.95238095 1. 0.95238095
|
|
0.95 0.9 1. 1. ]
|
|
|
|
mean value: 0.9659523809523808
|
|
|
|
key: train_recall
|
|
value: [0.98918919 0.98918919 0.99459459 0.98918919 0.98918919 0.99459459
|
|
0.98924731 0.99462366 0.99462366 0.99462366]
|
|
|
|
mean value: 0.9919064225515838
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9375 0.96774194 0.87096774 1. 0.93548387
|
|
0.93548387 0.90322581 0.96774194 0.87096774]
|
|
|
|
mean value: 0.9389112903225808
|
|
|
|
key: train_accuracy
|
|
value: [0.975 0.97857143 0.97864769 0.97864769 0.97864769 0.97864769
|
|
0.98220641 0.98576512 0.98220641 0.98220641]
|
|
|
|
mean value: 0.9800546517539402
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.93073593 0.97619048 0.82619048 1. 0.92619048
|
|
0.92954545 0.90454545 0.95454545 0.81818182]
|
|
|
|
mean value: 0.9266125541125542
|
|
|
|
key: train_roc_auc
|
|
value: [0.96827881 0.97354196 0.97125563 0.97376126 0.97376126 0.97125563
|
|
0.97883418 0.98152235 0.9762592 0.9762592 ]
|
|
|
|
mean value: 0.9744729481178972
|
|
|
|
key: test_jcc
|
|
value: [1. 0.90909091 0.95238095 0.83333333 1. 0.90909091
|
|
0.9047619 0.85714286 0.95238095 0.83333333]
|
|
|
|
mean value: 0.9151515151515153
|
|
|
|
key: train_jcc
|
|
value: [0.96315789 0.96825397 0.96842105 0.96825397 0.96825397 0.96842105
|
|
0.97354497 0.97883598 0.97368421 0.97368421]
|
|
|
|
mean value: 0.9704511278195488
|
|
|
|
key: TN
|
|
value: 94
|
|
|
|
mean value: 94.0
|
|
|
|
key: FP
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: FN
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: TP
|
|
value: 199
|
|
|
|
mean value: 199.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.87
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
Running classifier: 24
|
|
Model_name: Ridge ClassifierCV
|
|
Model func: RidgeClassifierCV(cv=10)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifierCV(cv=10))])
|
|
|
|
key: fit_time
|
|
value: [0.23733401 0.24284005 0.25065804 0.2886591 0.24323916 0.2424953
|
|
0.43217683 0.28001761 0.36137295 0.34959149]
|
|
|
|
mean value: 0.292838454246521
|
|
|
|
key: score_time
|
|
value: [0.0211041 0.02119303 0.02349997 0.01739216 0.02394438 0.02036405
|
|
0.02439165 0.0242722 0.02366924 0.02450585]
|
|
|
|
mean value: 0.022433662414550783
|
|
|
|
key: test_mcc
|
|
value: [1. 0.86147186 0.93048421 0.69695062 1. 0.85238095
|
|
0.85909091 0.79476958 0.93048421 0.66057826]
|
|
|
|
mean value: 0.8586210606372333
|
|
|
|
key: train_mcc
|
|
value: [0.94408115 0.95208091 0.95253998 0.95241514 0.95241514 0.95253998
|
|
0.96017122 0.96815373 0.96021134 0.97611544]
|
|
|
|
mean value: 0.9570724042956436
|
|
|
|
key: test_fscore
|
|
value: [1. 0.95238095 0.97560976 0.90909091 1. 0.95238095
|
|
0.95 0.92307692 0.97560976 0.88888889]
|
|
|
|
mean value: 0.9527038138013747
|
|
|
|
key: train_fscore
|
|
value: [0.98123324 0.98387097 0.98395722 0.98387097 0.98387097 0.98395722
|
|
0.98659517 0.98930481 0.98666667 0.9919571 ]
|
|
|
|
mean value: 0.9855284344017574
|
|
|
|
key: test_precision
|
|
value: [1. 0.95238095 1. 0.86956522 1. 0.95238095
|
|
0.95 0.94736842 0.95238095 0.8 ]
|
|
|
|
mean value: 0.9424076495586793
|
|
|
|
key: train_precision
|
|
value: [0.97340426 0.97860963 0.97354497 0.97860963 0.97860963 0.97354497
|
|
0.98395722 0.98404255 0.97883598 0.98930481]
|
|
|
|
mean value: 0.9792463643527473
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 0.95238095 0.95238095 1. 0.95238095
|
|
0.95 0.9 1. 1. ]
|
|
|
|
mean value: 0.9659523809523808
|
|
|
|
key: train_recall
|
|
value: [0.98918919 0.98918919 0.99459459 0.98918919 0.98918919 0.99459459
|
|
0.98924731 0.99462366 0.99462366 0.99462366]
|
|
|
|
mean value: 0.9919064225515838
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.9375 0.96774194 0.87096774 1. 0.93548387
|
|
0.93548387 0.90322581 0.96774194 0.83870968]
|
|
|
|
mean value: 0.9356854838709678
|
|
|
|
key: train_accuracy
|
|
value: [0.975 0.97857143 0.97864769 0.97864769 0.97864769 0.97864769
|
|
0.98220641 0.98576512 0.98220641 0.98932384]
|
|
|
|
mean value: 0.9807663955261822
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.93073593 0.97619048 0.82619048 1. 0.92619048
|
|
0.92954545 0.90454545 0.95454545 0.77272727]
|
|
|
|
mean value: 0.9220670995670996
|
|
|
|
key: train_roc_auc
|
|
value: [0.96827881 0.97354196 0.97125563 0.97376126 0.97376126 0.97125563
|
|
0.97883418 0.98152235 0.9762592 0.98678551]
|
|
|
|
mean value: 0.9755255796968445
|
|
|
|
key: test_jcc
|
|
value: [1. 0.90909091 0.95238095 0.83333333 1. 0.90909091
|
|
0.9047619 0.85714286 0.95238095 0.8 ]
|
|
|
|
mean value: 0.9118181818181819
|
|
|
|
key: train_jcc
|
|
value: [0.96315789 0.96825397 0.96842105 0.96825397 0.96825397 0.96842105
|
|
0.97354497 0.97883598 0.97368421 0.98404255]
|
|
|
|
mean value: 0.9714869620860662
|
|
|
|
key: TN
|
|
value: 93
|
|
|
|
mean value: 93.0
|
|
|
|
key: FP
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: FN
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: TP
|
|
value: 199
|
|
|
|
mean value: 199.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:130: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
baseline_CV['Data_source'] = 'CV'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:131: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
baseline_CV['Resampling'] = rs_none
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:136: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
baseline_BT['Data_source'] = 'BT'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:137: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
baseline_BT['Resampling'] = rs_none
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.87
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
PASS: sorting df by score that is mapped onto the order I want
|
|
|
|
==============================================================
|
|
|
|
Running several classification models (n): 24
|
|
List of models:
|
|
('Logistic Regression', LogisticRegression(random_state=42))
|
|
('Logistic RegressionCV', LogisticRegressionCV(random_state=42))
|
|
('Gaussian NB', GaussianNB())
|
|
('Naive Bayes', BernoulliNB())
|
|
('K-Nearest Neighbors', KNeighborsClassifier())
|
|
('SVC', SVC(random_state=42))
|
|
('MLP', MLPClassifier(max_iter=500, random_state=42))
|
|
('Decision Tree', DecisionTreeClassifier(random_state=42))
|
|
('Extra Trees', ExtraTreesClassifier(random_state=42))
|
|
('Extra Tree', ExtraTreeClassifier(random_state=42))
|
|
('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42))
|
|
('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42))
|
|
('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0))
|
|
('LDA', LinearDiscriminantAnalysis())
|
|
('Multinomial', MultinomialNB())
|
|
('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42))
|
|
('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42))
|
|
('AdaBoost Classifier', AdaBoostClassifier(random_state=42))
|
|
('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))
|
|
('Gaussian Process', GaussianProcessClassifier(random_state=42))
|
|
('Gradient Boosting', GradientBoostingClassifier(random_state=42))
|
|
('QDA', QuadraticDiscriminantAnalysis())
|
|
('Ridge Classifier', RidgeClassifier(random_state=42))
|
|
('Ridge ClassifierCV', RidgeClassifierCV(cv=10))
|
|
|
|
================================================================
|
|
|
|
|
|
Running classifier: 1
|
|
Model_name: Logistic Regression
|
|
Model func: LogisticRegression(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegression(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.03308296 0.03610563 0.03306627 0.03525186 0.03636479 0.03501225
|
|
0.03478789 0.0367043 0.03446531 0.03514433]
|
|
|
|
mean value: 0.03499855995178223
|
|
|
|
key: score_time
|
|
value: [0.01234603 0.01233053 0.01335764 0.01235819 0.01245904 0.01250768
|
|
0.01237798 0.01265287 0.01228142 0.01239014]
|
|
|
|
mean value: 0.01250615119934082
|
|
|
|
key: test_mcc
|
|
value: [0.95346259 0.90889326 0.85441771 0.76500781 0.85441771 0.8547619
|
|
0.80817439 0.70714286 0.90238095 0.85441771]
|
|
|
|
mean value: 0.846307688531032
|
|
|
|
key: train_mcc
|
|
value: [0.94066423 0.95136525 0.93025158 0.94614468 0.9355233 0.95160448
|
|
0.956873 0.940826 0.940826 0.94071491]
|
|
|
|
mean value: 0.9434793423451845
|
|
|
|
key: test_fscore
|
|
value: [0.97674419 0.95 0.93023256 0.87179487 0.93023256 0.92682927
|
|
0.89473684 0.85 0.95 0.92307692]
|
|
|
|
mean value: 0.9203647207595322
|
|
|
|
key: train_fscore
|
|
value: [0.97002725 0.97560976 0.96438356 0.97282609 0.96721311 0.97547684
|
|
0.97849462 0.9701897 0.9701897 0.9703504 ]
|
|
|
|
mean value: 0.9714761038408097
|
|
|
|
key: test_precision
|
|
value: [0.95454545 1. 0.90909091 0.94444444 0.90909091 0.95
|
|
0.94444444 0.85 0.95 0.94736842]
|
|
|
|
mean value: 0.9358984582668792
|
|
|
|
key: train_precision
|
|
value: [0.97802198 0.97826087 0.97777778 0.97814208 0.97790055 0.98351648
|
|
0.97849462 0.97814208 0.97814208 0.97297297]
|
|
|
|
mean value: 0.978137148750473
|
|
|
|
key: test_recall
|
|
value: [1. 0.9047619 0.95238095 0.80952381 0.95238095 0.9047619
|
|
0.85 0.85 0.95 0.9 ]
|
|
|
|
mean value: 0.9073809523809523
|
|
|
|
key: train_recall
|
|
value: [0.96216216 0.97297297 0.95135135 0.96756757 0.95675676 0.96756757
|
|
0.97849462 0.96236559 0.96236559 0.96774194]
|
|
|
|
mean value: 0.9649346120313862
|
|
|
|
key: test_accuracy
|
|
value: [0.97619048 0.95238095 0.92682927 0.87804878 0.92682927 0.92682927
|
|
0.90243902 0.85365854 0.95121951 0.92682927]
|
|
|
|
mean value: 0.9221254355400698
|
|
|
|
key: train_accuracy
|
|
value: [0.97027027 0.97567568 0.96495957 0.97304582 0.96765499 0.97574124
|
|
0.97843666 0.9703504 0.9703504 0.9703504 ]
|
|
|
|
mean value: 0.9716835433816566
|
|
|
|
key: test_roc_auc
|
|
value: [0.97619048 0.95238095 0.92619048 0.8797619 0.92619048 0.92738095
|
|
0.90119048 0.85357143 0.95119048 0.92619048]
|
|
|
|
mean value: 0.9220238095238095
|
|
|
|
key: train_roc_auc
|
|
value: [0.97027027 0.97567568 0.96492299 0.9730311 0.96762569 0.97571927
|
|
0.9784365 0.97037198 0.97037198 0.97035745]
|
|
|
|
mean value: 0.9716782911944202
|
|
|
|
key: test_jcc
|
|
value: [0.95454545 0.9047619 0.86956522 0.77272727 0.86956522 0.86363636
|
|
0.80952381 0.73913043 0.9047619 0.85714286]
|
|
|
|
mean value: 0.8545360436664785
|
|
|
|
key: train_jcc
|
|
value: [0.94179894 0.95238095 0.93121693 0.94708995 0.93650794 0.95212766
|
|
0.95789474 0.94210526 0.94210526 0.94240838]
|
|
|
|
mean value: 0.9445636008690423
|
|
|
|
key: TN
|
|
value: 193
|
|
|
|
mean value: 193.0
|
|
|
|
key: FP
|
|
value: 19
|
|
|
|
mean value: 19.0
|
|
|
|
key: FN
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: TP
|
|
value: 187
|
|
|
|
mean value: 187.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.75
|
|
|
|
Accuracy on Blind test: 0.88
|
|
|
|
Running classifier: 2
|
|
Model_name: Logistic RegressionCV
|
|
Model func: LogisticRegressionCV(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegressionCV(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.76303077 0.8300209 0.73767257 0.85287404 0.74666619 0.72793317
|
|
0.82305479 0.78377914 0.7454052 0.87989378]
|
|
|
|
mean value: 0.789033055305481
|
|
|
|
key: score_time
|
|
value: [0.01280284 0.01379657 0.01262498 0.01349235 0.01266551 0.01262546
|
|
0.01262879 0.01256895 0.01259089 0.0126822 ]
|
|
|
|
mean value: 0.01284785270690918
|
|
|
|
key: test_mcc
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
[0.9047619 0.9047619 0.90692382 0.90238095 0.95227002 0.90692382
|
|
0.95227002 0.7565654 1. 0.95238095]
|
|
|
|
mean value: 0.9139238793157022
|
|
|
|
key: train_mcc
|
|
value: [1. 0.99460913 1. 1. 0.99462366 1.
|
|
0.9946235 0.9946235 0.98927544 1. ]
|
|
|
|
mean value: 0.9967755223131768
|
|
|
|
key: test_fscore
|
|
value: [0.95238095 0.95238095 0.95 0.95238095 0.97674419 0.95
|
|
0.97435897 0.87179487 1. 0.97560976]
|
|
|
|
mean value: 0.9555650645440774
|
|
|
|
key: train_fscore
|
|
value: [1. 0.99730458 1. 1. 0.99730458 1.
|
|
0.99731903 0.99731903 0.99465241 1. ]
|
|
|
|
mean value: 0.998389964054269
|
|
|
|
key: test_precision
|
|
value: [0.95238095 0.95238095 1. 0.95238095 0.95454545 1.
|
|
1. 0.89473684 1. 0.95238095]
|
|
|
|
mean value: 0.9658806106174527
|
|
|
|
key: train_precision
|
|
value: [1. 0.99462366 1. 1. 0.99462366 1.
|
|
0.99465241 0.99465241 0.9893617 1. ]
|
|
|
|
mean value: 0.9967913826789842
|
|
|
|
key: test_recall
|
|
value: [0.95238095 0.95238095 0.9047619 0.95238095 1. 0.9047619
|
|
0.95 0.85 1. 1. ]
|
|
|
|
mean value: 0.9466666666666667
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.95238095 0.95238095 0.95121951 0.95121951 0.97560976 0.95121951
|
|
0.97560976 0.87804878 1. 0.97560976]
|
|
|
|
mean value: 0.9563298490127756
|
|
|
|
key: train_accuracy
|
|
value: [1. 0.9972973 1. 1. 0.99730458 1.
|
|
0.99730458 0.99730458 0.99460916 1. ]
|
|
|
|
mean value: 0.998382020834851
|
|
|
|
key: test_roc_auc
|
|
value: [0.95238095 0.95238095 0.95238095 0.95119048 0.975 0.95238095
|
|
0.975 0.87738095 1. 0.97619048]
|
|
|
|
mean value: 0.9564285714285713
|
|
|
|
key: train_roc_auc
|
|
value: [1. 0.9972973 1. 1. 0.99731183 1.
|
|
0.9972973 0.9972973 0.99459459 1. ]
|
|
|
|
mean value: 0.9983798314443476
|
|
|
|
key: test_jcc
|
|
value: [0.90909091 0.90909091 0.9047619 0.90909091 0.95454545 0.9047619
|
|
0.95 0.77272727 1. 0.95238095]
|
|
|
|
mean value: 0.9166450216450215
|
|
|
|
key: train_jcc
|
|
value: [1. 0.99462366 1. 1. 0.99462366 1.
|
|
0.99465241 0.99465241 0.9893617 1. ]
|
|
|
|
mean value: 0.9967913826789842
|
|
|
|
key: TN
|
|
value: 199
|
|
|
|
mean value: 199.0
|
|
|
|
key: FP
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 195
|
|
|
|
mean value: 195.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.86
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
Running classifier: 3
|
|
Model_name: Gaussian NB
|
|
Model func: GaussianNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianNB())])
|
|
|
|
key: fit_time
|
|
value: [0.01330519 0.01353216 0.01044512 0.00971842 0.00956607 0.00976133
|
|
0.00986981 0.00953341 0.00945997 0.00939202]
|
|
|
|
mean value: 0.01045835018157959
|
|
|
|
key: score_time
|
|
value: [0.01211691 0.01043463 0.01057625 0.00960898 0.00906467 0.008955
|
|
0.00923419 0.00883865 0.00894046 0.00897145]
|
|
|
|
mean value: 0.00967411994934082
|
|
|
|
key: test_mcc
|
|
value: [0.52380952 0.3478328 0.41963703 0.36718832 0.65871309 0.51966679
|
|
0.41766229 0.26904762 0.56836003 0.56836003]
|
|
|
|
mean value: 0.46602775272823205
|
|
|
|
key: train_mcc
|
|
value: [0.49994094 0.51360753 0.57218883 0.55700213 0.53457148 0.57456037
|
|
0.54451453 0.57340242 0.5491086 0.58606107]
|
|
|
|
mean value: 0.550495789809396
|
|
|
|
key: test_fscore
|
|
value: [0.76190476 0.70833333 0.73913043 0.71111111 0.8372093 0.7826087
|
|
0.71428571 0.63414634 0.79069767 0.79069767]
|
|
|
|
mean value: 0.7470125043695909
|
|
|
|
key: train_fscore
|
|
value: [0.76092545 0.77078086 0.79487179 0.78880407 0.77749361 0.7979798
|
|
0.7826087 0.7979798 0.78350515 0.805 ]
|
|
|
|
mean value: 0.7859949224802306
|
|
|
|
key: test_precision
|
|
value: [0.76190476 0.62962963 0.68 0.66666667 0.81818182 0.72
|
|
0.68181818 0.61904762 0.73913043 0.73913043]
|
|
|
|
mean value: 0.7055509546813894
|
|
|
|
key: train_precision
|
|
value: [0.7254902 0.72169811 0.75609756 0.74519231 0.73786408 0.74881517
|
|
0.74634146 0.75238095 0.75247525 0.75233645]
|
|
|
|
mean value: 0.7438691533419045
|
|
|
|
key: test_recall
|
|
value: [0.76190476 0.80952381 0.80952381 0.76190476 0.85714286 0.85714286
|
|
0.75 0.65 0.85 0.85 ]
|
|
|
|
mean value: 0.7957142857142856
|
|
|
|
key: train_recall
|
|
value: [0.8 0.82702703 0.83783784 0.83783784 0.82162162 0.85405405
|
|
0.82258065 0.84946237 0.8172043 0.8655914 ]
|
|
|
|
mean value: 0.8333217088055799
|
|
|
|
key: test_accuracy
|
|
value: [0.76190476 0.66666667 0.70731707 0.68292683 0.82926829 0.75609756
|
|
0.70731707 0.63414634 0.7804878 0.7804878 ]
|
|
|
|
mean value: 0.7306620209059232
|
|
|
|
key: train_accuracy
|
|
value: [0.74864865 0.75405405 0.78436658 0.77628032 0.76549865 0.78436658
|
|
0.77088949 0.78436658 0.77358491 0.78975741]
|
|
|
|
mean value: 0.7731813214832083
|
|
|
|
key: test_roc_auc
|
|
value: [0.76190476 0.66666667 0.7047619 0.68095238 0.82857143 0.75357143
|
|
0.70833333 0.63452381 0.78214286 0.78214286]
|
|
|
|
mean value: 0.7303571428571429
|
|
|
|
key: train_roc_auc
|
|
value: [0.74864865 0.75405405 0.78451032 0.7764458 0.76564952 0.78455391
|
|
0.77074978 0.78419064 0.77346702 0.78955246]
|
|
|
|
mean value: 0.7731822144725371
|
|
|
|
key: test_jcc
|
|
value: [0.61538462 0.5483871 0.5862069 0.55172414 0.72 0.64285714
|
|
0.55555556 0.46428571 0.65384615 0.65384615]
|
|
|
|
mean value: 0.5992093467032289
|
|
|
|
key: train_jcc
|
|
value: [0.61410788 0.62704918 0.65957447 0.6512605 0.63598326 0.66386555
|
|
0.64285714 0.66386555 0.6440678 0.67364017]
|
|
|
|
mean value: 0.6476271499298714
|
|
|
|
key: TN
|
|
value: 137
|
|
|
|
mean value: 137.0
|
|
|
|
key: FP
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: FN
|
|
value: 69
|
|
|
|
mean value: 69.0
|
|
|
|
key: TP
|
|
value: 164
|
|
|
|
mean value: 164.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.43
|
|
|
|
Accuracy on Blind test: 0.74
|
|
|
|
Running classifier: 4
|
|
Model_name: Naive Bayes
|
|
Model func: BernoulliNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', BernoulliNB())])
|
|
|
|
key: fit_time
|
|
value: [0.01092005 0.01027369 0.00994992 0.00966907 0.00953984 0.00965834
|
|
0.00972724 0.00973678 0.00973964 0.00964236]
|
|
|
|
mean value: 0.009885692596435547
|
|
|
|
key: score_time
|
|
value: [0.00965691 0.00880075 0.00904751 0.00887823 0.00878286 0.00878644
|
|
0.00877023 0.00896668 0.00879884 0.00876403]
|
|
|
|
mean value: 0.008925247192382812
|
|
|
|
key: test_mcc
|
|
value: [0.19611614 0.24253563 0.35038478 0.37171226 0.59982886 0.62325386
|
|
0.62048368 0.21957752 0.27179142 0.46300848]
|
|
|
|
mean value: 0.3958692609699289
|
|
|
|
key: train_mcc
|
|
value: [0.5197894 0.47073308 0.4803209 0.47192026 0.46367088 0.47192026
|
|
0.45388122 0.48220059 0.47119158 0.49005654]
|
|
|
|
mean value: 0.4775684717659203
|
|
|
|
key: test_fscore
|
|
value: [0.54054054 0.57894737 0.58823529 0.66666667 0.74285714 0.78947368
|
|
0.70967742 0.55555556 0.57142857 0.71794872]
|
|
|
|
mean value: 0.646133096110126
|
|
|
|
key: train_fscore
|
|
value: [0.72507553 0.67515924 0.69538462 0.68535826 0.67507886 0.68535826
|
|
0.6809816 0.70658683 0.7005988 0.71764706]
|
|
|
|
mean value: 0.694722903766912
|
|
|
|
key: test_precision
|
|
value: [0.625 0.64705882 0.76923077 0.72222222 0.92857143 0.88235294
|
|
1. 0.625 0.66666667 0.73684211]
|
|
|
|
mean value: 0.7602944956660127
|
|
|
|
key: train_precision
|
|
value: [0.82191781 0.82170543 0.80714286 0.80882353 0.81060606 0.80882353
|
|
0.79285714 0.7972973 0.79054054 0.79220779]
|
|
|
|
mean value: 0.8051921984050987
|
|
|
|
key: test_recall
|
|
value: [0.47619048 0.52380952 0.47619048 0.61904762 0.61904762 0.71428571
|
|
0.55 0.5 0.5 0.7 ]
|
|
|
|
mean value: 0.567857142857143
|
|
|
|
key: train_recall
|
|
value: [0.64864865 0.57297297 0.61081081 0.59459459 0.57837838 0.59459459
|
|
0.59677419 0.6344086 0.62903226 0.65591398]
|
|
|
|
mean value: 0.6116129032258065
|
|
|
|
key: test_accuracy
|
|
value: [0.5952381 0.61904762 0.65853659 0.68292683 0.7804878 0.80487805
|
|
0.7804878 0.6097561 0.63414634 0.73170732]
|
|
|
|
mean value: 0.6897212543554007
|
|
|
|
key: train_accuracy
|
|
value: [0.75405405 0.72432432 0.73315364 0.7277628 0.72237197 0.7277628
|
|
0.71967655 0.73584906 0.73045822 0.74123989]
|
|
|
|
mean value: 0.7316653310992934
|
|
|
|
key: test_roc_auc
|
|
value: [0.5952381 0.61904762 0.66309524 0.68452381 0.78452381 0.80714286
|
|
0.775 0.60714286 0.63095238 0.73095238]
|
|
|
|
mean value: 0.6897619047619047
|
|
|
|
key: train_roc_auc
|
|
value: [0.75405405 0.72432432 0.73282476 0.72740482 0.72198489 0.72740482
|
|
0.72000872 0.73612322 0.73073235 0.7414705 ]
|
|
|
|
mean value: 0.7316332461493751
|
|
|
|
key: test_jcc
|
|
value: [0.37037037 0.40740741 0.41666667 0.5 0.59090909 0.65217391
|
|
0.55 0.38461538 0.4 0.56 ]
|
|
|
|
mean value: 0.48321428330123994
|
|
|
|
key: train_jcc
|
|
value: [0.56872038 0.50961538 0.53301887 0.52132701 0.50952381 0.52132701
|
|
0.51627907 0.5462963 0.53917051 0.55963303]
|
|
|
|
mean value: 0.5324911370145777
|
|
|
|
key: TN
|
|
value: 167
|
|
|
|
mean value: 167.0
|
|
|
|
key: FP
|
|
value: 89
|
|
|
|
mean value: 89.0
|
|
|
|
key: FN
|
|
value: 39
|
|
|
|
mean value: 39.0
|
|
|
|
key: TP
|
|
value: 117
|
|
|
|
mean value: 117.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.28
|
|
|
|
Accuracy on Blind test: 0.63
|
|
|
|
Running classifier: 5
|
|
Model_name: K-Nearest Neighbors
|
|
Model func: KNeighborsClassifier()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', KNeighborsClassifier())])
|
|
|
|
key: fit_time
|
|
value: [0.00904441 0.00994539 0.00996995 0.00994349 0.00990462 0.00975704
|
|
0.00993729 0.00986505 0.00999761 0.00940776]
|
|
|
|
mean value: 0.009777259826660157
|
|
|
|
key: score_time
|
|
value: [0.01108098 0.0159514 0.01206779 0.01446748 0.01251292 0.01263952
|
|
0.01265025 0.01304054 0.01275277 0.01227689]
|
|
|
|
mean value: 0.01294405460357666
|
|
|
|
key: test_mcc
|
|
value: [0.43052839 0.53357838 0.27338837 0.41766229 0.6133669 0.51190476
|
|
0.51320273 0.42916625 0.51190476 0.31655495]
|
|
|
|
mean value: 0.4551257782268342
|
|
|
|
key: train_mcc
|
|
value: [0.64509306 0.67193129 0.66110925 0.6784174 0.65112849 0.68828497
|
|
0.66576577 0.65242388 0.66119778 0.68278387]
|
|
|
|
mean value: 0.6658135765240021
|
|
|
|
key: test_fscore
|
|
value: [0.7 0.73684211 0.61538462 0.7 0.8 0.76190476
|
|
0.73684211 0.64705882 0.75 0.63157895]
|
|
|
|
mean value: 0.7079611358713526
|
|
|
|
key: train_fscore
|
|
value: [0.81460674 0.82913165 0.82548476 0.83146067 0.81792717 0.83888889
|
|
0.83333333 0.81690141 0.82644628 0.83746556]
|
|
|
|
mean value: 0.8271646480205639
|
|
|
|
key: test_precision
|
|
value: [0.73684211 0.82352941 0.66666667 0.73684211 0.84210526 0.76190476
|
|
0.77777778 0.78571429 0.75 0.66666667]
|
|
|
|
mean value: 0.7548049044179075
|
|
|
|
key: train_precision
|
|
value: [0.84795322 0.86046512 0.84659091 0.86549708 0.84883721 0.86285714
|
|
0.83333333 0.85798817 0.84745763 0.85875706]
|
|
|
|
mean value: 0.8529736858206451
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.66666667 0.57142857 0.66666667 0.76190476 0.76190476
|
|
0.7 0.55 0.75 0.6 ]
|
|
|
|
mean value: 0.6695238095238094
|
|
|
|
key: train_recall
|
|
value: [0.78378378 0.8 0.80540541 0.8 0.78918919 0.81621622
|
|
0.83333333 0.77956989 0.80645161 0.8172043 ]
|
|
|
|
mean value: 0.803115373437954
|
|
|
|
key: test_accuracy
|
|
value: [0.71428571 0.76190476 0.63414634 0.70731707 0.80487805 0.75609756
|
|
0.75609756 0.70731707 0.75609756 0.65853659]
|
|
|
|
mean value: 0.7256678281068524
|
|
|
|
key: train_accuracy
|
|
value: [0.82162162 0.83513514 0.83018868 0.83827493 0.82479784 0.84366577
|
|
0.8328841 0.82479784 0.83018868 0.84097035]
|
|
|
|
mean value: 0.8322524950826837
|
|
|
|
key: test_roc_auc
|
|
value: [0.71428571 0.76190476 0.63571429 0.70833333 0.80595238 0.75595238
|
|
0.7547619 0.70357143 0.75595238 0.65714286]
|
|
|
|
mean value: 0.7253571428571429
|
|
|
|
key: train_roc_auc
|
|
value: [0.82162162 0.83513514 0.83012206 0.83817204 0.82470212 0.84359198
|
|
0.83288288 0.82492008 0.83025283 0.84103458]
|
|
|
|
mean value: 0.832243533856437
|
|
|
|
key: test_jcc
|
|
value: [0.53846154 0.58333333 0.44444444 0.53846154 0.66666667 0.61538462
|
|
0.58333333 0.47826087 0.6 0.46153846]
|
|
|
|
mean value: 0.5509884801189149
|
|
|
|
key: train_jcc
|
|
value: [0.68720379 0.70813397 0.70283019 0.71153846 0.69194313 0.72248804
|
|
0.71428571 0.69047619 0.70422535 0.72037915]
|
|
|
|
mean value: 0.7053503983012377
|
|
|
|
key: TN
|
|
value: 161
|
|
|
|
mean value: 161.0
|
|
|
|
key: FP
|
|
value: 68
|
|
|
|
mean value: 68.0
|
|
|
|
key: FN
|
|
value: 45
|
|
|
|
mean value: 45.0
|
|
|
|
key: TP
|
|
value: 138
|
|
|
|
mean value: 138.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.37
|
|
|
|
Accuracy on Blind test: 0.7
|
|
|
|
Running classifier: 6
|
|
Model_name: SVC
|
|
Model func: SVC(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SVC(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02168798 0.01971412 0.01750493 0.0180738 0.01735806 0.01955819
|
|
0.01806736 0.02039933 0.01962757 0.02065182]
|
|
|
|
mean value: 0.01926431655883789
|
|
|
|
key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
score_time
|
|
value: [0.01141286 0.01185846 0.01218891 0.01214862 0.01135635 0.01124072
|
|
0.01116204 0.0116775 0.01220202 0.01134872]
|
|
|
|
mean value: 0.011659622192382812
|
|
|
|
key: test_mcc
|
|
value: [0.66742381 0.62187434 0.56086079 0.65952381 0.66432098 0.7098505
|
|
0.7197263 0.51190476 0.76500781 0.71121921]
|
|
|
|
mean value: 0.6591712308091895
|
|
|
|
key: train_mcc
|
|
value: [0.78382959 0.81626392 0.79519893 0.80055217 0.80596972 0.81143372
|
|
0.83298455 0.81132843 0.79514676 0.80063366]
|
|
|
|
mean value: 0.8053341444808453
|
|
|
|
key: test_fscore
|
|
value: [0.8372093 0.8 0.79069767 0.82926829 0.84444444 0.86363636
|
|
0.83333333 0.75 0.88372093 0.85714286]
|
|
|
|
mean value: 0.828945319821667
|
|
|
|
key: train_fscore
|
|
value: [0.89130435 0.9076087 0.89784946 0.90026954 0.90217391 0.90616622
|
|
0.91733333 0.90616622 0.89784946 0.90133333]
|
|
|
|
mean value: 0.9028054529376849
|
|
|
|
key: test_precision
|
|
value: [0.81818182 0.84210526 0.77272727 0.85 0.79166667 0.82608696
|
|
0.9375 0.75 0.82608696 0.81818182]
|
|
|
|
mean value: 0.8232536751958948
|
|
|
|
key: train_precision
|
|
value: [0.89617486 0.91256831 0.89304813 0.89784946 0.90710383 0.89893617
|
|
0.91005291 0.90374332 0.89784946 0.89417989]
|
|
|
|
mean value: 0.9011506337562538
|
|
|
|
key: test_recall
|
|
value: [0.85714286 0.76190476 0.80952381 0.80952381 0.9047619 0.9047619
|
|
0.75 0.75 0.95 0.9 ]
|
|
|
|
mean value: 0.8397619047619047
|
|
|
|
key: train_recall
|
|
value: [0.88648649 0.9027027 0.9027027 0.9027027 0.8972973 0.91351351
|
|
0.92473118 0.90860215 0.89784946 0.90860215]
|
|
|
|
mean value: 0.9045190351641965
|
|
|
|
key: test_accuracy
|
|
value: [0.83333333 0.80952381 0.7804878 0.82926829 0.82926829 0.85365854
|
|
0.85365854 0.75609756 0.87804878 0.85365854]
|
|
|
|
mean value: 0.8277003484320558
|
|
|
|
key: train_accuracy
|
|
value: [0.89189189 0.90810811 0.89757412 0.90026954 0.90296496 0.90566038
|
|
0.91644205 0.90566038 0.89757412 0.90026954]
|
|
|
|
mean value: 0.9026415094339623
|
|
|
|
key: test_roc_auc
|
|
value: [0.83333333 0.80952381 0.7797619 0.8297619 0.82738095 0.85238095
|
|
0.85119048 0.75595238 0.8797619 0.8547619 ]
|
|
|
|
mean value: 0.8273809523809523
|
|
|
|
key: train_roc_auc
|
|
value: [0.89189189 0.90810811 0.89758791 0.90027608 0.90294972 0.90568149
|
|
0.91641965 0.90565243 0.89757338 0.90024702]
|
|
|
|
mean value: 0.9026387678000581
|
|
|
|
key: test_jcc
|
|
value: [0.72 0.66666667 0.65384615 0.70833333 0.73076923 0.76
|
|
0.71428571 0.6 0.79166667 0.75 ]
|
|
|
|
mean value: 0.7095567765567765
|
|
|
|
key: train_jcc
|
|
value: [0.80392157 0.83084577 0.81463415 0.81862745 0.82178218 0.82843137
|
|
0.84729064 0.82843137 0.81463415 0.82038835]
|
|
|
|
mean value: 0.8228986996659561
|
|
|
|
key: TN
|
|
value: 168
|
|
|
|
mean value: 168.0
|
|
|
|
key: FP
|
|
value: 33
|
|
|
|
mean value: 33.0
|
|
|
|
key: FN
|
|
value: 38
|
|
|
|
mean value: 38.0
|
|
|
|
key: TP
|
|
value: 173
|
|
|
|
mean value: 173.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.55
|
|
|
|
Accuracy on Blind test: 0.8
|
|
|
|
Running classifier: 7
|
|
Model_name: MLP
|
|
Model func: MLPClassifier(max_iter=500, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MLPClassifier(max_iter=500, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [1.29383063 1.44016743 1.28876901 1.44384265 1.41407895 1.30052304
|
|
1.70417809 1.71098447 1.61445165 1.84017086]
|
|
|
|
mean value: 1.5050996780395507
|
|
|
|
key: score_time
|
|
value: [0.01451826 0.01390624 0.01395798 0.01398897 0.01401591 0.01408267
|
|
0.01418805 0.01403236 0.02189183 0.01375771]
|
|
|
|
mean value: 0.014833998680114747
|
|
|
|
key: test_mcc
|
|
value: [0.95346259 0.81322028 0.86333169 0.80907152 0.90649828 0.8547619
|
|
0.86240942 0.65871309 0.95238095 0.85441771]
|
|
|
|
mean value: 0.852826743808685
|
|
|
|
key: train_mcc
|
|
value: [1. 0.99460913 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9994609125148145
|
|
|
|
key: test_fscore
|
|
value: [0.97674419 0.90909091 0.92307692 0.9 0.95454545 0.92682927
|
|
0.91891892 0.82051282 0.97560976 0.92307692]
|
|
|
|
mean value: 0.9228405159658705
|
|
|
|
key: train_fscore
|
|
value: [1. 0.99730458 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9997304582210242
|
|
|
|
key: test_precision
|
|
value: [0.95454545 0.86956522 1. 0.94736842 0.91304348 0.95
|
|
1. 0.84210526 0.95238095 0.94736842]
|
|
|
|
mean value: 0.9376377207841738
|
|
|
|
key: train_precision
|
|
value: [1. 0.99462366 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9994623655913978
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 0.85714286 0.85714286 1. 0.9047619
|
|
0.85 0.8 1. 0.9 ]
|
|
|
|
mean value: 0.9121428571428571
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.97619048 0.9047619 0.92682927 0.90243902 0.95121951 0.92682927
|
|
0.92682927 0.82926829 0.97560976 0.92682927]
|
|
|
|
mean value: 0.9246806039488968
|
|
|
|
key: train_accuracy
|
|
value: [1. 0.9972973 1. 1. 1. 1. 1.
|
|
1. 1. 1. ]
|
|
|
|
mean value: 0.9997297297297297
|
|
|
|
key: test_roc_auc
|
|
value: [0.97619048 0.9047619 0.92857143 0.90357143 0.95 0.92738095
|
|
0.925 0.82857143 0.97619048 0.92619048]
|
|
|
|
mean value: 0.924642857142857
|
|
|
|
key: train_roc_auc
|
|
value: [1. 0.9972973 1. 1. 1. 1. 1.
|
|
1. 1. 1. ]
|
|
|
|
mean value: 0.9997297297297297
|
|
|
|
key: test_jcc
|
|
value: [0.95454545 0.83333333 0.85714286 0.81818182 0.91304348 0.86363636
|
|
0.85 0.69565217 0.95238095 0.85714286]
|
|
|
|
mean value: 0.859505928853755
|
|
|
|
key: train_jcc
|
|
value: [1. 0.99462366 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9994623655913978
|
|
|
|
key: TN
|
|
value: 193
|
|
|
|
mean value: 193.0
|
|
|
|
key: FP
|
|
value: 18
|
|
|
|
mean value: 18.0
|
|
|
|
key: FN
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: TP
|
|
value: 188
|
|
|
|
mean value: 188.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.59
|
|
|
|
Accuracy on Blind test: 0.8
|
|
|
|
Running classifier: 8
|
|
Model_name: Decision Tree
|
|
Model func: DecisionTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', DecisionTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02476811 0.02039862 0.01846981 0.01775837 0.0182929 0.01789904
|
|
0.01828575 0.01807499 0.01761913 0.01760459]
|
|
|
|
mean value: 0.018917131423950195
|
|
|
|
key: score_time
|
|
value: [0.0121634 0.00941205 0.00896049 0.00882649 0.00887442 0.00888562
|
|
0.00931406 0.00897431 0.00980282 0.00914001]
|
|
|
|
mean value: 0.009435367584228516
|
|
|
|
key: test_mcc
|
|
value: [0.82462113 0.95346259 1. 0.70714286 0.90692382 0.8547619
|
|
0.86240942 0.95238095 0.90238095 0.90238095]
|
|
|
|
mean value: 0.8866464572934346
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.89473684 0.97674419 1. 0.85714286 0.95 0.92682927
|
|
0.91891892 0.97560976 0.95 0.95 ]
|
|
|
|
mean value: 0.9399981828603794
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.95454545 1. 0.85714286 1. 0.95
|
|
1. 0.95238095 0.95 0.95 ]
|
|
|
|
mean value: 0.9614069264069263
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.80952381 1. 1. 0.85714286 0.9047619 0.9047619
|
|
0.85 1. 0.95 0.95 ]
|
|
|
|
mean value: 0.9226190476190477
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.9047619 0.97619048 1. 0.85365854 0.95121951 0.92682927
|
|
0.92682927 0.97560976 0.95121951 0.95121951]
|
|
|
|
mean value: 0.9417537746806038
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.9047619 0.97619048 1. 0.85357143 0.95238095 0.92738095
|
|
0.925 0.97619048 0.95119048 0.95119048]
|
|
|
|
mean value: 0.9417857142857142
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.80952381 0.95454545 1. 0.75 0.9047619 0.86363636
|
|
0.85 0.95238095 0.9047619 0.9047619 ]
|
|
|
|
mean value: 0.8894372294372295
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 198
|
|
|
|
mean value: 198.0
|
|
|
|
key: FP
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 190
|
|
|
|
mean value: 190.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.86
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
Running classifier: 9
|
|
Model_name: Extra Trees
|
|
Model func: ExtraTreesClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreesClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.11411691 0.11291766 0.11341381 0.113482 0.11605811 0.11524677
|
|
0.1146853 0.11328244 0.1139667 0.11258531]
|
|
|
|
mean value: 0.11397550106048585
|
|
|
|
key: score_time
|
|
value: [0.01812983 0.01800847 0.01816654 0.01818466 0.01819277 0.0194211
|
|
0.01798368 0.01814198 0.01803756 0.01890779]
|
|
|
|
mean value: 0.018317437171936034
|
|
|
|
key: test_mcc
|
|
value: [0.76277007 0.80952381 0.65871309 0.75714286 0.8547619 0.7098505
|
|
0.7197263 0.70714286 0.85441771 0.7565654 ]
|
|
|
|
mean value: 0.7590614509822682
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.88372093 0.9047619 0.8372093 0.87804878 0.92682927 0.86363636
|
|
0.83333333 0.85 0.92307692 0.87179487]
|
|
|
|
mean value: 0.8772411677942025
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.86363636 0.9047619 0.81818182 0.9 0.95 0.82608696
|
|
0.9375 0.85 0.94736842 0.89473684]
|
|
|
|
mean value: 0.8892272306259722
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.9047619 0.9047619 0.85714286 0.85714286 0.9047619 0.9047619
|
|
0.75 0.85 0.9 0.85 ]
|
|
|
|
mean value: 0.8683333333333334
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.88095238 0.9047619 0.82926829 0.87804878 0.92682927 0.85365854
|
|
0.85365854 0.85365854 0.92682927 0.87804878]
|
|
|
|
mean value: 0.8785714285714284
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.88095238 0.9047619 0.82857143 0.87857143 0.92738095 0.85238095
|
|
0.85119048 0.85357143 0.92619048 0.87738095]
|
|
|
|
mean value: 0.8780952380952382
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.79166667 0.82608696 0.72 0.7826087 0.86363636 0.76
|
|
0.71428571 0.73913043 0.85714286 0.77272727]
|
|
|
|
mean value: 0.7827284961415396
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 183
|
|
|
|
mean value: 183.0
|
|
|
|
key: FP
|
|
value: 27
|
|
|
|
mean value: 27.0
|
|
|
|
key: FN
|
|
value: 23
|
|
|
|
mean value: 23.0
|
|
|
|
key: TP
|
|
value: 179
|
|
|
|
mean value: 179.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.59
|
|
|
|
Accuracy on Blind test: 0.82
|
|
|
|
Running classifier: 10
|
|
Model_name: Extra Tree
|
|
Model func: ExtraTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00981283 0.010782 0.0110085 0.01046014 0.00998545 0.00999331
|
|
0.00993228 0.00992513 0.0098927 0.00979471]
|
|
|
|
mean value: 0.010158705711364745
|
|
|
|
key: score_time
|
|
value: [0.00894189 0.00929785 0.00908732 0.00908136 0.00908279 0.00907326
|
|
0.00922275 0.00893164 0.00892639 0.0089097 ]
|
|
|
|
mean value: 0.009055495262145996
|
|
|
|
key: test_mcc
|
|
value: [0.47673129 0.48112522 0.38060103 0.75714286 0.46623254 0.21823107
|
|
0.31960727 0.44466675 0.37171226 0.16945156]
|
|
|
|
mean value: 0.40855018487405237
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.74418605 0.75555556 0.64864865 0.87804878 0.75555556 0.63636364
|
|
0.61111111 0.625 0.69767442 0.56410256]
|
|
|
|
mean value: 0.6916246316941156
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.72727273 0.70833333 0.75 0.9 0.70833333 0.60869565
|
|
0.6875 0.83333333 0.65217391 0.57894737]
|
|
|
|
mean value: 0.7154589660911173
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
[0.76190476 0.80952381 0.57142857 0.85714286 0.80952381 0.66666667
|
|
0.55 0.5 0.75 0.55 ]
|
|
|
|
mean value: 0.6826190476190476
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.73809524 0.73809524 0.68292683 0.87804878 0.73170732 0.6097561
|
|
0.65853659 0.70731707 0.68292683 0.58536585]
|
|
|
|
mean value: 0.7012775842044133
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.73809524 0.73809524 0.68571429 0.87857143 0.7297619 0.60833333
|
|
0.65595238 0.70238095 0.68452381 0.58452381]
|
|
|
|
mean value: 0.7005952380952382
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.59259259 0.60714286 0.48 0.7826087 0.60714286 0.46666667
|
|
0.44 0.45454545 0.53571429 0.39285714]
|
|
|
|
mean value: 0.535927055231403
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 148
|
|
|
|
mean value: 148.0
|
|
|
|
key: FP
|
|
value: 65
|
|
|
|
mean value: 65.0
|
|
|
|
key: FN
|
|
value: 58
|
|
|
|
mean value: 58.0
|
|
|
|
key: TP
|
|
value: 141
|
|
|
|
mean value: 141.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.12
|
|
|
|
Accuracy on Blind test: 0.59
|
|
|
|
Running classifier: 11
|
|
Model_name: Random Forest
|
|
Model func: RandomForestClassifier(n_estimators=1000, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(n_estimators=1000, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [1.66840959 1.6835866 1.67349887 1.60515428 1.62686896 1.62213206
|
|
1.61394548 1.60469913 1.62373829 1.62986398]
|
|
|
|
mean value: 1.6351897239685058
|
|
|
|
key: score_time
|
|
value: [0.15618062 0.0988071 0.09145093 0.09110022 0.09156513 0.09141111
|
|
0.09112048 0.09319663 0.0919323 0.09166527]
|
|
|
|
mean value: 0.09884297847747803
|
|
|
|
key: test_mcc
|
|
value: [0.90889326 1. 0.80817439 0.90649828 1. 0.80817439
|
|
0.90238095 0.8547619 0.90692382 0.95238095]
|
|
|
|
mean value: 0.9048187955773696
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.95454545 1. 0.90909091 0.95454545 1. 0.90909091
|
|
0.95 0.92682927 0.95238095 0.97560976]
|
|
|
|
mean value: 0.9532092704043922
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.91304348 1. 0.86956522 0.91304348 1. 0.86956522
|
|
0.95 0.9047619 0.90909091 0.95238095]
|
|
|
|
mean value: 0.9281451157538113
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.95238095 1. 1. 0.95238095
|
|
0.95 0.95 1. 1. ]
|
|
|
|
mean value: 0.9804761904761904
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.95238095 1. 0.90243902 0.95121951 1. 0.90243902
|
|
0.95121951 0.92682927 0.95121951 0.97560976]
|
|
|
|
mean value: 0.9513356562137052
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.95238095 1. 0.90119048 0.95 1. 0.90119048
|
|
0.95119048 0.92738095 0.95238095 0.97619048]
|
|
|
|
mean value: 0.9511904761904763
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.91304348 1. 0.83333333 0.91304348 1. 0.83333333
|
|
0.9047619 0.86363636 0.90909091 0.95238095]
|
|
|
|
mean value: 0.9122623753058535
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 190
|
|
|
|
mean value: 190.0
|
|
|
|
key: FP
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: FN
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: TP
|
|
value: 202
|
|
|
|
mean value: 202.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.88
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 12
|
|
Model_name: Random Forest2
|
|
Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...age_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10,
|
|
oob_score=True, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.96576428 1.03775167 0.95333004 0.91306329 0.939183 0.94196749
|
|
0.9377737 0.94258761 0.93521285 0.94724011]
|
|
|
|
mean value: 0.9513874053955078
|
|
|
|
key: score_time
|
|
value: [0.22685552 0.20241451 0.17469525 0.20963097 0.21572113 0.19469857
|
|
0.20701981 0.23400688 0.21420932 0.19970131]
|
|
|
|
mean value: 0.20789532661437987
|
|
|
|
key: test_mcc
|
|
value: [0.90889326 0.95346259 0.80817439 0.80817439 0.90238095 0.7565654
|
|
0.85441771 0.8047619 0.86333169 0.90692382]
|
|
|
|
mean value: 0.8567086116301835
|
|
|
|
key: train_mcc
|
|
value: [0.98391316 0.97860715 0.98395676 0.98395676 0.97866529 0.97866529
|
|
0.97866283 0.98395537 0.97866283 0.9946235 ]
|
|
|
|
mean value: 0.9823668931195029
|
|
|
|
key: test_fscore
|
|
value: [0.95454545 0.97560976 0.90909091 0.90909091 0.95238095 0.88372093
|
|
0.92307692 0.9 0.93023256 0.95238095]
|
|
|
|
mean value: 0.9290129345035755
|
|
|
|
key: train_fscore
|
|
value: [0.9919571 0.98930481 0.9919571 0.9919571 0.98930481 0.98930481
|
|
0.9893617 0.992 0.9893617 0.99731903]
|
|
|
|
mean value: 0.9911828191283462
|
|
|
|
key: test_precision
|
|
value: [0.91304348 1. 0.86956522 0.86956522 0.95238095 0.86363636
|
|
0.94736842 0.9 0.86956522 0.90909091]
|
|
|
|
mean value: 0.9094215776595638
|
|
|
|
key: train_precision
|
|
value: [0.98404255 0.97883598 0.98404255 0.98404255 0.97883598 0.97883598
|
|
0.97894737 0.98412698 0.97894737 0.99465241]
|
|
|
|
mean value: 0.9825309723468607
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 0.95238095 0.95238095 0.95238095 0.9047619
|
|
0.9 0.9 1. 1. ]
|
|
|
|
mean value: 0.9514285714285714
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.95238095 0.97619048 0.90243902 0.90243902 0.95121951 0.87804878
|
|
0.92682927 0.90243902 0.92682927 0.95121951]
|
|
|
|
mean value: 0.9270034843205576
|
|
|
|
key: train_accuracy
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
[0.99189189 0.98918919 0.99191375 0.99191375 0.98921833 0.98921833
|
|
0.98921833 0.99191375 0.98921833 0.99730458]
|
|
|
|
mean value: 0.9911000218547388
|
|
|
|
key: test_roc_auc
|
|
value: [0.95238095 0.97619048 0.90119048 0.90119048 0.95119048 0.87738095
|
|
0.92619048 0.90238095 0.92857143 0.95238095]
|
|
|
|
mean value: 0.9269047619047619
|
|
|
|
key: train_roc_auc
|
|
value: [0.99189189 0.98918919 0.99193548 0.99193548 0.98924731 0.98924731
|
|
0.98918919 0.99189189 0.98918919 0.9972973 ]
|
|
|
|
mean value: 0.9911014240046498
|
|
|
|
key: test_jcc
|
|
value: [0.91304348 0.95238095 0.83333333 0.83333333 0.90909091 0.79166667
|
|
0.85714286 0.81818182 0.86956522 0.90909091]
|
|
|
|
mean value: 0.8686829474872952
|
|
|
|
key: train_jcc
|
|
value: [0.98404255 0.97883598 0.98404255 0.98404255 0.97883598 0.97883598
|
|
0.97894737 0.98412698 0.97894737 0.99465241]
|
|
|
|
mean value: 0.9825309723468607
|
|
|
|
key: TN
|
|
value: 186
|
|
|
|
mean value: 186.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 20
|
|
|
|
mean value: 20.0
|
|
|
|
key: TP
|
|
value: 196
|
|
|
|
mean value: 196.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.87
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
Running classifier: 13
|
|
Model_name: XGBoost
|
|
Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...
|
|
interaction_constraints=None, learning_rate=None,
|
|
max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan,
|
|
monotone_constraints=None, n_estimators=100,
|
|
n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None,
|
|
reg_lambda=None, scale_pos_weight=None,
|
|
subsample=None, tree_method=None,
|
|
use_label_encoder=False,
|
|
validate_parameters=None, verbosity=0))])
|
|
|
|
key: fit_time
|
|
value: [0.10359287 0.05772686 0.06652761 0.057899 0.05960846 0.05904317
|
|
0.06005931 0.20866203 0.05684566 0.05950522]
|
|
|
|
mean value: 0.07894701957702636
|
|
|
|
key: score_time
|
|
value: [0.01138711 0.01046109 0.01090193 0.01055264 0.01067448 0.0106988
|
|
0.01056552 0.01120353 0.01074457 0.01049185]
|
|
|
|
mean value: 0.01076815128326416
|
|
|
|
key: test_mcc
|
|
value: [1. 0.95346259 0.95238095 0.85441771 0.95238095 0.95238095
|
|
0.90649828 0.86333169 0.90238095 0.95238095]
|
|
|
|
mean value: 0.928961503294655
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.97674419 0.97560976 0.93023256 0.97560976 0.97560976
|
|
0.94736842 0.93023256 0.95 0.97560976]
|
|
|
|
mean value: 0.9637016747768457
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.95454545 1. 0.90909091 1. 1.
|
|
1. 0.86956522 0.95 0.95238095]
|
|
|
|
mean value: 0.963558253340862
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.95238095 0.95238095 0.95238095 0.95238095
|
|
0.9 1. 0.95 1. ]
|
|
|
|
mean value: 0.9659523809523808
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.97619048 0.97560976 0.92682927 0.97560976 0.97560976
|
|
0.95121951 0.92682927 0.95121951 0.97560976]
|
|
|
|
mean value: 0.963472706155633
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.97619048 0.97619048 0.92619048 0.97619048 0.97619048
|
|
0.95 0.92857143 0.95119048 0.97619048]
|
|
|
|
mean value: 0.9636904761904761
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.95454545 0.95238095 0.86956522 0.95238095 0.95238095
|
|
0.9 0.86956522 0.9047619 0.95238095]
|
|
|
|
mean value: 0.9307961603613778
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 198
|
|
|
|
mean value: 198.0
|
|
|
|
key: FP
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 199
|
|
|
|
mean value: 199.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.9
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 14
|
|
Model_name: LDA
|
|
Model func: LinearDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LinearDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.03956175 0.06763196 0.06599092 0.04733467 0.07237792 0.04720092
|
|
0.08412266 0.0399518 0.10612369 0.05623484]
|
|
|
|
mean value: 0.06265311241149903
|
|
|
|
key: score_time
|
|
value: [0.02280021 0.02403569 0.02058625 0.01286125 0.01242781 0.02172351
|
|
0.0125792 0.02332878 0.03291178 0.02500701]
|
|
|
|
mean value: 0.020826148986816406
|
|
|
|
key: test_mcc
|
|
value: [1. 0.90889326 0.76500781 0.80907152 0.8547619 0.8547619
|
|
0.90649828 0.60952381 0.90238095 0.7098505 ]
|
|
|
|
mean value: 0.8320749938182688
|
|
|
|
key: train_mcc
|
|
value: [0.97843556 0.98379816 0.98384191 0.98384144 0.9784365 0.98384191
|
|
0.97305937 0.97306016 0.96227841 0.98384191]
|
|
|
|
mean value: 0.9784435309419832
|
|
|
|
key: test_fscore
|
|
value: [1. 0.95 0.87179487 0.9 0.92682927 0.92682927
|
|
0.94736842 0.8 0.95 0.84210526]
|
|
|
|
mean value: 0.9114927092590766
|
|
|
|
key: train_fscore
|
|
value: [0.98924731 0.99191375 0.99191375 0.99186992 0.98918919 0.99191375
|
|
0.98659517 0.98652291 0.98113208 0.99191375]
|
|
|
|
mean value: 0.9892211567024891
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.94444444 0.94736842 0.95 0.95
|
|
1. 0.8 0.95 0.88888889]
|
|
|
|
mean value: 0.9430701754385964
|
|
|
|
key: train_precision
|
|
value: [0.98395722 0.98924731 0.98924731 0.99456522 0.98918919 0.98924731
|
|
0.98395722 0.98918919 0.98378378 0.99459459]
|
|
|
|
mean value: 0.9886978348134606
|
|
|
|
key: test_recall
|
|
value: [1. 0.9047619 0.80952381 0.85714286 0.9047619 0.9047619
|
|
0.9 0.8 0.95 0.8 ]
|
|
|
|
mean value: 0.883095238095238
|
|
|
|
key: train_recall
|
|
value: [0.99459459 0.99459459 0.99459459 0.98918919 0.98918919 0.99459459
|
|
0.98924731 0.98387097 0.97849462 0.98924731]
|
|
|
|
mean value: 0.9897616971810521
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.95238095 0.87804878 0.90243902 0.92682927 0.92682927
|
|
0.95121951 0.80487805 0.95121951 0.85365854]
|
|
|
|
mean value: 0.9147502903600465
|
|
|
|
key: train_accuracy
|
|
value: [0.98918919 0.99189189 0.99191375 0.99191375 0.98921833 0.99191375
|
|
0.98652291 0.98652291 0.98113208 0.99191375]
|
|
|
|
mean value: 0.9892132294019087
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.95238095 0.8797619 0.90357143 0.92738095 0.92738095
|
|
0.95 0.8047619 0.95119048 0.85238095]
|
|
|
|
mean value: 0.9148809523809524
|
|
|
|
key: train_roc_auc
|
|
value: [0.98918919 0.99189189 0.99192095 0.99190642 0.98921825 0.99192095
|
|
0.98651555 0.98653008 0.9811392 0.99192095]
|
|
|
|
mean value: 0.9892153443766347
|
|
|
|
key: test_jcc
|
|
value: [1. 0.9047619 0.77272727 0.81818182 0.86363636 0.86363636
|
|
0.9 0.66666667 0.9047619 0.72727273]
|
|
|
|
mean value: 0.8421645021645021
|
|
|
|
key: train_jcc
|
|
value: [0.9787234 0.98395722 0.98395722 0.98387097 0.97860963 0.98395722
|
|
0.97354497 0.97340426 0.96296296 0.98395722]
|
|
|
|
mean value: 0.9786945066498138
|
|
|
|
key: TN
|
|
value: 195
|
|
|
|
mean value: 195.0
|
|
|
|
key: FP
|
|
value: 24
|
|
|
|
mean value: 24.0
|
|
|
|
key: FN
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: TP
|
|
value: 182
|
|
|
|
mean value: 182.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.77
|
|
|
|
Accuracy on Blind test: 0.9
|
|
|
|
Running classifier: 15
|
|
Model_name: Multinomial
|
|
Model func: MultinomialNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MultinomialNB())])
|
|
|
|
key: fit_time
|
|
value: [0.01337481 0.01335168 0.00990558 0.00938869 0.01046014 0.01006222
|
|
0.00962472 0.00944638 0.00971985 0.0095315 ]
|
|
|
|
mean value: 0.010486555099487305
|
|
|
|
key: score_time
|
|
value: [0.01207018 0.01088357 0.00918961 0.00873971 0.00949311 0.00893283
|
|
0.00898933 0.00877428 0.00887108 0.00888586]
|
|
|
|
mean value: 0.009482955932617188
|
|
|
|
key: test_mcc
|
|
value: [0.38138504 0.43052839 0.16909989 0.12803277 0.56190476 0.51320273
|
|
0.31960727 0.16945156 0.47003614 0.41428571]
|
|
|
|
mean value: 0.355753425845874
|
|
|
|
key: train_mcc
|
|
value: [0.37851665 0.42162778 0.48790839 0.46638766 0.43403959 0.47709968
|
|
0.4555575 0.46631793 0.41295769 0.48248903]
|
|
|
|
mean value: 0.4482901898810191
|
|
|
|
key: test_fscore
|
|
value: [0.69767442 0.72727273 0.62222222 0.52631579 0.7804878 0.77272727
|
|
0.61111111 0.56410256 0.74418605 0.7 ]
|
|
|
|
mean value: 0.6746099956903909
|
|
|
|
key: train_fscore
|
|
value: [0.69333333 0.7115903 0.74114441 0.73458445 0.71849866 0.73854447
|
|
0.73066667 0.73315364 0.69972452 0.74331551]
|
|
|
|
mean value: 0.724455595971974
|
|
|
|
key: test_precision
|
|
value: [0.68181818 0.69565217 0.58333333 0.58823529 0.8 0.73913043
|
|
0.6875 0.57894737 0.69565217 0.7 ]
|
|
|
|
mean value: 0.675026896029891
|
|
|
|
key: train_precision
|
|
value: [0.68421053 0.70967742 0.74725275 0.7287234 0.71276596 0.73655914
|
|
0.72486772 0.73513514 0.71751412 0.7393617 ]
|
|
|
|
mean value: 0.7236067880834754
|
|
|
|
key: test_recall
|
|
value: [0.71428571 0.76190476 0.66666667 0.47619048 0.76190476 0.80952381
|
|
0.55 0.55 0.8 0.7 ]
|
|
|
|
mean value: 0.6790476190476191
|
|
|
|
key: train_recall
|
|
value: [0.7027027 0.71351351 0.73513514 0.74054054 0.72432432 0.74054054
|
|
0.73655914 0.7311828 0.6827957 0.74731183]
|
|
|
|
mean value: 0.7254606219122348
|
|
|
|
key: test_accuracy
|
|
value: [0.69047619 0.71428571 0.58536585 0.56097561 0.7804878 0.75609756
|
|
0.65853659 0.58536585 0.73170732 0.70731707]
|
|
|
|
mean value: 0.6770615563298489
|
|
|
|
key: train_accuracy
|
|
value: [0.68918919 0.71081081 0.74393531 0.73315364 0.71698113 0.73854447
|
|
0.7277628 0.73315364 0.70619946 0.74123989]
|
|
|
|
mean value: 0.7240970350404312
|
|
|
|
key: test_roc_auc
|
|
value: [0.69047619 0.71428571 0.58333333 0.56309524 0.78095238 0.7547619
|
|
0.65595238 0.58452381 0.73333333 0.70714286]
|
|
|
|
mean value: 0.6767857142857143
|
|
|
|
key: train_roc_auc
|
|
value: [0.68918919 0.71081081 0.74391165 0.7331735 0.71700087 0.73854984
|
|
0.72773903 0.73315897 0.70626271 0.74122348]
|
|
|
|
mean value: 0.7241020052310374
|
|
|
|
key: test_jcc
|
|
value: [0.53571429 0.57142857 0.4516129 0.35714286 0.64 0.62962963
|
|
0.44 0.39285714 0.59259259 0.53846154]
|
|
|
|
mean value: 0.5149439521052425
|
|
|
|
key: train_jcc
|
|
value: [0.53061224 0.55230126 0.58874459 0.58050847 0.56066946 0.58547009
|
|
0.57563025 0.5787234 0.53813559 0.59148936]
|
|
|
|
mean value: 0.5682284716264602
|
|
|
|
key: TN
|
|
value: 139
|
|
|
|
mean value: 139.0
|
|
|
|
key: FP
|
|
value: 66
|
|
|
|
mean value: 66.0
|
|
|
|
key: FN
|
|
value: 67
|
|
|
|
mean value: 67.0
|
|
|
|
key: TP
|
|
value: 140
|
|
|
|
mean value: 140.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.43
|
|
|
|
Accuracy on Blind test: 0.72
|
|
|
|
Running classifier: 16
|
|
Model_name: Passive Aggresive
|
|
Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
PassiveAggressiveClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01812387 0.01968002 0.02329946 0.02299213 0.02618003 0.02688098
|
|
0.02359438 0.02325869 0.02430868 0.02280593]
|
|
|
|
mean value: 0.02311241626739502
|
|
|
|
key: score_time
|
|
value: [0.00875401 0.01142693 0.01191044 0.01212454 0.01197791 0.01205945
|
|
0.0119586 0.0119884 0.01208591 0.0119648 ]
|
|
|
|
mean value: 0.011625099182128906
|
|
|
|
key: test_mcc
|
|
value: [1. 0.95346259 0.95238095 0.90238095 1. 0.95238095
|
|
0.90649828 0.76500781 0.95238095 0.90238095]
|
|
|
|
mean value: 0.9286873437646441
|
|
|
|
key: train_mcc
|
|
value: [0.97837838 0.97310093 0.98927606 0.978494 0.98384191 0.98384191
|
|
0.9784365 0.98927544 0.9784365 0.97317407]
|
|
|
|
mean value: 0.9806255690715325
|
|
|
|
key: test_fscore
|
|
value: [1. 0.97560976 0.97560976 0.95238095 1. 0.97560976
|
|
0.94736842 0.88372093 0.97560976 0.95 ]
|
|
|
|
mean value: 0.9635909328056386
|
|
|
|
key: train_fscore
|
|
value: [0.98918919 0.98659517 0.99462366 0.98924731 0.99191375 0.99191375
|
|
0.98924731 0.99465241 0.98924731 0.98644986]
|
|
|
|
mean value: 0.9903079719026987
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 0.95238095 1. 1.
|
|
1. 0.82608696 0.95238095 0.95 ]
|
|
|
|
mean value: 0.9680848861283643
|
|
|
|
key: train_precision
|
|
value: [0.98918919 0.9787234 0.98930481 0.98395722 0.98924731 0.98924731
|
|
0.98924731 0.9893617 0.98924731 0.99453552]
|
|
|
|
mean value: 0.9882061094095242
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 0.95238095 0.95238095 1. 0.95238095
|
|
0.9 0.95 1. 0.95 ]
|
|
|
|
mean value: 0.9609523809523808
|
|
|
|
key: train_recall
|
|
value: [0.98918919 0.99459459 1. 0.99459459 0.99459459 0.99459459
|
|
0.98924731 1. 0.98924731 0.97849462]
|
|
|
|
mean value: 0.9924556814879397
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.97619048 0.97560976 0.95121951 1. 0.97560976
|
|
0.95121951 0.87804878 0.97560976 0.95121951]
|
|
|
|
mean value: 0.963472706155633
|
|
|
|
key: train_accuracy
|
|
value: [0.98918919 0.98648649 0.99460916 0.98921833 0.99191375 0.99191375
|
|
0.98921833 0.99460916 0.98921833 0.98652291]
|
|
|
|
mean value: 0.9902899395352224
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.97619048 0.97619048 0.95119048 1. 0.97619048
|
|
0.95 0.8797619 0.97619048 0.95119048]
|
|
|
|
mean value: 0.9636904761904761
|
|
|
|
key: train_roc_auc
|
|
value: [0.98918919 0.98648649 0.99462366 0.98923278 0.99192095 0.99192095
|
|
0.98921825 0.99459459 0.98921825 0.98654461]
|
|
|
|
mean value: 0.9902949723917466
|
|
|
|
key: test_jcc
|
|
value: [1. 0.95238095 0.95238095 0.90909091 1. 0.95238095
|
|
0.9 0.79166667 0.95238095 0.9047619 ]
|
|
|
|
mean value: 0.931504329004329
|
|
|
|
key: train_jcc
|
|
value: [0.97860963 0.97354497 0.98930481 0.9787234 0.98395722 0.98395722
|
|
0.9787234 0.9893617 0.9787234 0.97326203]
|
|
|
|
mean value: 0.9808167797529499
|
|
|
|
key: TN
|
|
value: 199
|
|
|
|
mean value: 199.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 198
|
|
|
|
mean value: 198.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.81
|
|
|
|
Accuracy on Blind test: 0.92
|
|
|
|
Running classifier: 17
|
|
Model_name: Stochastic GDescent
|
|
Model func: SGDClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SGDClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01709509 0.01672006 0.01696658 0.01680875 0.01753879 0.01735544
|
|
0.01576948 0.01762867 0.01622939 0.01648998]
|
|
|
|
mean value: 0.016860222816467284
|
|
|
|
key: score_time
|
|
value: [0.01177645 0.01180029 0.01178408 0.01206207 0.01212597 0.01212263
|
|
0.01181698 0.01177001 0.0121057 0.01177335]
|
|
|
|
mean value: 0.011913752555847168
|
|
|
|
key: test_mcc
|
|
value: [0.8660254 0.82462113 0.74124932 0.90238095 0.95238095 0.85441771
|
|
0.90238095 0.7565654 0.86333169 0.67700771]
|
|
|
|
mean value: 0.8340361214926171
|
|
|
|
key: train_mcc
|
|
value: [0.84285241 0.87540087 0.90229328 0.978494 0.92586351 0.90487588
|
|
0.96294605 0.97317407 0.90722239 0.80791198]
|
|
|
|
mean value: 0.908103443769595
|
|
|
|
key: test_fscore
|
|
value: [0.92307692 0.89473684 0.83333333 0.95238095 0.97560976 0.93023256
|
|
0.95 0.87179487 0.93023256 0.8 ]
|
|
|
|
mean value: 0.9061397795067976
|
|
|
|
key: train_fscore
|
|
value: [0.90909091 0.93142857 0.94586895 0.98924731 0.96111111 0.95287958
|
|
0.98153034 0.98644986 0.95384615 0.88288288]
|
|
|
|
mean value: 0.9494335674714925
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 0.95238095 1. 0.90909091
|
|
0.95 0.89473684 0.86956522 0.93333333]
|
|
|
|
mean value: 0.9509107254301762
|
|
|
|
key: train_precision
|
|
value: [0.99358974 0.98787879 1. 0.98395722 0.98857143 0.92385787
|
|
0.96373057 0.99453552 0.91176471 1. ]
|
|
|
|
mean value: 0.9747885842267824
|
|
|
|
key: test_recall
|
|
value: [0.85714286 0.80952381 0.71428571 0.95238095 0.95238095 0.95238095
|
|
0.95 0.85 1. 0.7 ]
|
|
|
|
mean value: 0.8738095238095237
|
|
|
|
key: train_recall
|
|
value: [0.83783784 0.88108108 0.8972973 0.99459459 0.93513514 0.98378378
|
|
1. 0.97849462 1. 0.79032258]
|
|
|
|
mean value: 0.9298546934030807
|
|
|
|
key: test_accuracy
|
|
value: [0.92857143 0.9047619 0.85365854 0.95121951 0.97560976 0.92682927
|
|
0.95121951 0.87804878 0.92682927 0.82926829]
|
|
|
|
mean value: 0.9126016260162603
|
|
|
|
key: train_accuracy
|
|
value: [0.91621622 0.93513514 0.94878706 0.98921833 0.96226415 0.95148248
|
|
0.98113208 0.98652291 0.95148248 0.89487871]
|
|
|
|
mean value: 0.9517119545421433
|
|
|
|
key: test_roc_auc
|
|
value: [0.92857143 0.9047619 0.85714286 0.95119048 0.97619048 0.92619048
|
|
0.95119048 0.87738095 0.92857143 0.82619048]
|
|
|
|
mean value: 0.9127380952380951
|
|
|
|
key: train_roc_auc
|
|
value: [0.91621622 0.93513514 0.94864865 0.98923278 0.96219122 0.95156931
|
|
0.98108108 0.98654461 0.95135135 0.89516129]
|
|
|
|
mean value: 0.9517131647776809
|
|
|
|
key: test_jcc
|
|
value: [0.85714286 0.80952381 0.71428571 0.90909091 0.95238095 0.86956522
|
|
0.9047619 0.77272727 0.86956522 0.66666667]
|
|
|
|
mean value: 0.8325710521362695
|
|
|
|
key: train_jcc
|
|
value: [0.83333333 0.87165775 0.8972973 0.9787234 0.92513369 0.91
|
|
0.96373057 0.97326203 0.91176471 0.79032258]
|
|
|
|
mean value: 0.9055225367297479
|
|
|
|
key: TN
|
|
value: 196
|
|
|
|
mean value: 196.0
|
|
|
|
key: FP
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: FN
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: TP
|
|
value: 180
|
|
|
|
mean value: 180.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.81
|
|
|
|
Accuracy on Blind test: 0.92
|
|
|
|
Running classifier: 18
|
|
Model_name: AdaBoost Classifier
|
|
Model func: AdaBoostClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', AdaBoostClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.18844795 0.16931653 0.1712935 0.17028832 0.17033982 0.16963387
|
|
0.16861916 0.16729665 0.17221189 0.16930819]
|
|
|
|
mean value: 0.17167558670043945
|
|
|
|
key: score_time
|
|
value: [0.0153811 0.01510787 0.01513457 0.01511717 0.01520443 0.01527405
|
|
0.01510739 0.01514196 0.01515222 0.01510954]
|
|
|
|
mean value: 0.015173029899597169
|
|
|
|
key: test_mcc
|
|
value: [1. 0.95346259 1. 0.85441771 0.95238095 0.95238095
|
|
0.90238095 0.75714286 1. 0.95238095]
|
|
|
|
mean value: 0.9324546963771422
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.97674419 1. 0.93023256 0.97560976 0.97560976
|
|
0.95 0.87804878 1. 0.97560976]
|
|
|
|
mean value: 0.9661854792966533
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.95454545 1. 0.90909091 1. 1.
|
|
0.95 0.85714286 1. 0.95238095]
|
|
|
|
mean value: 0.9623160173160172
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 0.95238095 0.95238095 0.95238095
|
|
0.95 0.9 1. 1. ]
|
|
|
|
mean value: 0.9707142857142858
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.97619048 1. 0.92682927 0.97560976 0.97560976
|
|
0.95121951 0.87804878 1. 0.97560976]
|
|
|
|
mean value: 0.9659117305458768
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.97619048 1. 0.92619048 0.97619048 0.97619048
|
|
0.95119048 0.87857143 1. 0.97619048]
|
|
|
|
mean value: 0.9660714285714287
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.95454545 1. 0.86956522 0.95238095 0.95238095
|
|
0.9047619 0.7826087 1. 0.95238095]
|
|
|
|
mean value: 0.9368624129493694
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 198
|
|
|
|
mean value: 198.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 200
|
|
|
|
mean value: 200.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.87
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
Running classifier: 19
|
|
Model_name: Bagging Classifier
|
|
Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
BaggingClassifier(n_jobs=10, oob_score=True,
|
|
random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.04259586 0.0584991 0.04867768 0.05368423 0.04338527 0.05776286
|
|
0.04904056 0.05466557 0.06025934 0.04896426]
|
|
|
|
mean value: 0.05175347328186035
|
|
|
|
key: score_time
|
|
value: [0.02193522 0.02411842 0.02880406 0.02554083 0.02484798 0.02451158
|
|
0.0243988 0.0265336 0.03643608 0.02784085]
|
|
|
|
mean value: 0.026496744155883788
|
|
|
|
key: test_mcc
|
|
value: [0.90889326 0.9047619 0.95238095 0.81975606 0.90692382 0.90238095
|
|
0.90649828 0.95238095 0.8547619 0.95238095]
|
|
|
|
mean value: 0.906111904278896
|
|
|
|
key: train_mcc
|
|
value: [0.98918919 0.989247 0.98921825 0.98927544 0.99462366 0.98921825
|
|
1. 0.9946235 1. 0.99462366]
|
|
|
|
mean value: 0.9930018943340556
|
|
|
|
key: test_fscore
|
|
value: [0.95 0.95238095 0.97560976 0.91304348 0.95 0.95238095
|
|
0.94736842 0.97560976 0.92682927 0.97560976]
|
|
|
|
mean value: 0.9518832340660772
|
|
|
|
key: train_fscore
|
|
value: [0.99459459 0.99462366 0.99459459 0.99456522 0.99730458 0.99459459
|
|
1. 0.99731903 1. 0.99730458]
|
|
|
|
mean value: 0.9964900856362098
|
|
|
|
key: test_precision
|
|
value: [1. 0.95238095 1. 0.84 1. 0.95238095
|
|
1. 0.95238095 0.9047619 0.95238095]
|
|
|
|
mean value: 0.9554285714285715
|
|
|
|
key: train_precision
|
|
value: [0.99459459 0.98930481 0.99459459 1. 0.99462366 0.99459459
|
|
1. 0.99465241 1. 1. ]
|
|
|
|
mean value: 0.9962364658949099
|
|
|
|
key: test_recall
|
|
value: [0.9047619 0.95238095 0.95238095 1. 0.9047619 0.95238095
|
|
0.9 1. 0.95 1. ]
|
|
|
|
mean value: 0.9516666666666665
|
|
|
|
key: train_recall
|
|
value: [0.99459459 1. 0.99459459 0.98918919 1. 0.99459459
|
|
1. 1. 1. 0.99462366]
|
|
|
|
mean value: 0.9967596628886952
|
|
|
|
key: test_accuracy
|
|
value: [0.95238095 0.95238095 0.97560976 0.90243902 0.95121951 0.95121951
|
|
0.95121951 0.97560976 0.92682927 0.97560976]
|
|
|
|
mean value: 0.951451800232288
|
|
|
|
key: train_accuracy
|
|
value: [0.99459459 0.99459459 0.99460916 0.99460916 0.99730458 0.99460916
|
|
1. 0.99730458 1. 0.99730458]
|
|
|
|
mean value: 0.9964930429081372
|
|
|
|
key: test_roc_auc
|
|
value: [0.95238095 0.95238095 0.97619048 0.9 0.95238095 0.95119048
|
|
0.95 0.97619048 0.92738095 0.97619048]
|
|
|
|
mean value: 0.9514285714285714
|
|
|
|
key: train_roc_auc
|
|
value: [0.99459459 0.99459459 0.99460913 0.99459459 0.99731183 0.99460913
|
|
1. 0.9972973 1. 0.99731183]
|
|
|
|
mean value: 0.9964922987503634
|
|
|
|
key: test_jcc
|
|
value: [0.9047619 0.90909091 0.95238095 0.84 0.9047619 0.90909091
|
|
0.9 0.95238095 0.86363636 0.95238095]
|
|
|
|
mean value: 0.9088484848484848
|
|
|
|
key: train_jcc
|
|
value: [0.98924731 0.98930481 0.98924731 0.98918919 0.99462366 0.98924731
|
|
1. 0.99465241 1. 0.99462366]
|
|
|
|
mean value: 0.9930135655752353
|
|
|
|
key: TN
|
|
value: 196
|
|
|
|
mean value: 196.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: TP
|
|
value: 196
|
|
|
|
mean value: 196.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.9
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 20
|
|
Model_name: Gaussian Process
|
|
Model func: GaussianProcessClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianProcessClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.12224102 0.10535049 0.1016345 0.0726614 0.08337069 0.09313035
|
|
0.13533998 0.11137009 0.06969023 0.06248879]
|
|
|
|
mean value: 0.09572775363922119
|
|
|
|
key: score_time
|
|
value: [0.02381134 0.01407385 0.02311468 0.01469684 0.02323651 0.01405168
|
|
0.02257681 0.028687 0.01425099 0.01401615]
|
|
|
|
mean value: 0.019251585006713867
|
|
|
|
key: test_mcc
|
|
value: [0.57735027 0.57207755 0.31666667 0.37171226 0.70714286 0.61152662
|
|
0.53206577 0.51320273 0.65871309 0.51190476]
|
|
|
|
mean value: 0.5372362570684607
|
|
|
|
key: train_mcc
|
|
value: [0.93039599 0.93039599 0.91978391 0.89264025 0.90846996 0.92500526
|
|
0.91926062 0.91947678 0.90901177 0.91423181]
|
|
|
|
mean value: 0.9168672355180574
|
|
|
|
key: test_fscore
|
|
value: [0.76923077 0.7804878 0.66666667 0.66666667 0.85714286 0.81818182
|
|
0.70588235 0.73684211 0.82051282 0.75 ]
|
|
|
|
mean value: 0.7571613861483981
|
|
|
|
key: train_fscore
|
|
value: [0.96418733 0.96418733 0.95867769 0.94505495 0.95367847 0.96153846
|
|
0.95934959 0.95912807 0.95342466 0.95628415]
|
|
|
|
mean value: 0.9575510691736385
|
|
|
|
key: test_precision
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
[0.83333333 0.8 0.66666667 0.72222222 0.85714286 0.7826087
|
|
0.85714286 0.77777778 0.84210526 0.75 ]
|
|
|
|
mean value: 0.7888999673095782
|
|
|
|
key: train_precision
|
|
value: [0.98314607 0.98314607 0.97752809 0.96089385 0.96153846 0.97765363
|
|
0.96721311 0.97237569 0.97206704 0.97222222]
|
|
|
|
mean value: 0.9727784238981283
|
|
|
|
key: test_recall
|
|
value: [0.71428571 0.76190476 0.66666667 0.61904762 0.85714286 0.85714286
|
|
0.6 0.7 0.8 0.75 ]
|
|
|
|
mean value: 0.7326190476190476
|
|
|
|
key: train_recall
|
|
value: [0.94594595 0.94594595 0.94054054 0.92972973 0.94594595 0.94594595
|
|
0.9516129 0.94623656 0.93548387 0.94086022]
|
|
|
|
mean value: 0.942824760244115
|
|
|
|
key: test_accuracy
|
|
value: [0.78571429 0.78571429 0.65853659 0.68292683 0.85365854 0.80487805
|
|
0.75609756 0.75609756 0.82926829 0.75609756]
|
|
|
|
mean value: 0.7668989547038327
|
|
|
|
key: train_accuracy
|
|
value: [0.96486486 0.96486486 0.95956873 0.94609164 0.9541779 0.96226415
|
|
0.95956873 0.95956873 0.9541779 0.95687332]
|
|
|
|
mean value: 0.9582020834851024
|
|
|
|
key: test_roc_auc
|
|
value: [0.78571429 0.78571429 0.65833333 0.68452381 0.85357143 0.80357143
|
|
0.75238095 0.7547619 0.82857143 0.75595238]
|
|
|
|
mean value: 0.7663095238095238
|
|
|
|
key: train_roc_auc
|
|
value: [0.96486486 0.96486486 0.95951758 0.94604766 0.95415577 0.96222028
|
|
0.95959024 0.95960477 0.95422842 0.95691659]
|
|
|
|
mean value: 0.9582011043301366
|
|
|
|
key: test_jcc
|
|
value: [0.625 0.64 0.5 0.5 0.75 0.69230769
|
|
0.54545455 0.58333333 0.69565217 0.6 ]
|
|
|
|
mean value: 0.6131747745008613
|
|
|
|
key: train_jcc
|
|
value: [0.93085106 0.93085106 0.92063492 0.89583333 0.91145833 0.92592593
|
|
0.921875 0.92146597 0.91099476 0.91623037]
|
|
|
|
mean value: 0.9186120740363529
|
|
|
|
key: TN
|
|
value: 165
|
|
|
|
mean value: 165.0
|
|
|
|
key: FP
|
|
value: 55
|
|
|
|
mean value: 55.0
|
|
|
|
key: FN
|
|
value: 41
|
|
|
|
mean value: 41.0
|
|
|
|
key: TP
|
|
value: 151
|
|
|
|
mean value: 151.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.34
|
|
|
|
Accuracy on Blind test: 0.7
|
|
|
|
Running classifier: 21
|
|
Model_name: Gradient Boosting
|
|
Model func: GradientBoostingClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GradientBoostingClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.67548418 0.65576053 0.66834664 0.65980339 0.65404487 0.61867952
|
|
0.65014267 0.65180707 0.64954734 0.66304302]
|
|
|
|
mean value: 0.6546659231185913
|
|
|
|
key: score_time
|
|
value: [0.0093596 0.00954175 0.00971174 0.00954843 0.00953078 0.00948763
|
|
0.01077414 0.00926828 0.00934982 0.00933218]
|
|
|
|
mean value: 0.009590435028076171
|
|
|
|
key: test_mcc
|
|
value: [1. 0.9047619 1. 0.86240942 0.95238095 0.95238095
|
|
0.90649828 0.95238095 0.90238095 0.95238095]
|
|
|
|
mean value: 0.9385574361533285
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.95238095 1. 0.93333333 0.97560976 0.97560976
|
|
0.94736842 0.97560976 0.95 0.97560976]
|
|
|
|
mean value: 0.968552173115716
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.95238095 1. 0.875 1. 1.
|
|
1. 0.95238095 0.95 0.95238095]
|
|
|
|
mean value: 0.9682142857142857
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 1. 1. 0.95238095 0.95238095
|
|
0.9 1. 0.95 1. ]
|
|
|
|
mean value: 0.9707142857142858
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.95238095 1. 0.92682927 0.97560976 0.97560976
|
|
0.95121951 0.97560976 0.95121951 0.97560976]
|
|
|
|
mean value: 0.9684088269454122
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.95238095 1. 0.925 0.97619048 0.97619048
|
|
0.95 0.97619048 0.95119048 0.97619048]
|
|
|
|
mean value: 0.9683333333333332
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.90909091 1. 0.875 0.95238095 0.95238095
|
|
0.9 0.95238095 0.9047619 0.95238095]
|
|
|
|
mean value: 0.9398376623376624
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 199
|
|
|
|
mean value: 199.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 200
|
|
|
|
mean value: 200.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.91
|
|
|
|
Accuracy on Blind test: 0.96
|
|
|
|
Running classifier: 22
|
|
Model_name: QDA
|
|
Model func: QuadraticDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', QuadraticDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.02539349 0.02820396 0.02839875 0.02784157 0.02765083 0.02783728
|
|
0.02735615 0.02679372 0.02673268 0.02854919]
|
|
|
|
mean value: 0.027475762367248534
|
|
|
|
key: score_time
|
|
value: [0.01260948 0.01275754 0.01296639 0.01299047 0.01315665 0.01306176
|
|
0.01306844 0.01392341 0.01316857 0.01312613]
|
|
|
|
mean value: 0.0130828857421875
|
|
|
|
key: test_mcc
|
|
value: [0.8660254 0.71428571 0.59335232 0.7633652 0.56190476 0.73786479
|
|
0.62325386 0.56086079 0.76500781 0.7098505 ]
|
|
|
|
mean value: 0.6895771149319809
|
|
|
|
key: train_mcc
|
|
value: [0.88737794 0.88252261 0.95692987 0.92023091 0.92993316 0.95215551
|
|
0.92586351 0.940826 0.95261005 0.91637608]
|
|
|
|
mean value: 0.9264825641731319
|
|
|
|
key: test_fscore
|
|
value: [0.92307692 0.85714286 0.81632653 0.88888889 0.7804878 0.875
|
|
0.81818182 0.76923077 0.88372093 0.84210526]
|
|
|
|
mean value: 0.8454161785402003
|
|
|
|
key: train_fscore
|
|
value: [0.93678161 0.93371758 0.97849462 0.96042216 0.96495957 0.97520661
|
|
0.96335079 0.9701897 0.97520661 0.95530726]
|
|
|
|
mean value: 0.9613636517371242
|
|
|
|
key: test_precision
|
|
value: [1. 0.85714286 0.71428571 0.83333333 0.8 0.77777778
|
|
0.75 0.78947368 0.82608696 0.88888889]
|
|
|
|
mean value: 0.8236989212160838
|
|
|
|
key: train_precision
|
|
value: [1. 1. 0.97326203 0.93814433 0.96236559 0.99438202
|
|
0.93877551 0.97814208 1. 0.99418605]
|
|
|
|
mean value: 0.9779257609070671
|
|
|
|
key: test_recall
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
[0.85714286 0.85714286 0.95238095 0.95238095 0.76190476 1.
|
|
0.9 0.75 0.95 0.8 ]
|
|
|
|
mean value: 0.878095238095238
|
|
|
|
key: train_recall
|
|
value: [0.88108108 0.87567568 0.98378378 0.98378378 0.96756757 0.95675676
|
|
0.98924731 0.96236559 0.9516129 0.91935484]
|
|
|
|
mean value: 0.947122929380994
|
|
|
|
key: test_accuracy
|
|
value: [0.92857143 0.85714286 0.7804878 0.87804878 0.7804878 0.85365854
|
|
0.80487805 0.7804878 0.87804878 0.85365854]
|
|
|
|
mean value: 0.8395470383275262
|
|
|
|
key: train_accuracy
|
|
value: [0.94054054 0.93783784 0.97843666 0.95956873 0.96495957 0.97574124
|
|
0.96226415 0.9703504 0.97574124 0.95687332]
|
|
|
|
mean value: 0.9622313688351424
|
|
|
|
key: test_roc_auc
|
|
value: [0.92857143 0.85714286 0.77619048 0.87619048 0.78095238 0.85
|
|
0.80714286 0.7797619 0.8797619 0.85238095]
|
|
|
|
mean value: 0.8388095238095239
|
|
|
|
key: train_roc_auc
|
|
value: [0.94054054 0.93783784 0.97845103 0.95963383 0.96496658 0.97569021
|
|
0.96219122 0.97037198 0.97580645 0.95697472]
|
|
|
|
mean value: 0.9622464399883756
|
|
|
|
key: test_jcc
|
|
value: [0.85714286 0.75 0.68965517 0.8 0.64 0.77777778
|
|
0.69230769 0.625 0.79166667 0.72727273]
|
|
|
|
mean value: 0.7350822893581515
|
|
|
|
key: train_jcc
|
|
value: [0.88108108 0.87567568 0.95789474 0.92385787 0.93229167 0.9516129
|
|
0.92929293 0.94210526 0.9516129 0.9144385 ]
|
|
|
|
mean value: 0.9259863529862067
|
|
|
|
key: TN
|
|
value: 171
|
|
|
|
mean value: 171.0
|
|
|
|
key: FP
|
|
value: 32
|
|
|
|
mean value: 32.0
|
|
|
|
key: FN
|
|
value: 35
|
|
|
|
mean value: 35.0
|
|
|
|
key: TP
|
|
value: 174
|
|
|
|
mean value: 174.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.13
|
|
|
|
Accuracy on Blind test: 0.61
|
|
|
|
Running classifier: 23
|
|
Model_name: Ridge Classifier
|
|
Model func: RidgeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02332497 0.03366351 0.03370738 0.03406048 0.03355503 0.03292322
|
|
0.03243351 0.03156877 0.05635238 0.03871083]
|
|
|
|
mean value: 0.03503000736236572
|
|
|
|
key: score_time
|
|
value: [0.02165461 0.02171969 0.02165103 0.02171063 0.02163982 0.02164435
|
|
0.02162695 0.02164865 0.02405787 0.02173615]
|
|
|
|
mean value: 0.02190897464752197
|
|
|
|
key: test_mcc
|
|
value: [1. 0.9047619 0.90692382 0.8547619 0.90238095 0.95238095
|
|
0.90649828 0.60952381 0.95238095 0.95238095]
|
|
|
|
mean value: 0.8941993531889617
|
|
|
|
key: train_mcc
|
|
value: [0.96762411 0.96228869 0.96771194 0.97306016 0.956873 0.97317407
|
|
0.96771006 0.9784365 0.96771006 0.97305937]
|
|
|
|
mean value: 0.9687647972123978
|
|
|
|
key: test_fscore
|
|
value: [1. 0.95238095 0.95 0.92682927 0.95238095 0.97560976
|
|
0.94736842 0.8 0.97560976 0.97560976]
|
|
|
|
mean value: 0.9455788862399903
|
|
|
|
key: train_fscore
|
|
value: [0.98387097 0.98123324 0.98387097 0.98652291 0.97837838 0.98659517
|
|
0.98395722 0.98924731 0.98395722 0.98659517]
|
|
|
|
mean value: 0.9844228567737392
|
|
|
|
key: test_precision
|
|
value: [1. 0.95238095 1. 0.95 0.95238095 1.
|
|
1. 0.8 0.95238095 0.95238095]
|
|
|
|
mean value: 0.955952380952381
|
|
|
|
key: train_precision
|
|
value: [0.97860963 0.97340426 0.97860963 0.98387097 0.97837838 0.9787234
|
|
0.9787234 0.98924731 0.9787234 0.98395722]
|
|
|
|
mean value: 0.9802247596621612
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 0.9047619 0.9047619 0.95238095 0.95238095
|
|
0.9 0.8 1. 1. ]
|
|
|
|
mean value: 0.9366666666666668
|
|
|
|
key: train_recall
|
|
value: [0.98918919 0.98918919 0.98918919 0.98918919 0.97837838 0.99459459
|
|
0.98924731 0.98924731 0.98924731 0.98924731]
|
|
|
|
mean value: 0.9886718977041558
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.95238095 0.95121951 0.92682927 0.95121951 0.97560976
|
|
0.95121951 0.80487805 0.97560976 0.97560976]
|
|
|
|
mean value: 0.9464576074332172
|
|
|
|
key: train_accuracy
|
|
value: [0.98378378 0.98108108 0.98382749 0.98652291 0.97843666 0.98652291
|
|
0.98382749 0.98921833 0.98382749 0.98652291]
|
|
|
|
mean value: 0.9843571064325781
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.95238095 0.95238095 0.92738095 0.95119048 0.97619048
|
|
0.95 0.8047619 0.97619048 0.97619048]
|
|
|
|
mean value: 0.9466666666666667
|
|
|
|
key: train_roc_auc
|
|
value: [0.98378378 0.98108108 0.98384191 0.98653008 0.9784365 0.98654461
|
|
0.98381285 0.98921825 0.98381285 0.98651555]
|
|
|
|
mean value: 0.9843577448416159
|
|
|
|
key: test_jcc
|
|
value: [1. 0.90909091 0.9047619 0.86363636 0.90909091 0.95238095
|
|
0.9 0.66666667 0.95238095 0.95238095]
|
|
|
|
mean value: 0.9010389610389611
|
|
|
|
key: train_jcc
|
|
value: [0.96825397 0.96315789 0.96825397 0.97340426 0.95767196 0.97354497
|
|
0.96842105 0.9787234 0.96842105 0.97354497]
|
|
|
|
mean value: 0.969339750084431
|
|
|
|
key: TN
|
|
value: 197
|
|
|
|
mean value: 197.0
|
|
|
|
key: FP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: FN
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: TP
|
|
value: 193
|
|
|
|
mean value: 193.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.86
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
Running classifier: 24
|
|
Model_name: Ridge ClassifierCV
|
|
Model func: RidgeClassifierCV(cv=10)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifierCV(cv=10))])
|
|
|
|
key: fit_time
|
|
value: [0.24888968 0.24928164 0.25829434 0.29429793 0.29187274 0.25702906
|
|
0.25375938 0.25846148 0.25591302 0.25412965]
|
|
|
|
mean value: 0.26219289302825927
|
|
|
|
key: score_time
|
|
value: [0.02372575 0.02388597 0.02393842 0.0240922 0.02259684 0.02371073
|
|
0.02040315 0.02021837 0.02421808 0.02299547]
|
|
|
|
mean value: 0.022978496551513673
|
|
|
|
key: test_mcc
|
|
value: [1. 0.9047619 0.90692382 0.8547619 0.90238095 0.95238095
|
|
0.90649828 0.65952381 0.95238095 0.95238095]
|
|
|
|
mean value: 0.8991993531889616
|
|
|
|
key: train_mcc
|
|
value: [0.96762411 0.96228869 0.96771194 0.97306016 0.956873 0.97317407
|
|
0.96771006 0.9784365 0.96771006 0.97305937]
|
|
|
|
mean value: 0.9687647972123978
|
|
|
|
key: test_fscore
|
|
value: [1. 0.95238095 0.95 0.92682927 0.95238095 0.97560976
|
|
0.94736842 0.82926829 0.97560976 0.97560976]
|
|
|
|
mean value: 0.9485057155082831
|
|
|
|
key: train_fscore
|
|
value: [0.98387097 0.98123324 0.98387097 0.98652291 0.97837838 0.98659517
|
|
0.98395722 0.98924731 0.98395722 0.98659517]
|
|
|
|
mean value: 0.9844228567737392
|
|
|
|
key: test_precision
|
|
value: [1. 0.95238095 1. 0.95 0.95238095 1.
|
|
1. 0.80952381 0.95238095 0.95238095]
|
|
|
|
mean value: 0.9569047619047619
|
|
|
|
key: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:206: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
smnc_CV['Data_source'] = 'CV'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:207: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
smnc_CV['Resampling'] = rs_smnc
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:212: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
smnc_BT['Data_source'] = 'BT'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:213: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
smnc_BT['Resampling'] = rs_smnc
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
train_precision
|
|
value: [0.97860963 0.97340426 0.97860963 0.98387097 0.97837838 0.9787234
|
|
0.9787234 0.98924731 0.9787234 0.98395722]
|
|
|
|
mean value: 0.9802247596621612
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 0.9047619 0.9047619 0.95238095 0.95238095
|
|
0.9 0.85 1. 1. ]
|
|
|
|
mean value: 0.9416666666666667
|
|
|
|
key: train_recall
|
|
value: [0.98918919 0.98918919 0.98918919 0.98918919 0.97837838 0.99459459
|
|
0.98924731 0.98924731 0.98924731 0.98924731]
|
|
|
|
mean value: 0.9886718977041558
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.95238095 0.95121951 0.92682927 0.95121951 0.97560976
|
|
0.95121951 0.82926829 0.97560976 0.97560976]
|
|
|
|
mean value: 0.948896631823461
|
|
|
|
key: train_accuracy
|
|
value: [0.98378378 0.98108108 0.98382749 0.98652291 0.97843666 0.98652291
|
|
0.98382749 0.98921833 0.98382749 0.98652291]
|
|
|
|
mean value: 0.9843571064325781
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.95238095 0.95238095 0.92738095 0.95119048 0.97619048
|
|
0.95 0.8297619 0.97619048 0.97619048]
|
|
|
|
mean value: 0.9491666666666665
|
|
|
|
key: train_roc_auc
|
|
value: [0.98378378 0.98108108 0.98384191 0.98653008 0.9784365 0.98654461
|
|
0.98381285 0.98921825 0.98381285 0.98651555]
|
|
|
|
mean value: 0.9843577448416159
|
|
|
|
key: test_jcc
|
|
value: [1. 0.90909091 0.9047619 0.86363636 0.90909091 0.95238095
|
|
0.9 0.70833333 0.95238095 0.95238095]
|
|
|
|
mean value: 0.9052056277056277
|
|
|
|
key: train_jcc
|
|
value: [0.96825397 0.96315789 0.96825397 0.97340426 0.95767196 0.97354497
|
|
0.96842105 0.9787234 0.96842105 0.97354497]
|
|
|
|
mean value: 0.969339750084431
|
|
|
|
key: TN
|
|
value: 197
|
|
|
|
mean value: 197.0
|
|
|
|
key: FP
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: FN
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: TP
|
|
value: 194
|
|
|
|
mean value: 194.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.86
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
PASS: sorting df by score that is mapped onto the order I want
|
|
|
|
==============================================================
|
|
|
|
Running several classification models (n): 24
|
|
List of models:
|
|
('Logistic Regression', LogisticRegression(random_state=42))
|
|
('Logistic RegressionCV', LogisticRegressionCV(random_state=42))
|
|
('Gaussian NB', GaussianNB())
|
|
('Naive Bayes', BernoulliNB())
|
|
('K-Nearest Neighbors', KNeighborsClassifier())
|
|
('SVC', SVC(random_state=42))
|
|
('MLP', MLPClassifier(max_iter=500, random_state=42))
|
|
('Decision Tree', DecisionTreeClassifier(random_state=42))
|
|
('Extra Trees', ExtraTreesClassifier(random_state=42))
|
|
('Extra Tree', ExtraTreeClassifier(random_state=42))
|
|
('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42))
|
|
('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42))
|
|
('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0))
|
|
('LDA', LinearDiscriminantAnalysis())
|
|
('Multinomial', MultinomialNB())
|
|
('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42))
|
|
('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42))
|
|
('AdaBoost Classifier', AdaBoostClassifier(random_state=42))
|
|
('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))
|
|
('Gaussian Process', GaussianProcessClassifier(random_state=42))
|
|
('Gradient Boosting', GradientBoostingClassifier(random_state=42))
|
|
('QDA', QuadraticDiscriminantAnalysis())
|
|
('Ridge Classifier', RidgeClassifier(random_state=42))
|
|
('Ridge ClassifierCV', RidgeClassifierCV(cv=10))
|
|
|
|
================================================================
|
|
|
|
|
|
Running classifier: 1
|
|
Model_name: Logistic Regression
|
|
Model func: LogisticRegression(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegression(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.03052306 0.03408241 0.03512359 0.03415132 0.03537679 0.03478885
|
|
0.03490472 0.03314066 0.03462148 0.06600475]
|
|
|
|
mean value: 0.037271761894226076
|
|
|
|
key: score_time
|
|
value: [0.01202464 0.01207113 0.01203728 0.01235628 0.01250768 0.01239419
|
|
0.01239467 0.01211047 0.01261497 0.01401401]
|
|
|
|
mean value: 0.012452530860900878
|
|
|
|
key: test_mcc
|
|
value: [0.95346259 0.90889326 0.7565654 0.76500781 0.95238095 0.86333169
|
|
0.80817439 0.60952381 0.8547619 0.8047619 ]
|
|
|
|
mean value: 0.8276863714013631
|
|
|
|
key: train_mcc
|
|
value: [0.91925472 0.94055428 0.90316864 0.91379661 0.89789222 0.92473841
|
|
0.92458368 0.91396351 0.91947678 0.91947678]
|
|
|
|
mean value: 0.91769056480883
|
|
|
|
key: test_fscore
|
|
value: [0.97674419 0.95 0.88372093 0.87179487 0.97560976 0.92307692
|
|
0.89473684 0.8 0.92682927 0.9 ]
|
|
|
|
mean value: 0.9102512777646373
|
|
|
|
key: train_fscore
|
|
value: [0.95890411 0.9701897 0.95081967 0.95652174 0.94794521 0.96174863
|
|
0.96216216 0.95652174 0.95912807 0.95912807]
|
|
|
|
mean value: 0.9583069094189665
|
|
|
|
key: test_precision
|
|
value: [0.95454545 1. 0.86363636 0.94444444 1. 1.
|
|
0.94444444 0.8 0.9047619 0.9 ]
|
|
|
|
mean value: 0.9311832611832612
|
|
|
|
key: train_precision
|
|
value: [0.97222222 0.97282609 0.96132597 0.96174863 0.96111111 0.97237569
|
|
0.9673913 0.96703297 0.97237569 0.97237569]
|
|
|
|
mean value: 0.968078536422446
|
|
|
|
key: test_recall
|
|
value: [1. 0.9047619 0.9047619 0.80952381 0.95238095 0.85714286
|
|
0.85 0.8 0.95 0.9 ]
|
|
|
|
mean value: 0.8928571428571429
|
|
|
|
key: train_recall
|
|
value: [0.94594595 0.96756757 0.94054054 0.95135135 0.93513514 0.95135135
|
|
0.95698925 0.94623656 0.94623656 0.94623656]
|
|
|
|
mean value: 0.9487590816623076
|
|
|
|
key: test_accuracy
|
|
value: [0.97619048 0.95238095 0.87804878 0.87804878 0.97560976 0.92682927
|
|
0.90243902 0.80487805 0.92682927 0.90243902]
|
|
|
|
mean value: 0.9123693379790941
|
|
|
|
key: train_accuracy
|
|
value: [0.95945946 0.97027027 0.95148248 0.95687332 0.94878706 0.96226415
|
|
0.96226415 0.95687332 0.95956873 0.95956873]
|
|
|
|
mean value: 0.9587411670430537
|
|
|
|
key: test_roc_auc
|
|
value: [0.97619048 0.95238095 0.87738095 0.8797619 0.97619048 0.92857143
|
|
0.90119048 0.8047619 0.92738095 0.90238095]
|
|
|
|
mean value: 0.9126190476190474
|
|
|
|
key: train_roc_auc
|
|
value: [0.95945946 0.97027027 0.95145307 0.95685847 0.94875036 0.96223482
|
|
0.96227841 0.95690206 0.95960477 0.95960477]
|
|
|
|
mean value: 0.9587416448706773
|
|
|
|
key: test_jcc
|
|
value: [0.95454545 0.9047619 0.79166667 0.77272727 0.95238095 0.85714286
|
|
0.80952381 0.66666667 0.86363636 0.81818182]
|
|
|
|
mean value: 0.8391233766233765
|
|
|
|
key: train_jcc
|
|
value: [0.92105263 0.94210526 0.90625 0.91666667 0.90104167 0.92631579
|
|
0.92708333 0.91666667 0.92146597 0.92146597]
|
|
|
|
mean value: 0.9200113954716634
|
|
|
|
key: TN
|
|
value: 192
|
|
|
|
mean value: 192.0
|
|
|
|
key: FP
|
|
value: 22
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
|
|
mean value: 22.0
|
|
|
|
key: FN
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: TP
|
|
value: 184
|
|
|
|
mean value: 184.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.77
|
|
|
|
Accuracy on Blind test: 0.9
|
|
|
|
Running classifier: 2
|
|
Model_name: Logistic RegressionCV
|
|
Model func: LogisticRegressionCV(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegressionCV(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.8054328 0.84636688 1.01926517 1.05817366 0.7309134 0.71366811
|
|
0.91611481 0.73690271 0.72525883 0.915658 ]
|
|
|
|
mean value: 0.8467754364013672
|
|
|
|
key: score_time
|
|
value: [0.01364899 0.01264262 0.01263046 0.01277018 0.01270843 0.01261044
|
|
0.01264334 0.01270008 0.01384187 0.01266003]
|
|
|
|
mean value: 0.012885642051696778
|
|
|
|
key: test_mcc
|
|
value: [0.95346259 1. 0.90692382 0.95238095 1. 0.90692382
|
|
0.90649828 0.70714286 0.95238095 0.90238095]
|
|
|
|
mean value: 0.9188094230834079
|
|
|
|
key: train_mcc
|
|
value: [1. 0.98379816 1. 0.98384191 0.98921825 0.98384191
|
|
1. 0.9946235 0.98384144 1. ]
|
|
|
|
mean value: 0.9919165157084897
|
|
|
|
key: test_fscore
|
|
value: [0.97674419 1. 0.95 0.97560976 1. 0.95
|
|
0.94736842 0.85 0.97560976 0.95 ]
|
|
|
|
mean value: 0.9575332119294264
|
|
|
|
key: train_fscore
|
|
value: [1. 0.99191375 1. 0.99191375 0.99459459 0.99191375
|
|
1. 0.99731903 0.9919571 1. ]
|
|
|
|
mean value: 0.9959611973896966
|
|
|
|
key: test_precision
|
|
value: [0.95454545 1. 1. 1. 1. 1.
|
|
1. 0.85 0.95238095 0.95 ]
|
|
|
|
mean value: 0.9706926406926406
|
|
|
|
key: train_precision
|
|
value: [1. 0.98924731 1. 0.98924731 0.99459459 0.98924731
|
|
1. 0.99465241 0.98930481 1. ]
|
|
|
|
mean value: 0.9946293749329802
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.9047619 0.95238095 1. 0.9047619
|
|
0.9 0.85 1. 0.95 ]
|
|
|
|
mean value: 0.9461904761904762
|
|
|
|
key: train_recall
|
|
value: [1. 0.99459459 1. 0.99459459 0.99459459 0.99459459
|
|
1. 1. 0.99462366 1. ]
|
|
|
|
mean value: 0.9973002034292356
|
|
|
|
key: test_accuracy
|
|
value: [0.97619048 1. 0.95121951 0.97560976 1. 0.95121951
|
|
0.95121951 0.85365854 0.97560976 0.95121951]
|
|
|
|
mean value: 0.9585946573751452
|
|
|
|
key: train_accuracy
|
|
value: [1. 0.99189189 1. 0.99191375 0.99460916 0.99191375
|
|
1. 0.99730458 0.99191375 1. ]
|
|
|
|
mean value: 0.9959546878414802
|
|
|
|
key: test_roc_auc
|
|
value: [0.97619048 1. 0.95238095 0.97619048 1. 0.95238095
|
|
0.95 0.85357143 0.97619048 0.95119048]
|
|
|
|
mean value: 0.9588095238095237
|
|
|
|
key: train_roc_auc
|
|
value: [1. 0.99189189 1. 0.99192095 0.99460913 0.99192095
|
|
1. 0.9972973 0.99190642 1. ]
|
|
|
|
mean value: 0.995954664341761
|
|
|
|
key: test_jcc
|
|
value: [0.95454545 1. 0.9047619 0.95238095 1. 0.9047619
|
|
0.9 0.73913043 0.95238095 0.9047619 ]
|
|
|
|
mean value: 0.9212723508375683
|
|
|
|
key: train_jcc
|
|
value: [1. 0.98395722 1. 0.98395722 0.98924731 0.98395722
|
|
1. 0.99465241 0.98404255 1. ]
|
|
|
|
mean value: 0.991981392919057
|
|
|
|
key: TN
|
|
value: 200
|
|
|
|
mean value: 200.0
|
|
|
|
key: FP
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: FN
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: TP
|
|
value: 194
|
|
|
|
mean value: 194.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.86
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
Running classifier: 3
|
|
Model_name: Gaussian NB
|
|
Model func: GaussianNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianNB())])
|
|
|
|
key: fit_time
|
|
value: [0.01342845 0.01186204 0.01067162 0.00947356 0.00962114 0.00934386
|
|
0.00946069 0.00932765 0.00938511 0.00942278]
|
|
|
|
mean value: 0.010199689865112304
|
|
|
|
key: score_time
|
|
value: [0.01247454 0.00964475 0.00987816 0.00903606 0.00877929 0.00873184
|
|
0.00876999 0.0087111 0.00880551 0.00896764]
|
|
|
|
mean value: 0.009379887580871582
|
|
|
|
key: test_mcc
|
|
value: [0.42857143 0.47673129 0.23018043 0.26730386 0.57570364 0.56086079
|
|
0.46428571 0.26904762 0.62325386 0.56836003]
|
|
|
|
mean value: 0.44642986720816974
|
|
|
|
key: train_mcc
|
|
value: [0.50978658 0.47746734 0.47521493 0.49836503 0.45863438 0.50506482
|
|
0.52229262 0.51472381 0.45731256 0.498771 ]
|
|
|
|
mean value: 0.491763305975787
|
|
|
|
key: test_fscore
|
|
value: [0.71428571 0.74418605 0.68 0.65116279 0.80851064 0.79069767
|
|
0.73170732 0.63414634 0.81818182 0.79069767]
|
|
|
|
mean value: 0.7363576015348501
|
|
|
|
key: train_fscore
|
|
value: [0.74366197 0.75621891 0.75 0.76262626 0.74168798 0.76691729
|
|
0.77120823 0.77192982 0.7403599 0.765 ]
|
|
|
|
mean value: 0.7569610360657331
|
|
|
|
key: test_precision
|
|
value: [0.71428571 0.72727273 0.5862069 0.63636364 0.73076923 0.77272727
|
|
0.71428571 0.61904762 0.75 0.73913043]
|
|
|
|
mean value: 0.6990089246086246
|
|
|
|
key: train_precision
|
|
value: [0.77647059 0.70046083 0.71014493 0.71563981 0.7038835 0.71495327
|
|
0.73891626 0.72300469 0.70935961 0.71495327]
|
|
|
|
mean value: 0.7207786749797507
|
|
|
|
key: test_recall
|
|
value: [0.71428571 0.76190476 0.80952381 0.66666667 0.9047619 0.80952381
|
|
0.75 0.65 0.9 0.85 ]
|
|
|
|
mean value: 0.7816666666666666
|
|
|
|
key: train_recall
|
|
value: [0.71351351 0.82162162 0.79459459 0.81621622 0.78378378 0.82702703
|
|
0.80645161 0.82795699 0.77419355 0.82258065]
|
|
|
|
mean value: 0.7987939552455681
|
|
|
|
key: test_accuracy
|
|
value: [0.71428571 0.73809524 0.6097561 0.63414634 0.7804878 0.7804878
|
|
0.73170732 0.63414634 0.80487805 0.7804878 ]
|
|
|
|
mean value: 0.7208478513356562
|
|
|
|
key: train_accuracy
|
|
value: [0.75405405 0.73513514 0.73584906 0.74663073 0.7277628 0.74932615
|
|
0.76010782 0.75471698 0.7277628 0.74663073]
|
|
|
|
mean value: 0.7437976251183799
|
|
|
|
key: test_roc_auc
|
|
value: [0.71428571 0.73809524 0.6047619 0.63333333 0.77738095 0.7797619
|
|
0.73214286 0.63452381 0.80714286 0.78214286]
|
|
|
|
mean value: 0.7203571428571429
|
|
|
|
key: train_roc_auc
|
|
value: [0.75405405 0.73513514 0.73600697 0.74681779 0.7279134 0.74953502
|
|
0.75998256 0.75451904 0.72763731 0.74642546]
|
|
|
|
mean value: 0.7438026736413834
|
|
|
|
key: test_jcc
|
|
value: [0.55555556 0.59259259 0.51515152 0.48275862 0.67857143 0.65384615
|
|
0.57692308 0.46428571 0.69230769 0.65384615]
|
|
|
|
mean value: 0.5865838503769538
|
|
|
|
key: train_jcc
|
|
value: [0.59192825 0.608 0.6 0.61632653 0.58943089 0.62195122
|
|
0.62761506 0.62857143 0.5877551 0.6194332 ]
|
|
|
|
mean value: 0.6091011687308778
|
|
|
|
key: TN
|
|
value: 136
|
|
|
|
mean value: 136.0
|
|
|
|
key: FP
|
|
value: 45
|
|
|
|
mean value: 45.0
|
|
|
|
key: FN
|
|
value: 70
|
|
|
|
mean value: 70.0
|
|
|
|
key: TP
|
|
value: 161
|
|
|
|
mean value: 161.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.38
|
|
|
|
Accuracy on Blind test: 0.72
|
|
|
|
Running classifier: 4
|
|
Model_name: Naive Bayes
|
|
Model func: BernoulliNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', BernoulliNB())])
|
|
|
|
key: fit_time
|
|
value: [0.01096463 0.00961113 0.00975704 0.00960565 0.00958586 0.00966382
|
|
0.0096786 0.0097096 0.00990868 0.0098772 ]
|
|
|
|
mean value: 0.009836220741271972
|
|
|
|
key: score_time
|
|
value: [0.00957084 0.0090189 0.00870156 0.00873661 0.00881624 0.00886679
|
|
0.00888467 0.00873137 0.00875568 0.00878763]
|
|
|
|
mean value: 0.008887028694152832
|
|
|
|
key: test_mcc
|
|
value: [ 0. 0.30304576 0.41327851 0.29113032 0.45524446 0.63994524
|
|
0.62048368 -0.04029115 0.18976803 0.41428571]
|
|
|
|
mean value: 0.32868905707398166
|
|
|
|
key: train_mcc
|
|
value: [0.46108397 0.40209243 0.41598338 0.41863013 0.42311032 0.36261286
|
|
0.43869287 0.425884 0.46299486 0.42319846]
|
|
|
|
mean value: 0.4234283285039922
|
|
|
|
key: test_fscore
|
|
value: [0.43243243 0.57142857 0.60606061 0.57142857 0.64705882 0.77777778
|
|
0.70967742 0.32258065 0.4137931 0.7 ]
|
|
|
|
mean value: 0.5752237950621776
|
|
|
|
key: train_fscore
|
|
value: [0.66666667 0.60338983 0.65420561 0.64984227 0.64308682 0.6038961
|
|
0.65822785 0.65408805 0.66878981 0.67069486]
|
|
|
|
mean value: 0.647288786794278
|
|
|
|
key: test_precision
|
|
value: [0.5 0.71428571 0.83333333 0.71428571 0.84615385 0.93333333
|
|
1. 0.45454545 0.66666667 0.7 ]
|
|
|
|
mean value: 0.7362604062604063
|
|
|
|
key: train_precision
|
|
value: [0.81889764 0.80909091 0.77205882 0.78030303 0.79365079 0.75609756
|
|
0.8 0.78787879 0.8203125 0.76551724]
|
|
|
|
mean value: 0.7903807284603128
|
|
|
|
key: test_recall
|
|
value: [0.38095238 0.47619048 0.47619048 0.47619048 0.52380952 0.66666667
|
|
0.55 0.25 0.3 0.7 ]
|
|
|
|
mean value: 0.48
|
|
|
|
key: train_recall
|
|
value: [0.56216216 0.48108108 0.56756757 0.55675676 0.54054054 0.5027027
|
|
0.55913978 0.55913978 0.56451613 0.59677419]
|
|
|
|
mean value: 0.5490380703283929
|
|
|
|
key: test_accuracy
|
|
value: [0.5 0.64285714 0.68292683 0.63414634 0.70731707 0.80487805
|
|
0.7804878 0.48780488 0.58536585 0.70731707]
|
|
|
|
mean value: 0.6533101045296166
|
|
|
|
key: train_accuracy
|
|
value: [0.71891892 0.68378378 0.70080863 0.70080863 0.70080863 0.67115903
|
|
0.70889488 0.70350404 0.71967655 0.70619946]
|
|
|
|
mean value: 0.7014562540977635
|
|
|
|
key: test_roc_auc
|
|
value: [0.5 0.64285714 0.68809524 0.63809524 0.71190476 0.80833333
|
|
0.775 0.48214286 0.57857143 0.70714286]
|
|
|
|
mean value: 0.6532142857142857
|
|
|
|
key: train_roc_auc
|
|
value: [0.71891892 0.68378378 0.70045045 0.70042139 0.7003778 0.67070619
|
|
0.70929962 0.70389422 0.7200959 0.7064952 ]
|
|
|
|
mean value: 0.7014443475733797
|
|
|
|
key: test_jcc
|
|
value: [0.27586207 0.4 0.43478261 0.4 0.47826087 0.63636364
|
|
0.55 0.19230769 0.26086957 0.53846154]
|
|
|
|
mean value: 0.41669079795766456
|
|
|
|
key: train_jcc
|
|
value: [0.5 0.43203883 0.48611111 0.48130841 0.47393365 0.43255814
|
|
0.49056604 0.48598131 0.50239234 0.50454545]
|
|
|
|
mean value: 0.478943529129163
|
|
|
|
key: TN
|
|
value: 170
|
|
|
|
mean value: 170.0
|
|
|
|
key: FP
|
|
value: 107
|
|
|
|
mean value: 107.0
|
|
|
|
key: FN
|
|
value: 36
|
|
|
|
mean value: 36.0
|
|
|
|
key: TP
|
|
value: 99
|
|
|
|
mean value: 99.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.25
|
|
|
|
Accuracy on Blind test: 0.61
|
|
|
|
Running classifier: 5
|
|
Model_name: K-Nearest Neighbors
|
|
Model func: KNeighborsClassifier()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', KNeighborsClassifier())])
|
|
|
|
key: fit_time
|
|
value: [0.00891542 0.00999284 0.00998735 0.00997591 0.00999928 0.0100472
|
|
0.00998139 0.0099628 0.00964546 0.00991631]
|
|
|
|
mean value: 0.009842395782470703
|
|
|
|
key: score_time
|
|
value: [0.01492429 0.01553607 0.01342964 0.01545405 0.01206732 0.01232624
|
|
0.01210999 0.01499987 0.01185274 0.01187205]
|
|
|
|
mean value: 0.013457226753234863
|
|
|
|
key: test_mcc
|
|
value: [0.43052839 0.38490018 0.17142857 0.37171226 0.6133669 0.56086079
|
|
0.51320273 0.07159925 0.57570364 0.37309549]
|
|
|
|
mean value: 0.40663981862522663
|
|
|
|
key: train_mcc
|
|
value: [0.57917445 0.56736651 0.59587155 0.60158874 0.5713147 0.5850851
|
|
0.5912125 0.63077465 0.59830079 0.59830079]
|
|
|
|
mean value: 0.59189897754378
|
|
|
|
key: test_fscore
|
|
value: [0.7 0.66666667 0.58536585 0.66666667 0.8 0.79069767
|
|
0.73684211 0.51282051 0.74285714 0.62857143]
|
|
|
|
mean value: 0.6830488050922717
|
|
|
|
key: train_fscore
|
|
value: [0.7696793 0.76521739 0.79452055 0.79558011 0.77272727 0.7890411
|
|
0.79005525 0.8056338 0.78873239 0.78873239]
|
|
|
|
mean value: 0.78599195588241
|
|
|
|
key: test_precision
|
|
value: [0.73684211 0.72222222 0.6 0.72222222 0.84210526 0.77272727
|
|
0.77777778 0.52631579 0.86666667 0.73333333]
|
|
|
|
mean value: 0.7300212652844231
|
|
|
|
key: train_precision
|
|
value: [0.83544304 0.825 0.80555556 0.81355932 0.81437126 0.8
|
|
0.8125 0.84615385 0.82840237 0.82840237]
|
|
|
|
mean value: 0.8209387752930825
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.61904762 0.57142857 0.61904762 0.76190476 0.80952381
|
|
0.7 0.5 0.65 0.55 ]
|
|
|
|
mean value: 0.6447619047619048
|
|
|
|
key: train_recall
|
|
value: [0.71351351 0.71351351 0.78378378 0.77837838 0.73513514 0.77837838
|
|
0.7688172 0.7688172 0.75268817 0.75268817]
|
|
|
|
mean value: 0.7545713455390876
|
|
|
|
key: test_accuracy
|
|
value: [0.71428571 0.69047619 0.58536585 0.68292683 0.80487805 0.7804878
|
|
0.75609756 0.53658537 0.7804878 0.68292683]
|
|
|
|
mean value: 0.7014518002322879
|
|
|
|
key: train_accuracy
|
|
value: [0.78648649 0.78108108 0.79784367 0.80053908 0.78436658 0.79245283
|
|
0.79514825 0.81401617 0.79784367 0.79784367]
|
|
|
|
mean value: 0.7947621475923363
|
|
|
|
key: test_roc_auc
|
|
value: [0.71428571 0.69047619 0.58571429 0.68452381 0.80595238 0.7797619
|
|
0.7547619 0.53571429 0.77738095 0.6797619 ]
|
|
|
|
mean value: 0.7008333333333333
|
|
|
|
key: train_roc_auc
|
|
value: [0.78648649 0.78108108 0.79780587 0.80047951 0.78423423 0.792415
|
|
0.79521941 0.81413833 0.79796571 0.79796571]
|
|
|
|
mean value: 0.7947791339726823
|
|
|
|
key: test_jcc
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
[0.53846154 0.5 0.4137931 0.5 0.66666667 0.65384615
|
|
0.58333333 0.34482759 0.59090909 0.45833333]
|
|
|
|
mean value: 0.5250170806205288
|
|
|
|
key: train_jcc
|
|
value: [0.62559242 0.61971831 0.65909091 0.66055046 0.62962963 0.65158371
|
|
0.65296804 0.6745283 0.65116279 0.65116279]
|
|
|
|
mean value: 0.6475987354575963
|
|
|
|
key: TN
|
|
value: 156
|
|
|
|
mean value: 156.0
|
|
|
|
key: FP
|
|
value: 73
|
|
|
|
mean value: 73.0
|
|
|
|
key: FN
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: TP
|
|
value: 133
|
|
|
|
mean value: 133.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.31
|
|
|
|
Accuracy on Blind test: 0.65
|
|
|
|
Running classifier: 6
|
|
Model_name: SVC
|
|
Model func: SVC(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SVC(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.0182035 0.01881266 0.01938367 0.01898909 0.01740909 0.0181179
|
|
0.01810026 0.01760936 0.0182271 0.01885557]
|
|
|
|
mean value: 0.018370819091796876
|
|
|
|
key: score_time
|
|
value: [0.01155233 0.01083374 0.01057696 0.01137233 0.01090479 0.01087809
|
|
0.01130199 0.01125836 0.01051879 0.01087332]
|
|
|
|
mean value: 0.011007070541381836
|
|
|
|
key: test_mcc
|
|
value: [0.62187434 0.71754731 0.60952381 0.66668392 0.8547619 0.65871309
|
|
0.7633652 0.51190476 0.70714286 0.65871309]
|
|
|
|
mean value: 0.6770230281474807
|
|
|
|
key: train_mcc
|
|
value: [0.76946971 0.80658462 0.78453492 0.77396856 0.76871644 0.74663605
|
|
0.77898224 0.80112842 0.78512038 0.78553828]
|
|
|
|
mean value: 0.780067963437501
|
|
|
|
key: test_fscore
|
|
value: [0.81818182 0.85 0.80952381 0.82051282 0.92682927 0.8372093
|
|
0.86486486 0.75 0.85 0.82051282]
|
|
|
|
mean value: 0.8347634704214398
|
|
|
|
key: train_fscore
|
|
value: [0.87955182 0.9 0.89071038 0.88461538 0.8815427 0.87262873
|
|
0.89008043 0.89863014 0.89010989 0.88950276]
|
|
|
|
mean value: 0.8877372232350673
|
|
|
|
key: test_precision
|
|
value: [0.7826087 0.89473684 0.80952381 0.88888889 0.95 0.81818182
|
|
0.94117647 0.75 0.85 0.84210526]
|
|
|
|
mean value: 0.8527221788098082
|
|
|
|
key: train_precision
|
|
value: [0.9127907 0.92571429 0.90055249 0.89944134 0.8988764 0.875
|
|
0.88770053 0.91620112 0.91011236 0.91477273]
|
|
|
|
mean value: 0.9041161953754138
|
|
|
|
key: test_recall
|
|
value: [0.85714286 0.80952381 0.80952381 0.76190476 0.9047619 0.85714286
|
|
0.8 0.75 0.85 0.8 ]
|
|
|
|
mean value: 0.82
|
|
|
|
key: train_recall
|
|
value: [0.84864865 0.87567568 0.88108108 0.87027027 0.86486486 0.87027027
|
|
0.89247312 0.88172043 0.87096774 0.8655914 ]
|
|
|
|
mean value: 0.8721563498982853
|
|
|
|
key: test_accuracy
|
|
value: [0.80952381 0.85714286 0.80487805 0.82926829 0.92682927 0.82926829
|
|
0.87804878 0.75609756 0.85365854 0.82926829]
|
|
|
|
mean value: 0.83739837398374
|
|
|
|
key: train_accuracy
|
|
value: [0.88378378 0.9027027 0.89218329 0.88679245 0.88409704 0.87331536
|
|
0.88948787 0.90026954 0.89218329 0.89218329]
|
|
|
|
mean value: 0.8896998615866542
|
|
|
|
key: test_roc_auc
|
|
value: [0.80952381 0.85714286 0.8047619 0.83095238 0.92738095 0.82857143
|
|
0.87619048 0.75595238 0.85357143 0.82857143]
|
|
|
|
mean value: 0.8372619047619047
|
|
|
|
key: train_roc_auc
|
|
value: [0.88378378 0.9027027 0.89215344 0.88674804 0.88404534 0.87330718
|
|
0.8894798 0.90031967 0.89224063 0.89225516]
|
|
|
|
mean value: 0.8897035745422844
|
|
|
|
key: test_jcc
|
|
value: [0.69230769 0.73913043 0.68 0.69565217 0.86363636 0.72
|
|
0.76190476 0.6 0.73913043 0.69565217]
|
|
|
|
mean value: 0.7187414035240123
|
|
|
|
key: train_jcc
|
|
value: [0.785 0.81818182 0.80295567 0.79310345 0.78817734 0.77403846
|
|
0.80193237 0.8159204 0.8019802 0.80099502]
|
|
|
|
mean value: 0.7982284720977383
|
|
|
|
key: TN
|
|
value: 176
|
|
|
|
mean value: 176.0
|
|
|
|
key: FP
|
|
value: 37
|
|
|
|
mean value: 37.0
|
|
|
|
key: FN
|
|
value: 30
|
|
|
|
mean value: 30.0
|
|
|
|
key: TP
|
|
value: 169
|
|
|
|
mean value: 169.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.57
|
|
|
|
Accuracy on Blind test: 0.81
|
|
|
|
Running classifier: 7
|
|
Model_name: MLP
|
|
Model func: MLPClassifier(max_iter=500, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MLPClassifier(max_iter=500, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [1.38255119 1.43847704 1.32005191 1.51834154 1.42000461 1.32521057
|
|
1.5029707 1.40174365 1.32761645 1.41051412]
|
|
|
|
mean value: 1.4047481775283814
|
|
|
|
key: score_time
|
|
value: [0.01294279 0.01267171 0.01273942 0.01282597 0.01268029 0.01274633
|
|
0.01404238 0.01446939 0.0127387 0.01268911]
|
|
|
|
mean value: 0.013054609298706055
|
|
|
|
key: test_mcc
|
|
value: [0.90889326 0.9047619 0.71121921 0.86333169 1. 0.90692382
|
|
0.86240942 0.65952381 0.95238095 0.8547619 ]
|
|
|
|
mean value: 0.8624205974031269
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.95454545 0.95238095 0.85 0.92307692 1. 0.95
|
|
0.91891892 0.82926829 0.97560976 0.92682927]
|
|
|
|
mean value: 0.9280629565995419
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.91304348 0.95238095 0.89473684 1. 1. 1.
|
|
1. 0.80952381 0.95238095 0.9047619 ]
|
|
|
|
mean value: 0.9426827939413751
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 0.80952381 0.85714286 1. 0.9047619
|
|
0.85 0.85 1. 0.95 ]
|
|
|
|
mean value: 0.9173809523809522
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.95238095 0.95238095 0.85365854 0.92682927 1. 0.95121951
|
|
0.92682927 0.82926829 0.97560976 0.92682927]
|
|
|
|
mean value: 0.929500580720093
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.95238095 0.95238095 0.8547619 0.92857143 1. 0.95238095
|
|
0.925 0.8297619 0.97619048 0.92738095]
|
|
|
|
mean value: 0.9298809523809524
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.91304348 0.90909091 0.73913043 0.85714286 1. 0.9047619
|
|
0.85 0.70833333 0.95238095 0.86363636]
|
|
|
|
mean value: 0.8697520233389799
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 194
|
|
|
|
mean value: 194.0
|
|
|
|
key: FP
|
|
value: 17
|
|
|
|
mean value: 17.0
|
|
|
|
key: FN
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: TP
|
|
value: 189
|
|
|
|
mean value: 189.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.75
|
|
|
|
Accuracy on Blind test: 0.89
|
|
|
|
Running classifier: 8
|
|
Model_name: Decision Tree
|
|
Model func: DecisionTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', DecisionTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02127624 0.02067518 0.01595592 0.01767468 0.01548076 0.01587391
|
|
0.01521254 0.0156157 0.01792765 0.01475978]
|
|
|
|
mean value: 0.017045235633850096
|
|
|
|
key: score_time
|
|
value: [0.01265335 0.00976419 0.00935817 0.0089314 0.00892782 0.00888157
|
|
0.00895119 0.00915241 0.00912976 0.00949645]
|
|
|
|
mean value: 0.00952463150024414
|
|
|
|
key: test_mcc
|
|
value: [0.90889326 0.90889326 1. 0.8047619 1. 0.90692382
|
|
1. 0.86333169 0.95227002 0.95238095]
|
|
|
|
mean value: 0.929745490920746
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.95 0.95 1. 0.9047619 1. 0.95
|
|
1. 0.93023256 0.97435897 0.97560976]
|
|
|
|
mean value: 0.9634963193357976
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 0.9047619 1. 1.
|
|
1. 0.86956522 1. 0.95238095]
|
|
|
|
mean value: 0.9726708074534163
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.9047619 0.9047619 1. 0.9047619 1. 0.9047619 1.
|
|
1. 0.95 1. ]
|
|
|
|
mean value: 0.9569047619047618
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.95238095 0.95238095 1. 0.90243902 1. 0.95121951
|
|
1. 0.92682927 0.97560976 0.97560976]
|
|
|
|
mean value: 0.9636469221835074
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.95238095 0.95238095 1. 0.90238095 1. 0.95238095
|
|
1. 0.92857143 0.975 0.97619048]
|
|
|
|
mean value: 0.9639285714285715
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.9047619 0.9047619 1. 0.82608696 1. 0.9047619
|
|
1. 0.86956522 0.95 0.95238095]
|
|
|
|
mean value: 0.931231884057971
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 200
|
|
|
|
mean value: 200.0
|
|
|
|
key: FP
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: FN
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: TP
|
|
value: 197
|
|
|
|
mean value: 197.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.9
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 9
|
|
Model_name: Extra Trees
|
|
Model func: ExtraTreesClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreesClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.10961461 0.10851312 0.109653 0.11035824 0.11034012 0.11315393
|
|
0.10982227 0.10867882 0.10954762 0.10860085]
|
|
|
|
mean value: 0.10982825756072997
|
|
|
|
key: score_time
|
|
value: [0.01763701 0.01784396 0.0181098 0.01774406 0.01787066 0.0188036
|
|
0.01838684 0.01768804 0.01775908 0.01774549]
|
|
|
|
mean value: 0.017958855628967284
|
|
|
|
key: test_mcc
|
|
value: [0.71754731 0.81322028 0.75714286 0.8547619 0.90692382 0.90238095
|
|
0.80817439 0.56190476 0.8547619 0.70714286]
|
|
|
|
mean value: 0.7883961048324287
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.86363636 0.90909091 0.87804878 0.92682927 0.95 0.95238095
|
|
0.89473684 0.7804878 0.92682927 0.85 ]
|
|
|
|
mean value: 0.8932040189164707
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.82608696 0.86956522 0.9 0.95 1. 0.95238095
|
|
0.94444444 0.76190476 0.9047619 0.85 ]
|
|
|
|
mean value: 0.8959144237405108
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.9047619 0.95238095 0.85714286 0.9047619 0.9047619 0.95238095
|
|
0.85 0.8 0.95 0.85 ]
|
|
|
|
mean value: 0.8926190476190475
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.85714286 0.9047619 0.87804878 0.92682927 0.95121951 0.95121951
|
|
0.90243902 0.7804878 0.92682927 0.85365854]
|
|
|
|
mean value: 0.8932636469221835
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.85714286 0.9047619 0.87857143 0.92738095 0.95238095 0.95119048
|
|
0.90119048 0.78095238 0.92738095 0.85357143]
|
|
|
|
mean value: 0.893452380952381
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.76 0.83333333 0.7826087 0.86363636 0.9047619 0.90909091
|
|
0.80952381 0.64 0.86363636 0.73913043]
|
|
|
|
mean value: 0.8105721814417466
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 184
|
|
|
|
mean value: 184.0
|
|
|
|
key: FP
|
|
value: 22
|
|
|
|
mean value: 22.0
|
|
|
|
key: FN
|
|
value: 22
|
|
|
|
mean value: 22.0
|
|
|
|
key: TP
|
|
value: 184
|
|
|
|
mean value: 184.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.54
|
|
|
|
Accuracy on Blind test: 0.8
|
|
|
|
Running classifier: 10
|
|
Model_name: Extra Tree
|
|
Model func: ExtraTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01006579 0.00969267 0.00944519 0.00950289 0.00946856 0.0098021
|
|
0.0096879 0.00964212 0.00958872 0.00959158]
|
|
|
|
mean value: 0.009648752212524415
|
|
|
|
key: score_time
|
|
value: [0.00878835 0.00860691 0.00863194 0.00864887 0.00862575 0.00864124
|
|
0.00870109 0.00867701 0.00868344 0.00871062]
|
|
|
|
mean value: 0.00867152214050293
|
|
|
|
key: test_mcc
|
|
value: [0.33954988 0.43656413 0.75714286 0.41766229 0.76500781 0.8047619
|
|
0.77831178 0.31960727 0.7098505 0.75714286]
|
|
|
|
mean value: 0.6085601274524202
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.69565217 0.68421053 0.87804878 0.7 0.87179487 0.9047619
|
|
0.85714286 0.61111111 0.84210526 0.87804878]
|
|
|
|
mean value: 0.7922876269173081
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.64 0.76470588 0.9 0.73684211 0.94444444 0.9047619
|
|
1. 0.6875 0.88888889 0.85714286]
|
|
|
|
mean value: 0.8324286082854195
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.76190476 0.61904762 0.85714286 0.66666667 0.80952381 0.9047619
|
|
0.75 0.55 0.8 0.9 ]
|
|
|
|
mean value: 0.761904761904762
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.66666667 0.71428571 0.87804878 0.70731707 0.87804878 0.90243902
|
|
0.87804878 0.65853659 0.85365854 0.87804878]
|
|
|
|
mean value: 0.8015098722415797
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.66666667 0.71428571 0.87857143 0.70833333 0.8797619 0.90238095
|
|
0.875 0.65595238 0.85238095 0.87857143]
|
|
|
|
mean value: 0.8011904761904761
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.53333333 0.52 0.7826087 0.53846154 0.77272727 0.82608696
|
|
0.75 0.44 0.72727273 0.7826087 ]
|
|
|
|
mean value: 0.6673099219620959
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 173
|
|
|
|
mean value: 173.0
|
|
|
|
key: FP
|
|
value: 49
|
|
|
|
mean value: 49.0
|
|
|
|
key: FN
|
|
value: 33
|
|
|
|
mean value: 33.0
|
|
|
|
key: TP
|
|
value: 157
|
|
|
|
mean value: 157.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.41
|
|
|
|
Accuracy on Blind test: 0.73
|
|
|
|
Running classifier: 11
|
|
Model_name: Random Forest
|
|
Model func: RandomForestClassifier(n_estimators=1000, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(n_estimators=1000, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [1.51198244 1.54020786 1.51207709 1.50037861 1.5325613 1.51260614
|
|
1.51920581 1.49548864 1.51993775 1.50940585]
|
|
|
|
mean value: 1.5153851509094238
|
|
|
|
key: score_time
|
|
value: [0.10183477 0.09141374 0.0912416 0.09255338 0.09245515 0.09194899
|
|
0.09139752 0.09136343 0.09181547 0.09130526]
|
|
|
|
mean value: 0.09273293018341064
|
|
|
|
key: test_mcc
|
|
value: [0.90889326 0.85811633 0.85441771 0.85441771 0.95238095 0.90692382
|
|
0.95227002 0.76500781 0.95238095 0.90692382]
|
|
|
|
mean value: 0.8911732380263115
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.95454545 0.93023256 0.93023256 0.93023256 0.97560976 0.95
|
|
0.97435897 0.88372093 0.97560976 0.95238095]
|
|
|
|
mean value: 0.9456923498131665
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.91304348 0.90909091 0.90909091 0.90909091 1. 1.
|
|
1. 0.82608696 0.95238095 0.90909091]
|
|
|
|
mean value: 0.9327875023527197
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 0.95238095 0.95238095 0.95238095 0.9047619
|
|
0.95 0.95 1. 1. ]
|
|
|
|
mean value: 0.9614285714285714
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.95238095 0.92857143 0.92682927 0.92682927 0.97560976 0.95121951
|
|
0.97560976 0.87804878 0.97560976 0.95121951]
|
|
|
|
mean value: 0.9441927990708479
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.95238095 0.92857143 0.92619048 0.92619048 0.97619048 0.95238095
|
|
0.975 0.8797619 0.97619048 0.95238095]
|
|
|
|
mean value: 0.9445238095238094
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.91304348 0.86956522 0.86956522 0.86956522 0.95238095 0.9047619
|
|
0.95 0.79166667 0.95238095 0.90909091]
|
|
|
|
mean value: 0.8982020515716167
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 191
|
|
|
|
mean value: 191.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: TP
|
|
value: 198
|
|
|
|
mean value: 198.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.85
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
Running classifier: 12
|
|
Model_name: Random Forest2
|
|
Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42)
|
|
|
|
Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...age_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10,
|
|
oob_score=True, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.92990637 0.95555329 0.89409733 0.94846439 0.97272491 0.92180276
|
|
0.9189589 0.93088937 0.8951056 1.03274155]
|
|
|
|
mean value: 0.940024447441101
|
|
|
|
key: score_time
|
|
value: [0.20952916 0.21944141 0.19545674 0.20378399 0.12587929 0.18719935
|
|
0.19421124 0.20344734 0.24252033 0.22045898]
|
|
|
|
mean value: 0.20019278526306153
|
|
|
|
key: test_mcc
|
|
value: [0.81322028 0.81322028 0.7565654 0.85441771 0.95238095 0.90692382
|
|
1. 0.65952381 0.90692382 0.86333169]
|
|
|
|
mean value: 0.852650778186983
|
|
|
|
key: train_mcc
|
|
value: [0.989247 0.98391316 0.97866529 0.98384191 0.98395676 0.98927606
|
|
0.98395537 0.98395537 0.97866283 0.98395537]
|
|
|
|
mean value: 0.9839429109753077
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.90909091 0.88372093 0.93023256 0.97560976 0.95
|
|
1. 0.82926829 0.95238095 0.93023256]
|
|
|
|
mean value: 0.9269626865854885
|
|
|
|
key: train_fscore
|
|
value: [0.99462366 0.9919571 0.98930481 0.99191375 0.9919571 0.99462366
|
|
0.992 0.992 0.9893617 0.992 ]
|
|
|
|
mean value: 0.9919741782535849
|
|
|
|
key: test_precision
|
|
value: [0.86956522 0.86956522 0.86363636 0.90909091 1. 1.
|
|
1. 0.80952381 0.90909091 0.86956522]
|
|
|
|
mean value: 0.9100037643515904
|
|
|
|
key: train_precision
|
|
value: [0.98930481 0.98404255 0.97883598 0.98924731 0.98404255 0.98930481
|
|
0.98412698 0.98412698 0.97894737 0.98412698]
|
|
|
|
mean value: 0.984610634351737
|
|
|
|
key: test_recall
|
|
value: [0.95238095 0.95238095 0.9047619 0.95238095 0.95238095 0.9047619
|
|
1. 0.85 1. 1. ]
|
|
|
|
mean value: 0.9469047619047618
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 0.99459459 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9994594594594595
|
|
|
|
key: test_accuracy
|
|
value: [0.9047619 0.9047619 0.87804878 0.92682927 0.97560976 0.95121951
|
|
1. 0.82926829 0.95121951 0.92682927]
|
|
|
|
mean value: 0.9248548199767713
|
|
|
|
key: train_accuracy
|
|
value: [0.99459459 0.99189189 0.98921833 0.99191375 0.99191375 0.99460916
|
|
0.99191375 0.99191375 0.98921833 0.99191375]
|
|
|
|
mean value: 0.9919101041742551
|
|
|
|
key: test_roc_auc
|
|
value: [0.9047619 0.9047619 0.87738095 0.92619048 0.97619048 0.95238095
|
|
1. 0.8297619 0.95238095 0.92857143]
|
|
|
|
mean value: 0.9252380952380952
|
|
|
|
key: train_roc_auc
|
|
value: [0.99459459 0.99189189 0.98924731 0.99192095 0.99193548 0.99462366
|
|
0.99189189 0.99189189 0.98918919 0.99189189]
|
|
|
|
mean value: 0.991907875617553
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.83333333 0.79166667 0.86956522 0.95238095 0.9047619
|
|
1. 0.70833333 0.90909091 0.86956522]
|
|
|
|
mean value: 0.8672030867683042
|
|
|
|
key: train_jcc
|
|
value: [0.98930481 0.98404255 0.97883598 0.98395722 0.98404255 0.98930481
|
|
0.98412698 0.98412698 0.97894737 0.98412698]
|
|
|
|
mean value: 0.9840816250940749
|
|
|
|
key: TN
|
|
value: 186
|
|
|
|
mean value: 186.0
|
|
|
|
key: FP
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: FN
|
|
value: 20
|
|
|
|
mean value: 20.0
|
|
|
|
key: TP
|
|
value: 195
|
|
|
|
mean value: 195.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.8
|
|
|
|
Accuracy on Blind test: 0.91
|
|
|
|
Running classifier: 13
|
|
Model_name: XGBoost
|
|
Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...
|
|
interaction_constraints=None, learning_rate=None,
|
|
max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan,
|
|
monotone_constraints=None, n_estimators=100,
|
|
n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None,
|
|
reg_lambda=None, scale_pos_weight=None,
|
|
subsample=None, tree_method=None,
|
|
use_label_encoder=False,
|
|
validate_parameters=None, verbosity=0))])
|
|
|
|
key: fit_time
|
|
value: [0.07739592 0.05289793 0.05677772 0.0575881 0.06224346 0.06622791
|
|
0.06729889 0.06369901 0.06968975 0.06554985]
|
|
|
|
mean value: 0.06393685340881347
|
|
|
|
key: score_time
|
|
value: [0.01081038 0.0105238 0.01094198 0.01074648 0.01077628 0.01114082
|
|
0.0110867 0.01144195 0.01085472 0.01093197]
|
|
|
|
mean value: 0.010925507545471192
|
|
|
|
key: test_mcc
|
|
value: [1. 1. 1. 1. 1. 0.95238095
|
|
0.90649828 0.86333169 0.95227002 0.95238095]
|
|
|
|
mean value: 0.9626861893882752
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 1. 1. 1. 1. 0.97560976
|
|
0.94736842 0.93023256 0.97435897 0.97560976]
|
|
|
|
mean value: 0.9803179465746261
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 0.86956522 1. 0.95238095]
|
|
|
|
mean value: 0.9821946169772258
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 1. 1. 0.95238095
|
|
0.9 1. 0.95 1. ]
|
|
|
|
mean value: 0.9802380952380952
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 1. 1. 1. 1. 0.97560976
|
|
0.95121951 0.92682927 0.97560976 0.97560976]
|
|
|
|
mean value: 0.9804878048780488
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 1. 1. 1. 1. 0.97619048
|
|
0.95 0.92857143 0.975 0.97619048]
|
|
|
|
mean value: 0.9805952380952381
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 1. 1. 1. 1. 0.95238095
|
|
0.9 0.86956522 0.95 0.95238095]
|
|
|
|
mean value: 0.9624327122153209
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 202
|
|
|
|
mean value: 202.0
|
|
|
|
key: FP
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: FN
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: TP
|
|
value: 202
|
|
|
|
mean value: 202.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.9
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 14
|
|
Model_name: LDA
|
|
Model func: LinearDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LinearDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.02928782 0.04429269 0.09572411 0.04821539 0.03643346 0.03635001
|
|
0.09062386 0.06526732 0.07165194 0.07450461]
|
|
|
|
mean value: 0.059235119819641115
|
|
|
|
key: score_time
|
|
value: [0.01215029 0.02206373 0.03581309 0.0122366 0.01228285 0.01228428
|
|
0.02322578 0.02300858 0.02337956 0.02231598]
|
|
|
|
mean value: 0.01987607479095459
|
|
|
|
key: test_mcc
|
|
value: [1. 0.85811633 0.71121921 0.75714286 0.8547619 0.8213423
|
|
0.86240942 0.70714286 0.90238095 0.7197263 ]
|
|
|
|
mean value: 0.819424213092508
|
|
|
|
key: train_mcc
|
|
value: [0.98379816 0.98379816 0.978494 0.97317407 0.98384144 0.98384191
|
|
0.98384144 0.98384191 0.9784365 0.98384191]
|
|
|
|
mean value: 0.9816909473768508
|
|
|
|
key: test_fscore
|
|
value: [1. 0.92682927 0.85 0.87804878 0.92682927 0.89473684
|
|
0.91891892 0.85 0.95 0.83333333]
|
|
|
|
mean value: 0.9028696411430687
|
|
|
|
key: train_fscore
|
|
value: [0.99191375 0.99191375 0.98924731 0.98659517 0.99186992 0.99191375
|
|
0.9919571 0.99191375 0.98924731 0.99191375]
|
|
|
|
mean value: 0.9908485554329116
|
|
|
|
key: test_precision
|
|
value: [1. 0.95 0.89473684 0.9 0.95 1.
|
|
1. 0.85 0.95 0.9375 ]
|
|
|
|
mean value: 0.9432236842105264
|
|
|
|
key: train_precision
|
|
value: [0.98924731 0.98924731 0.98395722 0.9787234 0.99456522 0.98924731
|
|
0.98930481 0.99459459 0.98924731 0.99459459]
|
|
|
|
mean value: 0.9892729090233203
|
|
|
|
key: test_recall
|
|
value: [1. 0.9047619 0.80952381 0.85714286 0.9047619 0.80952381
|
|
0.85 0.85 0.95 0.75 ]
|
|
|
|
mean value: 0.8685714285714287
|
|
|
|
key: train_recall
|
|
value: [0.99459459 0.99459459 0.99459459 0.99459459 0.98918919 0.99459459
|
|
0.99462366 0.98924731 0.98924731 0.98924731]
|
|
|
|
mean value: 0.9924527753560011
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.92857143 0.85365854 0.87804878 0.92682927 0.90243902
|
|
0.92682927 0.85365854 0.95121951 0.85365854]
|
|
|
|
mean value: 0.9074912891986063
|
|
|
|
key: train_accuracy
|
|
value: [0.99189189 0.99189189 0.98921833 0.98652291 0.99191375 0.99191375
|
|
0.99191375 0.99191375 0.98921833 0.99191375]
|
|
|
|
mean value: 0.9908312085670575
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.92857143 0.8547619 0.87857143 0.92738095 0.9047619
|
|
0.925 0.85357143 0.95119048 0.85119048]
|
|
|
|
mean value: 0.9075
|
|
|
|
key: train_roc_auc
|
|
value: [0.99189189 0.99189189 0.98923278 0.98654461 0.99190642 0.99192095
|
|
0.99190642 0.99192095 0.98921825 0.99192095]
|
|
|
|
mean value: 0.9908355129322871
|
|
|
|
key: test_jcc
|
|
value: [1. 0.86363636 0.73913043 0.7826087 0.86363636 0.80952381
|
|
0.85 0.73913043 0.9047619 0.71428571]
|
|
|
|
mean value: 0.8266713721061547
|
|
|
|
key: train_jcc
|
|
value: [0.98395722 0.98395722 0.9787234 0.97354497 0.98387097 0.98395722
|
|
0.98404255 0.98395722 0.9787234 0.98395722]
|
|
|
|
mean value: 0.9818691399245723
|
|
|
|
key: TN
|
|
value: 195
|
|
|
|
mean value: 195.0
|
|
|
|
key: FP
|
|
value: 27
|
|
|
|
mean value: 27.0
|
|
|
|
key: FN
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: TP
|
|
value: 179
|
|
|
|
mean value: 179.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.68
|
|
|
|
Accuracy on Blind test: 0.86
|
|
|
|
Running classifier: 15
|
|
Model_name: Multinomial
|
|
Model func: MultinomialNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MultinomialNB())])
|
|
|
|
key: fit_time
|
|
value: [0.00992703 0.00981212 0.009444 0.00925446 0.00927901 0.00920343
|
|
0.00935507 0.00949359 0.00937295 0.00925636]
|
|
|
|
mean value: 0.009439802169799805
|
|
|
|
key: score_time
|
|
value: [0.01170301 0.00905418 0.00861382 0.00861716 0.00858498 0.00860953
|
|
0.00862718 0.00857091 0.00866532 0.00863743]
|
|
|
|
mean value: 0.008968353271484375
|
|
|
|
key: test_mcc
|
|
value: [0.23918244 0.47673129 0.06905393 0.22195767 0.46300848 0.56190476
|
|
0.41487884 0.16945156 0.56190476 0.46428571]
|
|
|
|
mean value: 0.3642359453605911
|
|
|
|
key: train_mcc
|
|
value: [0.36235278 0.38387352 0.43399024 0.41274834 0.38004592 0.41782673
|
|
0.39622203 0.4018383 0.41258723 0.44486121]
|
|
|
|
mean value: 0.40463463119473336
|
|
|
|
key: test_fscore
|
|
value: [0.63636364 0.73170732 0.6122449 0.6 0.74418605 0.7804878
|
|
0.68421053 0.56410256 0.7804878 0.73170732]
|
|
|
|
mean value: 0.686549791515524
|
|
|
|
key: train_fscore
|
|
value: [0.68617021 0.69518717 0.71389646 0.71087533 0.68834688 0.70967742
|
|
0.69892473 0.70712401 0.70299728 0.72679045]
|
|
|
|
mean value: 0.7039989938565314
|
|
|
|
key: test_precision
|
|
value: [0.60869565 0.75 0.53571429 0.63157895 0.72727273 0.8
|
|
0.72222222 0.57894737 0.76190476 0.71428571]
|
|
|
|
mean value: 0.6830621679363098
|
|
|
|
key: train_precision
|
|
value: [0.67539267 0.68783069 0.71978022 0.69791667 0.69021739 0.70588235
|
|
0.69892473 0.69430052 0.71270718 0.71727749]
|
|
|
|
mean value: 0.7000229907229114
|
|
|
|
key: test_recall
|
|
value: [0.66666667 0.71428571 0.71428571 0.57142857 0.76190476 0.76190476
|
|
0.65 0.55 0.8 0.75 ]
|
|
|
|
mean value: 0.694047619047619
|
|
|
|
key: train_recall
|
|
value: [0.6972973 0.7027027 0.70810811 0.72432432 0.68648649 0.71351351
|
|
0.69892473 0.72043011 0.69354839 0.73655914]
|
|
|
|
mean value: 0.708189479802383
|
|
|
|
key: test_accuracy
|
|
value: [0.61904762 0.73809524 0.53658537 0.6097561 0.73170732 0.7804878
|
|
0.70731707 0.58536585 0.7804878 0.73170732]
|
|
|
|
mean value: 0.6820557491289199
|
|
|
|
key: train_accuracy
|
|
value: [0.68108108 0.69189189 0.71698113 0.70619946 0.69002695 0.70889488
|
|
0.69811321 0.70080863 0.70619946 0.72237197]
|
|
|
|
mean value: 0.7022568660304509
|
|
|
|
key: test_roc_auc
|
|
value: [0.61904762 0.73809524 0.53214286 0.61071429 0.73095238 0.78095238
|
|
0.70595238 0.58452381 0.78095238 0.73214286]
|
|
|
|
mean value: 0.681547619047619
|
|
|
|
key: train_roc_auc
|
|
value: [0.68108108 0.69189189 0.71695728 0.70624818 0.69001744 0.70890729
|
|
0.69811101 0.70075559 0.70623365 0.72233362]
|
|
|
|
mean value: 0.7022537053182214
|
|
|
|
key: test_jcc
|
|
value: [0.46666667 0.57692308 0.44117647 0.42857143 0.59259259 0.64
|
|
0.52 0.39285714 0.64 0.57692308]
|
|
|
|
mean value: 0.5275710455122219
|
|
|
|
key: train_jcc
|
|
value: [0.52226721 0.53278689 0.55508475 0.55144033 0.52479339 0.55
|
|
0.53719008 0.54693878 0.54201681 0.57083333]
|
|
|
|
mean value: 0.543335155334506
|
|
|
|
key: TN
|
|
value: 138
|
|
|
|
mean value: 138.0
|
|
|
|
key: FP
|
|
value: 63
|
|
|
|
mean value: 63.0
|
|
|
|
key: FN
|
|
value: 68
|
|
|
|
mean value: 68.0
|
|
|
|
key: TP
|
|
value: 143
|
|
|
|
mean value: 143.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.33
|
|
|
|
Accuracy on Blind test: 0.68
|
|
|
|
Running classifier: 16
|
|
Model_name: Passive Aggresive
|
|
Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
PassiveAggressiveClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02252197 0.01936436 0.02320266 0.01887608 0.02798271 0.02770257
|
|
0.02312875 0.02318501 0.0263114 0.02959681]
|
|
|
|
mean value: 0.024187231063842775
|
|
|
|
key: score_time
|
|
value: [0.00863934 0.01137352 0.01186848 0.01171231 0.01185107 0.01182008
|
|
0.01177526 0.01178193 0.0118196 0.0117321 ]
|
|
|
|
mean value: 0.011437368392944337
|
|
|
|
key: test_mcc
|
|
value: [0.95346259 1. 0.90692382 0.86333169 1. 0.95238095
|
|
0.95227002 0.76500781 0.90238095 0.95238095]
|
|
|
|
mean value: 0.9248138787322764
|
|
|
|
key: train_mcc
|
|
value: [0.98379816 0.98379816 0.98927606 0.9361732 0.98927606 0.98384191
|
|
0.98384144 0.97866283 0.98384144 0.9946235 ]
|
|
|
|
mean value: 0.9807132751752643
|
|
|
|
key: test_fscore
|
|
value: [0.97674419 1. 0.95 0.92307692 1. 0.97560976
|
|
0.97435897 0.88372093 0.95 0.97560976]
|
|
|
|
mean value: 0.960912052591009
|
|
|
|
key: train_fscore
|
|
value: [0.99191375 0.99191375 0.99462366 0.96685083 0.99462366 0.99191375
|
|
0.9919571 0.9893617 0.9919571 0.99731903]
|
|
|
|
mean value: 0.990243432654491
|
|
|
|
key: test_precision
|
|
value: [0.95454545 1. 1. 1. 1. 1.
|
|
1. 0.82608696 0.95 0.95238095]
|
|
|
|
mean value: 0.9683013363448145
|
|
|
|
key: train_precision
|
|
value: [0.98924731 0.98924731 0.98930481 0.98870056 0.98930481 0.98924731
|
|
0.98930481 0.97894737 0.98930481 0.99465241]
|
|
|
|
mean value: 0.9887261526630686
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.9047619 0.85714286 1. 0.95238095
|
|
0.95 0.95 0.95 1. ]
|
|
|
|
mean value: 0.9564285714285713
|
|
|
|
key: train_recall
|
|
value: [0.99459459 0.99459459 1. 0.94594595 1. 0.99459459
|
|
0.99462366 1. 0.99462366 1. ]
|
|
|
|
mean value: 0.9918977041557687
|
|
|
|
key: test_accuracy
|
|
value: [0.97619048 1. 0.95121951 0.92682927 1. 0.97560976
|
|
0.97560976 0.87804878 0.95121951 0.97560976]
|
|
|
|
mean value: 0.9610336817653892
|
|
|
|
key: train_accuracy
|
|
value: [0.99189189 0.99189189 0.99460916 0.96765499 0.99460916 0.99191375
|
|
0.99191375 0.98921833 0.99191375 0.99730458]
|
|
|
|
mean value: 0.9902921250091061
|
|
|
|
key: test_roc_auc
|
|
value: [0.97619048 1. 0.95238095 0.92857143 1. 0.97619048
|
|
0.975 0.8797619 0.95119048 0.97619048]
|
|
|
|
mean value: 0.9615476190476191
|
|
|
|
key: train_roc_auc
|
|
value: [0.99189189 0.99189189 0.99462366 0.96759663 0.99462366 0.99192095
|
|
0.99190642 0.98918919 0.99190642 0.9972973 ]
|
|
|
|
mean value: 0.9902848009299621
|
|
|
|
key: test_jcc
|
|
value: [0.95454545 1. 0.9047619 0.85714286 1. 0.95238095
|
|
0.95 0.79166667 0.9047619 0.95238095]
|
|
|
|
mean value: 0.9267640692640693
|
|
|
|
key: train_jcc
|
|
value: [0.98395722 0.98395722 0.98930481 0.93582888 0.98930481 0.98395722
|
|
0.98404255 0.97894737 0.98404255 0.99465241]
|
|
|
|
mean value: 0.9807995041648951
|
|
|
|
key: TN
|
|
value: 199
|
|
|
|
mean value: 199.0
|
|
|
|
key: FP
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 197
|
|
|
|
mean value: 197.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.86
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
Running classifier: 17
|
|
Model_name: Stochastic GDescent
|
|
Model func: SGDClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SGDClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01611996 0.01744223 0.01719046 0.01823139 0.01707792 0.01627302
|
|
0.01734209 0.01785684 0.01796865 0.0183959 ]
|
|
|
|
mean value: 0.017389845848083497
|
|
|
|
key: score_time
|
|
value: [0.01184702 0.0118773 0.01190281 0.01194453 0.01188087 0.01207185
|
|
0.01205659 0.01236892 0.01192665 0.01186895]
|
|
|
|
mean value: 0.011974549293518067
|
|
|
|
key: test_mcc
|
|
value: [0.72760688 0.95346259 0.90692382 0.95227002 1. 0.90692382
|
|
0.66668392 0.75714286 0.77831178 0.80817439]
|
|
|
|
mean value: 0.8457500078758784
|
|
|
|
key: train_mcc
|
|
value: [0.69419957 0.97310093 0.97305937 0.9734012 0.97317407 0.95709306
|
|
0.90722239 0.98384191 0.73247456 0.94236768]
|
|
|
|
mean value: 0.9109934736638234
|
|
|
|
key: test_fscore
|
|
value: [0.84210526 0.97674419 0.95 0.97674419 1. 0.95
|
|
0.8372093 0.87804878 0.85714286 0.89473684]
|
|
|
|
mean value: 0.9162731417312425
|
|
|
|
key: train_fscore
|
|
value: [0.79220779 0.98637602 0.98644986 0.98666667 0.98659517 0.97814208
|
|
0.95384615 0.99191375 0.82278481 0.96952909]
|
|
|
|
mean value: 0.9454511392412976
|
|
|
|
key: test_precision
|
|
value: [0.94117647 0.95454545 1. 0.95454545 1. 1.
|
|
0.7826087 0.85714286 1. 0.94444444]
|
|
|
|
mean value: 0.943446337691862
|
|
|
|
key: train_precision
|
|
value: [0.99186992 0.99450549 0.98913043 0.97368421 0.9787234 0.98895028
|
|
0.91176471 0.99459459 1. 1. ]
|
|
|
|
mean value: 0.9823223039488965
|
|
|
|
key: test_recall
|
|
value: [0.76190476 1. 0.9047619 1. 1. 0.9047619
|
|
0.9 0.9 0.75 0.85 ]
|
|
|
|
mean value: 0.8971428571428571
|
|
|
|
key: train_recall
|
|
value: [0.65945946 0.97837838 0.98378378 1. 0.99459459 0.96756757
|
|
1. 0.98924731 0.69892473 0.94086022]
|
|
|
|
mean value: 0.9212816041848301
|
|
|
|
key: test_accuracy
|
|
value: [0.85714286 0.97619048 0.95121951 0.97560976 1. 0.95121951
|
|
0.82926829 0.87804878 0.87804878 0.90243902]
|
|
|
|
mean value: 0.9199186991869919
|
|
|
|
key: train_accuracy
|
|
value: [0.82702703 0.98648649 0.98652291 0.98652291 0.98652291 0.97843666
|
|
0.95148248 0.99191375 0.8490566 0.9703504 ]
|
|
|
|
mean value: 0.9514322138850441
|
|
|
|
key: test_roc_auc
|
|
value: [0.85714286 0.97619048 0.95238095 0.975 1. 0.95238095
|
|
0.83095238 0.87857143 0.875 0.90119048]
|
|
|
|
mean value: 0.9198809523809525
|
|
|
|
key: train_roc_auc
|
|
value: [0.82702703 0.98648649 0.98651555 0.98655914 0.98654461 0.97840744
|
|
0.95135135 0.99192095 0.84946237 0.97043011]
|
|
|
|
mean value: 0.9514705027608255
|
|
|
|
key: test_jcc
|
|
value: [0.72727273 0.95454545 0.9047619 0.95454545 1. 0.9047619
|
|
0.72 0.7826087 0.75 0.80952381]
|
|
|
|
mean value: 0.850801995106343
|
|
|
|
key: train_jcc
|
|
value: [0.65591398 0.97311828 0.97326203 0.97368421 0.97354497 0.95721925
|
|
0.91176471 0.98395722 0.69892473 0.94086022]
|
|
|
|
mean value: 0.9042249596928515
|
|
|
|
key: TN
|
|
value: 194
|
|
|
|
mean value: 194.0
|
|
|
|
key: FP
|
|
value: 21
|
|
|
|
mean value: 21.0
|
|
|
|
key: FN
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: TP
|
|
value: 185
|
|
|
|
mean value: 185.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.45
|
|
|
|
Accuracy on Blind test: 0.64
|
|
|
|
Running classifier: 18
|
|
Model_name: AdaBoost Classifier
|
|
Model func: AdaBoostClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', AdaBoostClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.16417074 0.14623141 0.14611936 0.14649653 0.14675474 0.15263605
|
|
0.15256119 0.1455555 0.14614868 0.14716053]
|
|
|
|
mean value: 0.14938347339630126
|
|
|
|
key: score_time
|
|
value: [0.01506853 0.01520467 0.01521516 0.01508999 0.01510882 0.0154891
|
|
0.01506519 0.01622915 0.01549888 0.0156498 ]
|
|
|
|
mean value: 0.015361928939819336
|
|
|
|
key: test_mcc
|
|
value: [0.9047619 1. 1. 0.95227002 1. 0.95238095
|
|
0.95227002 0.86333169 1. 0.95238095]
|
|
|
|
mean value: 0.9577395534494869
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.95238095 1. 1. 0.97674419 1. 0.97560976
|
|
0.97435897 0.93023256 1. 0.97560976]
|
|
|
|
mean value: 0.9784936183121097
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.95238095 1. 1. 0.95454545 1. 1.
|
|
1. 0.86956522 1. 0.95238095]
|
|
|
|
mean value: 0.9728872576698663
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.95238095 1. 1. 1. 1. 0.95238095
|
|
0.95 1. 1. 1. ]
|
|
|
|
mean value: 0.9854761904761904
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.95238095 1. 1. 0.97560976 1. 0.97560976
|
|
0.97560976 0.92682927 1. 0.97560976]
|
|
|
|
mean value: 0.9781649245063878
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.95238095 1. 1. 0.975 1. 0.97619048
|
|
0.975 0.92857143 1. 0.97619048]
|
|
|
|
mean value: 0.9783333333333333
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.90909091 1. 1. 0.95454545 1. 0.95238095
|
|
0.95 0.86956522 1. 0.95238095]
|
|
|
|
mean value: 0.9587963485789572
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 200
|
|
|
|
mean value: 200.0
|
|
|
|
key: FP
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: FN
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: TP
|
|
value: 203
|
|
|
|
mean value: 203.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.93
|
|
|
|
Accuracy on Blind test: 0.97
|
|
|
|
Running classifier: 19
|
|
Model_name: Bagging Classifier
|
|
Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
BaggingClassifier(n_jobs=10, oob_score=True,
|
|
random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.03610611 0.04993343 0.05819893 0.05507755 0.06369472 0.05800819
|
|
0.05741405 0.04725194 0.0697515 0.05640531]
|
|
|
|
mean value: 0.05518417358398438
|
|
|
|
key: score_time
|
|
value: [0.02264643 0.02638483 0.02947092 0.03190923 0.03001213 0.03039432
|
|
0.02232504 0.0379746 0.03620601 0.01787853]
|
|
|
|
mean value: 0.02852020263671875
|
|
|
|
key: test_mcc
|
|
value: [0.90889326 0.90889326 0.90238095 0.95227002 0.95238095 0.95238095
|
|
0.90649828 0.8213423 0.95227002 0.95238095]
|
|
|
|
mean value: 0.9209690938025448
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 0.98927544 0.9946235 0.98927544 0.98927544
|
|
0.9946235 0.98921825 0.98384191 0.98921825]
|
|
|
|
mean value: 0.9919351733305355
|
|
|
|
key: test_fscore
|
|
value: [0.95 0.95 0.95238095 0.97674419 0.97560976 0.97560976
|
|
0.94736842 0.90909091 0.97435897 0.97560976]
|
|
|
|
mean value: 0.9586772711222661
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 0.99456522 0.99728997 0.99456522 0.99456522
|
|
0.99731903 0.99462366 0.99191375 0.99462366]
|
|
|
|
mean value: 0.9959465718384873
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.95238095 0.95454545 1. 1.
|
|
1. 0.83333333 1. 0.95238095]
|
|
|
|
mean value: 0.9692640692640693
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
0.99465241 0.99462366 0.99459459 0.99462366]
|
|
|
|
mean value: 0.9978494312839663
|
|
|
|
key: test_recall
|
|
value: [0.9047619 0.9047619 0.95238095 1. 0.95238095 0.95238095
|
|
0.9 1. 0.95 1. ]
|
|
|
|
mean value: 0.9516666666666665
|
|
|
|
key: train_recall
|
|
value: [1. 1. 0.98918919 0.99459459 0.98918919 0.98918919
|
|
1. 0.99462366 0.98924731 0.99462366]
|
|
|
|
mean value: 0.9940656785818076
|
|
|
|
key: test_accuracy
|
|
value: [0.95238095 0.95238095 0.95121951 0.97560976 0.97560976 0.97560976
|
|
0.95121951 0.90243902 0.97560976 0.97560976]
|
|
|
|
mean value: 0.9587688734030199
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 0.99460916 0.99730458 0.99460916 0.99460916
|
|
0.99730458 0.99460916 0.99191375 0.99460916]
|
|
|
|
mean value: 0.9959568733153639
|
|
|
|
key: test_roc_auc
|
|
value: [0.95238095 0.95238095 0.95119048 0.975 0.97619048 0.97619048
|
|
0.95 0.9047619 0.975 0.97619048]
|
|
|
|
mean value: 0.9589285714285714
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 0.99459459 0.9972973 0.99459459 0.99459459
|
|
0.9972973 0.99460913 0.99192095 0.99460913]
|
|
|
|
mean value: 0.995951758209823
|
|
|
|
key: test_jcc
|
|
value: [0.9047619 0.9047619 0.90909091 0.95454545 0.95238095 0.95238095
|
|
0.9 0.83333333 0.95 0.95238095]
|
|
|
|
mean value: 0.9213636363636363
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 0.98918919 0.99459459 0.98918919 0.98918919
|
|
0.99465241 0.98930481 0.98395722 0.98930481]
|
|
|
|
mean value: 0.991938141349906
|
|
|
|
key: TN
|
|
value: 199
|
|
|
|
mean value: 199.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 196
|
|
|
|
mean value: 196.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.91
|
|
|
|
Accuracy on Blind test: 0.96
|
|
|
|
Running classifier: 20
|
|
Model_name: Gaussian Process
|
|
Model func: GaussianProcessClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianProcessClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.0951314 0.0657649 0.08133554 0.10624385 0.10583973 0.05929923
|
|
0.07229829 0.14089751 0.10813332 0.08627105]
|
|
|
|
mean value: 0.09212148189544678
|
|
|
|
key: score_time
|
|
value: [0.0226891 0.01400733 0.02260351 0.02237463 0.02234697 0.01414204
|
|
0.01397967 0.02263641 0.0223701 0.01710892]
|
|
|
|
mean value: 0.01942586898803711
|
|
|
|
key: test_mcc
|
|
value: [0.62187434 0.61904762 0.6133669 0.48063079 0.80907152 0.76500781
|
|
0.57570364 0.46300848 0.7098505 0.65871309]
|
|
|
|
mean value: 0.6316274678175477
|
|
|
|
key: train_mcc
|
|
value: [0.92567765 0.91485102 0.92538015 0.89861855 0.91506448 0.91458711
|
|
0.92501826 0.93618785 0.92588469 0.92539732]
|
|
|
|
mean value: 0.9206667079567575
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.80952381 0.8 0.7027027 0.9 0.87179487
|
|
0.74285714 0.71794872 0.84210526 0.82051282]
|
|
|
|
mean value: 0.800744532849796
|
|
|
|
key: train_fscore
|
|
value: [0.96111111 0.95555556 0.96132597 0.94736842 0.95555556 0.9558011
|
|
0.96174863 0.96703297 0.96132597 0.96153846]
|
|
|
|
mean value: 0.9588363744400097
|
|
|
|
key: test_precision
|
|
value: [0.84210526 0.80952381 0.84210526 0.8125 0.94736842 0.94444444
|
|
0.86666667 0.73684211 0.88888889 0.84210526]
|
|
|
|
mean value: 0.8532550125313282
|
|
|
|
key: train_precision
|
|
value: [0.98857143 0.98285714 0.98305085 0.97159091 0.98285714 0.97740113
|
|
0.97777778 0.98876404 0.98863636 0.98314607]
|
|
|
|
mean value: 0.9824652854551446
|
|
|
|
key: test_recall
|
|
value: [0.76190476 0.80952381 0.76190476 0.61904762 0.85714286 0.80952381
|
|
0.65 0.7 0.8 0.8 ]
|
|
|
|
mean value: 0.7569047619047619
|
|
|
|
key: train_recall
|
|
value: [0.93513514 0.92972973 0.94054054 0.92432432 0.92972973 0.93513514
|
|
0.94623656 0.94623656 0.93548387 0.94086022]
|
|
|
|
mean value: 0.9363411798895671
|
|
|
|
key: test_accuracy
|
|
value: [0.80952381 0.80952381 0.80487805 0.73170732 0.90243902 0.87804878
|
|
0.7804878 0.73170732 0.85365854 0.82926829]
|
|
|
|
mean value: 0.813124274099884
|
|
|
|
key: train_accuracy
|
|
value: [0.96216216 0.95675676 0.96226415 0.94878706 0.95687332 0.95687332
|
|
0.96226415 0.96765499 0.96226415 0.96226415]
|
|
|
|
mean value: 0.9598164201937787
|
|
|
|
key: test_roc_auc
|
|
value: [0.80952381 0.80952381 0.80595238 0.73452381 0.90357143 0.8797619
|
|
0.77738095 0.73095238 0.85238095 0.82857143]
|
|
|
|
mean value: 0.8132142857142857
|
|
|
|
key: train_roc_auc
|
|
value: [0.96216216 0.95675676 0.96220575 0.9487213 0.95680035 0.95681488
|
|
0.96230747 0.96771287 0.96233653 0.962322 ]
|
|
|
|
mean value: 0.9598140075559429
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.68 0.66666667 0.54166667 0.81818182 0.77272727
|
|
0.59090909 0.56 0.72727273 0.69565217]
|
|
|
|
mean value: 0.6719743083003953
|
|
|
|
key: train_jcc
|
|
value: [0.92513369 0.91489362 0.92553191 0.9 0.91489362 0.91534392
|
|
0.92631579 0.93617021 0.92553191 0.92592593]
|
|
|
|
mean value: 0.9209740597178842
|
|
|
|
key: TN
|
|
value: 179
|
|
|
|
mean value: 179.0
|
|
|
|
key: FP
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: FN
|
|
value: 27
|
|
|
|
mean value: 27.0
|
|
|
|
key: TP
|
|
value: 156
|
|
|
|
mean value: 156.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.42
|
|
|
|
Accuracy on Blind test: 0.74
|
|
|
|
Running classifier: 21
|
|
Model_name: Gradient Boosting
|
|
Model func: GradientBoostingClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GradientBoostingClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.53300953 0.51279473 0.51417923 0.54170537 0.52085948 0.51572061
|
|
0.51508975 0.50532246 0.51649785 0.51727605]
|
|
|
|
mean value: 0.5192455053329468
|
|
|
|
key: score_time
|
|
value: [0.00918722 0.00914145 0.00938344 0.00916529 0.00929856 0.00910997
|
|
0.00938439 0.00916171 0.00917625 0.00926542]
|
|
|
|
mean value: 0.009227371215820313
|
|
|
|
key: test_mcc
|
|
value: [1. 1. 1. 0.90238095 1. 0.95238095
|
|
1. 0.86333169 0.95227002 0.95238095]
|
|
|
|
mean value: 0.9622744566930101
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 1. 1. 0.95238095 1. 0.97560976
|
|
1. 0.93023256 0.97435897 0.97560976]
|
|
|
|
mean value: 0.9808191997074583
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 0.95238095 1. 1.
|
|
1. 0.86956522 1. 0.95238095]
|
|
|
|
mean value: 0.977432712215321
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 0.95238095 1. 0.95238095
|
|
1. 1. 0.95 1. ]
|
|
|
|
mean value: 0.9854761904761904
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 1. 1. 0.95121951 1. 0.97560976
|
|
1. 0.92682927 0.97560976 0.97560976]
|
|
|
|
mean value: 0.9804878048780488
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 1. 1. 0.95119048 1. 0.97619048
|
|
1. 0.92857143 0.975 0.97619048]
|
|
|
|
mean value: 0.9807142857142856
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 1. 1. 0.90909091 1. 0.95238095
|
|
1. 0.86956522 0.95 0.95238095]
|
|
|
|
mean value: 0.9633418031244118
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 201
|
|
|
|
mean value: 201.0
|
|
|
|
key: FP
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: FN /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: TP
|
|
value: 203
|
|
|
|
mean value: 203.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.91
|
|
|
|
Accuracy on Blind test: 0.96
|
|
|
|
Running classifier: 22
|
|
Model_name: QDA
|
|
Model func: QuadraticDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', QuadraticDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.02316737 0.02680206 0.02732277 0.02696562 0.02697563 0.02746534
|
|
0.02724886 0.02720308 0.02752709 0.02719426]
|
|
|
|
mean value: 0.026787209510803222
|
|
|
|
key: score_time
|
|
value: [0.01268864 0.0126133 0.01294136 0.01298118 0.0129559 0.01299787
|
|
0.01317263 0.01305532 0.01269197 0.01263595]
|
|
|
|
mean value: 0.012873411178588867
|
|
|
|
key: test_mcc
|
|
value: [0.48737732 0.78446454 0.7633652 0.7197263 0.86333169 0.73786479
|
|
0.80817439 0.56836003 0.86333169 0.56190476]
|
|
|
|
mean value: 0.7157900728918658
|
|
|
|
key: train_mcc
|
|
value: [1. 0.98391316 0.98921825 0.9734012 0.97339739 0.97339739
|
|
0.99462366 0.98921825 0.99462366 0.95776892]
|
|
|
|
mean value: 0.9829561871566351
|
|
|
|
key: test_fscore
|
|
value: [0.76923077 0.89361702 0.88888889 0.86956522 0.92307692 0.875
|
|
0.89473684 0.79069767 0.93023256 0.7804878 ]
|
|
|
|
mean value: 0.8615533699405933
|
|
|
|
key: train_fscore
|
|
value: [1. 0.9919571 0.99459459 0.98666667 0.98630137 0.98630137
|
|
0.99730458 0.99462366 0.99730458 0.97802198]
|
|
|
|
mean value: 0.991307590390137
|
|
|
|
key: test_precision
|
|
value: [0.64516129 0.80769231 0.83333333 0.8 1. 0.77777778
|
|
0.94444444 0.73913043 0.86956522 0.76190476]
|
|
|
|
mean value: 0.8179009567649118
|
|
|
|
key: train_precision
|
|
value: [1. 0.98404255 0.99459459 0.97368421 1. 1.
|
|
1. 0.99462366 1. 1. ]
|
|
|
|
mean value: 0.9946945014226378
|
|
|
|
key: test_recall
|
|
value: [0.95238095 1. 0.95238095 0.95238095 0.85714286 1.
|
|
0.85 0.85 1. 0.8 ]
|
|
|
|
mean value: 0.9214285714285715
|
|
|
|
key: train_recall
|
|
value: [1. 1. 0.99459459 1. 0.97297297 0.97297297
|
|
0.99462366 0.99462366 0.99462366 0.95698925]
|
|
|
|
mean value: 0.9881400755594305
|
|
|
|
key: test_accuracy
|
|
value: [0.71428571 0.88095238 0.87804878 0.85365854 0.92682927 0.85365854
|
|
0.90243902 0.7804878 0.92682927 0.7804878 ]
|
|
|
|
mean value: 0.8497677119628341
|
|
|
|
key: train_accuracy
|
|
value: [1. 0.99189189 0.99460916 0.98652291 0.98652291 0.98652291
|
|
0.99730458 0.99460916 0.99730458 0.97843666]
|
|
|
|
mean value: 0.9913724775988927
|
|
|
|
key: test_roc_auc
|
|
value: [0.71428571 0.88095238 0.87619048 0.85119048 0.92857143 0.85
|
|
0.90119048 0.78214286 0.92857143 0.78095238]
|
|
|
|
mean value: 0.849404761904762
|
|
|
|
key: train_roc_auc
|
|
value: [1. 0.99189189 0.99460913 0.98655914 0.98648649 0.98648649
|
|
0.99731183 0.99460913 0.99731183 0.97849462]
|
|
|
|
mean value: 0.9913760534728278
|
|
|
|
key: test_jcc
|
|
value: [0.625 0.80769231 0.8 0.76923077 0.85714286 0.77777778
|
|
0.80952381 0.65384615 0.86956522 0.64 ]
|
|
|
|
mean value: 0.7609778892604979
|
|
|
|
key: train_jcc
|
|
value: [1. 0.98404255 0.98924731 0.97368421 0.97297297 0.97297297
|
|
0.99462366 0.98930481 0.99462366 0.95698925]
|
|
|
|
mean value: 0.9828461393465717
|
|
|
|
key: TN
|
|
value: 160
|
|
|
|
mean value: 160.0
|
|
|
|
key: FP
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: FN
|
|
value: 46
|
|
|
|
mean value: 46.0
|
|
|
|
key: TP
|
|
value: 190
|
|
|
|
mean value: 190.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.21
|
|
|
|
Accuracy on Blind test: 0.68
|
|
|
|
Running classifier: 23
|
|
Model_name: Ridge Classifier
|
|
Model func: RidgeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01527357 0.01521778 0.01518393 0.03679013 0.036515 0.04166365
|
|
0.03657699 0.04537487 0.02655697 0.03394699]
|
|
|
|
mean value: 0.03030998706817627
|
|
|
|
key: score_time
|
|
value: [0.01232934 0.01232672 0.01249433 0.02470875 0.02159786 0.02464294
|
|
0.02181315 0.03624797 0.02282119 0.03252983]
|
|
|
|
mean value: 0.02215120792388916
|
|
|
|
key: test_mcc
|
|
value: [1. 0.95346259 0.8047619 0.75714286 1. 0.95238095
|
|
0.95227002 0.65952381 0.90692382 0.85441771]
|
|
|
|
mean value: 0.8840883660082678
|
|
|
|
key: train_mcc
|
|
value: [0.97298719 0.97298719 0.97305937 0.95692987 0.96771006 0.96771006
|
|
0.97306016 0.96765475 0.95692987 0.96771194]
|
|
|
|
mean value: 0.967674046655907
|
|
|
|
key: test_fscore
|
|
value: [1. 0.97560976 0.9047619 0.87804878 1. 0.97560976
|
|
0.97435897 0.82926829 0.95238095 0.92307692]
|
|
|
|
mean value: 0.9413115339944609
|
|
|
|
key: train_fscore
|
|
value: [0.98644986 0.98644986 0.98644986 0.97849462 0.98369565 0.98369565
|
|
0.98652291 0.98387097 0.97837838 0.98378378]
|
|
|
|
mean value: 0.9837791562454985
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.9047619 0.9 1. 1.
|
|
1. 0.80952381 0.90909091 0.94736842]
|
|
|
|
mean value: 0.9470745044429254
|
|
|
|
key: train_precision
|
|
value: [0.98913043 0.98913043 0.98913043 0.97326203 0.98907104 0.98907104
|
|
0.98918919 0.98387097 0.98369565 0.98913043]
|
|
|
|
mean value: 0.9864681656823766
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 0.9047619 0.85714286 1. 0.95238095
|
|
0.95 0.85 1. 0.9 ]
|
|
|
|
mean value: 0.9366666666666668
|
|
|
|
key: train_recall
|
|
value: [0.98378378 0.98378378 0.98378378 0.98378378 0.97837838 0.97837838
|
|
0.98387097 0.98387097 0.97311828 0.97849462]
|
|
|
|
mean value: 0.9811246730601569
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.97619048 0.90243902 0.87804878 1. 0.97560976
|
|
0.97560976 0.82926829 0.95121951 0.92682927]
|
|
|
|
mean value: 0.9415214866434379
|
|
|
|
key: train_accuracy
|
|
value: [0.98648649 0.98648649 0.98652291 0.97843666 0.98382749 0.98382749
|
|
0.98652291 0.98382749 0.97843666 0.98382749]
|
|
|
|
mean value: 0.9838202083485104
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.97619048 0.90238095 0.87857143 1. 0.97619048
|
|
0.975 0.8297619 0.95238095 0.92619048]
|
|
|
|
mean value: 0.9416666666666667
|
|
|
|
key: train_roc_auc
|
|
value: [0.98648649 0.98648649 0.98651555 0.97845103 0.98381285 0.98381285
|
|
0.98653008 0.98382738 0.97845103 0.98384191]
|
|
|
|
mean value: 0.983821563498983
|
|
|
|
key: test_jcc
|
|
value: [1. 0.95238095 0.82608696 0.7826087 1. 0.95238095
|
|
0.95 0.70833333 0.90909091 0.85714286]
|
|
|
|
mean value: 0.8938024656502919
|
|
|
|
key: train_jcc
|
|
value: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:282: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
ros_CV['Data_source'] = 'CV'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:283: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
ros_CV['Resampling'] = rs_ros
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:288: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
ros_BT['Data_source'] = 'BT'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:289: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
ros_BT['Resampling'] = rs_ros
|
|
[0.97326203 0.97326203 0.97326203 0.95789474 0.96791444 0.96791444
|
|
0.97340426 0.96825397 0.95767196 0.96808511]
|
|
|
|
mean value: 0.9680924997732191
|
|
|
|
key: TN
|
|
value: 195
|
|
|
|
mean value: 195.0
|
|
|
|
key: FP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: FN
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: TP
|
|
value: 193
|
|
|
|
mean value: 193.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.9
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 24
|
|
Model_name: Ridge ClassifierCV
|
|
Model func: RidgeClassifierCV(cv=10)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifierCV(cv=10))])
|
|
|
|
key: fit_time
|
|
value: [0.3271625 0.26242065 0.26160622 0.2503655 0.16396499 0.2712121
|
|
0.25434446 0.27765441 0.22024608 0.19233727]
|
|
|
|
mean value: 0.2481314182281494
|
|
|
|
key: score_time
|
|
value: [0.02375269 0.01582861 0.02272272 0.02287126 0.01536274 0.0208714
|
|
0.02181959 0.03867531 0.01217222 0.02051806]
|
|
|
|
mean value: 0.021459460258483887
|
|
|
|
key: test_mcc
|
|
value: [1. 0.95346259 0.8047619 0.75714286 1. 0.95238095
|
|
0.95227002 0.65952381 0.90692382 0.85441771]
|
|
|
|
mean value: 0.8840883660082678
|
|
|
|
key: train_mcc
|
|
value: [0.97298719 0.97298719 0.98384191 0.95692987 0.96771006 0.96771006
|
|
0.97306016 0.9784365 0.95692987 0.96771194]
|
|
|
|
mean value: 0.9698304748061133
|
|
|
|
key: test_fscore
|
|
value: [1. 0.97560976 0.9047619 0.87804878 1. 0.97560976
|
|
0.97435897 0.82926829 0.95238095 0.92307692]
|
|
|
|
mean value: 0.9413115339944609
|
|
|
|
key: train_fscore
|
|
value: [0.98644986 0.98644986 0.99191375 0.97849462 0.98369565 0.98369565
|
|
0.98652291 0.98924731 0.97837838 0.98378378]
|
|
|
|
mean value: 0.984863178867309
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.9047619 0.9 1. 1.
|
|
1. 0.80952381 0.90909091 0.94736842]
|
|
|
|
mean value: 0.9470745044429254
|
|
|
|
key: train_precision
|
|
value: [0.98913043 0.98913043 0.98924731 0.97326203 0.98907104 0.98907104
|
|
0.98918919 0.98924731 0.98369565 0.98913043]
|
|
|
|
mean value: 0.9870174877955137
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 0.9047619 0.85714286 1. 0.95238095
|
|
0.95 0.85 1. 0.9 ]
|
|
|
|
mean value: 0.9366666666666668
|
|
|
|
key: train_recall
|
|
value: [0.98378378 0.98378378 0.99459459 0.98378378 0.97837838 0.97837838
|
|
0.98387097 0.98924731 0.97311828 0.97849462]
|
|
|
|
mean value: 0.9827433885498402
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.97619048 0.90243902 0.87804878 1. 0.97560976
|
|
0.97560976 0.82926829 0.95121951 0.92682927]
|
|
|
|
mean value: 0.9415214866434379
|
|
|
|
key: train_accuracy
|
|
value: [0.98648649 0.98648649 0.99191375 0.97843666 0.98382749 0.98382749
|
|
0.98652291 0.98921833 0.97843666 0.98382749]
|
|
|
|
mean value: 0.9848983754644133
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.97619048 0.90238095 0.87857143 1. 0.97619048
|
|
0.975 0.8297619 0.95238095 0.92619048]
|
|
|
|
mean value: 0.9416666666666667
|
|
|
|
key: train_roc_auc
|
|
value: [0.98648649 0.98648649 0.99192095 0.97845103 0.98381285 0.98381285
|
|
0.98653008 0.98921825 0.97845103 0.98384191]
|
|
|
|
mean value: 0.9849011915140947
|
|
|
|
key: test_jcc
|
|
value: [1. 0.95238095 0.82608696 0.7826087 1. 0.95238095
|
|
0.95 0.70833333 0.90909091 0.85714286]
|
|
|
|
mean value: 0.8938024656502919
|
|
|
|
key: train_jcc
|
|
value: [0.97326203 0.97326203 0.98395722 0.95789474 0.96791444 0.96791444
|
|
0.97340426 0.9787234 0.95767196 0.96808511]
|
|
|
|
mean value: 0.9702089620899317
|
|
|
|
key: TN
|
|
value: 195
|
|
|
|
mean value: 195.0
|
|
|
|
key: FP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: FN
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: TP
|
|
value: 193
|
|
|
|
mean value: 193.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.9
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
PASS: sorting df by score that is mapped onto the order I want
|
|
|
|
==============================================================
|
|
|
|
Running several classification models (n): 24
|
|
List of models:
|
|
('Logistic Regression', LogisticRegression(random_state=42))
|
|
('Logistic RegressionCV', LogisticRegressionCV(random_state=42))
|
|
('Gaussian NB', GaussianNB())
|
|
('Naive Bayes', BernoulliNB())
|
|
('K-Nearest Neighbors', KNeighborsClassifier())
|
|
('SVC', SVC(random_state=42))
|
|
('MLP', MLPClassifier(max_iter=500, random_state=42))
|
|
('Decision Tree', DecisionTreeClassifier(random_state=42))
|
|
('Extra Trees', ExtraTreesClassifier(random_state=42))
|
|
('Extra Tree', ExtraTreeClassifier(random_state=42))
|
|
('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42))
|
|
('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42))
|
|
('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0))
|
|
('LDA', LinearDiscriminantAnalysis())
|
|
('Multinomial', MultinomialNB())
|
|
('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42))
|
|
('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42))
|
|
('AdaBoost Classifier', AdaBoostClassifier(random_state=42))
|
|
('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))
|
|
('Gaussian Process', GaussianProcessClassifier(random_state=42))
|
|
('Gradient Boosting', GradientBoostingClassifier(random_state=42))
|
|
('QDA', QuadraticDiscriminantAnalysis())
|
|
('Ridge Classifier', RidgeClassifier(random_state=42))
|
|
('Ridge ClassifierCV', RidgeClassifierCV(cv=10))
|
|
|
|
================================================================
|
|
|
|
|
|
Running classifier: 1
|
|
Model_name: Logistic Regression
|
|
Model func: LogisticRegression(random_state=42)
|
|
|
|
Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegression(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02878523 0.02737093 0.03174114 0.02732897 0.02855468 0.04491949
|
|
0.0289259 0.03289247 0.02990484 0.02912402]
|
|
|
|
mean value: 0.030954766273498534
|
|
|
|
key: score_time
|
|
value: [0.01180434 0.01176667 0.01328897 0.01191807 0.01183844 0.01215386
|
|
0.01213384 0.01283312 0.01175165 0.01184511]
|
|
|
|
mean value: 0.012133407592773437
|
|
|
|
key: test_mcc
|
|
value: [0.73029674 0.54772256 1. 0.90829511 0.71562645 0.44038551
|
|
0.63305416 1. 0.62641448 0.13483997]
|
|
|
|
mean value: 0.6736634981203286
|
|
|
|
key: train_mcc
|
|
value: [0.88425952 0.85282059 0.8848558 0.93717105 0.88486842 0.85339912
|
|
0.86391052 0.87454765 0.86509383 0.89528509]
|
|
|
|
mean value: 0.8796211592825314
|
|
|
|
key: test_fscore
|
|
value: [0.86956522 0.7826087 1. 0.94736842 0.84210526 0.72727273
|
|
0.8 1. 0.83333333 0.64 ]
|
|
|
|
mean value: 0.8442253657860064
|
|
|
|
key: train_fscore
|
|
value: [0.94240838 0.92708333 0.94300518 0.96875 0.94240838 0.92708333
|
|
0.93121693 0.9375 0.93333333 0.94736842]
|
|
|
|
mean value: 0.9400157287543415
|
|
|
|
key: test_precision
|
|
value: [0.83333333 0.75 1. 1. 0.88888889 0.66666667
|
|
0.88888889 1. 0.76923077 0.57142857]
|
|
|
|
mean value: 0.8368437118437118
|
|
|
|
key: train_precision
|
|
value: [0.9375 0.91752577 0.93814433 0.96875 0.94736842 0.92708333
|
|
0.93617021 0.92783505 0.91 0.94736842]
|
|
|
|
mean value: 0.9357745542843728
|
|
|
|
key: test_recall
|
|
value: [0.90909091 0.81818182 1. 0.9 0.8 0.8
|
|
0.72727273 1. 0.90909091 0.72727273]
|
|
|
|
mean value: 0.859090909090909
|
|
|
|
key: train_recall
|
|
value: [0.94736842 0.93684211 0.94791667 0.96875 0.9375 0.92708333
|
|
0.92631579 0.94736842 0.95789474 0.94736842]
|
|
|
|
mean value: 0.9444407894736843
|
|
|
|
key: test_accuracy
|
|
value: [0.86363636 0.77272727 1. 0.95238095 0.85714286 0.71428571
|
|
0.80952381 1. 0.80952381 0.57142857]
|
|
|
|
mean value: 0.8350649350649351
|
|
|
|
key: train_accuracy
|
|
value: [0.94210526 0.92631579 0.94240838 0.96858639 0.94240838 0.92670157
|
|
0.93193717 0.93717277 0.93193717 0.94764398]
|
|
|
|
mean value: 0.9397216864149904
|
|
|
|
key: test_roc_auc
|
|
value: [0.86363636 0.77272727 1. 0.95 0.85454545 0.71818182
|
|
0.81363636 1. 0.80454545 0.56363636]
|
|
|
|
mean value: 0.8340909090909092
|
|
|
|
key: train_roc_auc
|
|
value: [0.94210526 0.92631579 0.94237939 0.96858553 0.94243421 0.92669956
|
|
0.93190789 0.93722588 0.93207237 0.94764254]
|
|
|
|
mean value: 0.9397368421052631
|
|
|
|
key: test_jcc
|
|
value: [0.76923077 0.64285714 1. 0.9 0.72727273 0.57142857
|
|
0.66666667 1. 0.71428571 0.47058824]
|
|
|
|
mean value: 0.7462329827035709
|
|
|
|
key: train_jcc
|
|
value: [0.89108911 0.86407767 0.89215686 0.93939394 0.89108911 0.86407767
|
|
0.87128713 0.88235294 0.875 0.9 ]
|
|
|
|
mean value: 0.8870524429655987
|
|
|
|
key: TN
|
|
value: 86
|
|
|
|
mean value: 86.0
|
|
|
|
key: FP
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: FN
|
|
value: 20
|
|
|
|
mean value: 20.0
|
|
|
|
key: TP
|
|
value: 91
|
|
|
|
mean value: 91.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.68
|
|
|
|
Accuracy on Blind test: 0.85
|
|
|
|
Running classifier: 2
|
|
Model_name: Logistic RegressionCV
|
|
Model func: LogisticRegressionCV(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegressionCV(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.8474319 0.64568019 0.65819812 0.74121666 0.64531708 0.66669083
|
|
0.67523885 0.69857788 0.63087988 0.70886588]
|
|
|
|
mean value: 0.6918097257614135
|
|
|
|
key: score_time
|
|
value: [0.01244688 0.01247382 0.01246166 0.01256037 0.01257372 0.0124898
|
|
0.01269221 0.01252818 0.01268768 0.01263762]
|
|
|
|
mean value: 0.012555193901062012
|
|
|
|
key: test_mcc
|
|
value: [1. 0.83205029 1. 0.90829511 0.90909091 0.80909091
|
|
0.74795759 1. 0.82275335 0.58630197]
|
|
|
|
mean value: 0.8615540131941046
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.9 1. 0.94736842 0.95238095 0.9
|
|
0.84210526 1. 0.91666667 0.81481481]
|
|
|
|
mean value: 0.927333611807296
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 1. 0.90909091 0.9
|
|
1. 1. 0.84615385 0.6875 ]
|
|
|
|
mean value: 0.9342744755244755
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.81818182 1. 0.9 1. 0.9
|
|
0.72727273 1. 1. 1. ]
|
|
|
|
mean value: 0.9345454545454546
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.90909091 1. 0.95238095 0.95238095 0.9047619
|
|
0.85714286 1. 0.9047619 0.76190476]
|
|
|
|
mean value: 0.9242424242424242
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.90909091 1. 0.95 0.95454545 0.90454545
|
|
0.86363636 1. 0.9 0.75 ]
|
|
|
|
mean value: 0.9231818181818182
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.81818182 1. 0.9 0.90909091 0.81818182
|
|
0.72727273 1. 0.84615385 0.6875 ]
|
|
|
|
mean value: 0.8706381118881119
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 97
|
|
|
|
mean value: 97.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: TP
|
|
value: 98
|
|
|
|
mean value: 98.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.78
|
|
|
|
Accuracy on Blind test: 0.9
|
|
|
|
Running classifier: 3
|
|
Model_name: Gaussian NB
|
|
Model func: GaussianNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianNB())])
|
|
|
|
key: fit_time
|
|
value: [0.01245213 0.01243806 0.00974703 0.00927186 0.0097537 0.01013827
|
|
0.00901365 0.00911999 0.00905824 0.00975323]
|
|
|
|
mean value: 0.010074615478515625
|
|
|
|
key: score_time
|
|
value: [0.01222992 0.01084161 0.00986362 0.00998068 0.00956964 0.0091145
|
|
0.00861526 0.00893164 0.0087111 0.00935578]
|
|
|
|
mean value: 0.00972137451171875
|
|
|
|
key: test_mcc
|
|
value: [0.20412415 0.18898224 0.82572282 0.14545455 0.55161872 0.14545455
|
|
0.44038551 0.43007562 0.03739788 0.24120908]
|
|
|
|
mean value: 0.32104250973123794
|
|
|
|
key: train_mcc
|
|
value: [0.41809986 0.44147316 0.43405367 0.45081174 0.45172183 0.400897
|
|
0.48801375 0.44832596 0.47224501 0.41856152]
|
|
|
|
mean value: 0.4424203495544211
|
|
|
|
key: test_fscore
|
|
value: [0.66666667 0.64 0.90909091 0.57142857 0.7826087 0.57142857
|
|
0.7 0.75 0.58333333 0.69230769]
|
|
|
|
mean value: 0.6866864439907918
|
|
|
|
key: train_fscore
|
|
value: [0.73076923 0.74285714 0.74178404 0.75229358 0.74641148 0.72641509
|
|
0.73224044 0.73631841 0.75121951 0.7254902 ]
|
|
|
|
mean value: 0.7385799120152144
|
|
|
|
key: test_precision
|
|
value: [0.5625 0.57142857 0.83333333 0.54545455 0.69230769 0.54545455
|
|
0.77777778 0.69230769 0.53846154 0.6 ]
|
|
|
|
mean value: 0.6359025696525695
|
|
|
|
key: train_precision
|
|
value: [0.67256637 0.67826087 0.67521368 0.67213115 0.69026549 0.6637931
|
|
0.76136364 0.69811321 0.7 0.67889908]
|
|
|
|
mean value: 0.6890606580654846
|
|
|
|
key: test_recall
|
|
value: [0.81818182 0.72727273 1. 0.6 0.9 0.6
|
|
0.63636364 0.81818182 0.63636364 0.81818182]
|
|
|
|
mean value: 0.7554545454545455
|
|
|
|
key: train_recall
|
|
value: [0.8 0.82105263 0.82291667 0.85416667 0.8125 0.80208333
|
|
0.70526316 0.77894737 0.81052632 0.77894737]
|
|
|
|
mean value: 0.7986403508771931
|
|
|
|
key: test_accuracy
|
|
value: [0.59090909 0.59090909 0.9047619 0.57142857 0.76190476 0.57142857
|
|
0.71428571 0.71428571 0.52380952 0.61904762]
|
|
|
|
mean value: 0.6562770562770563
|
|
|
|
key: train_accuracy
|
|
value: [0.70526316 0.71578947 0.71204188 0.71727749 0.72251309 0.69633508
|
|
0.7434555 0.72251309 0.73298429 0.70680628]
|
|
|
|
mean value: 0.7174979333149627
|
|
|
|
key: test_roc_auc
|
|
value: [0.59090909 0.59090909 0.90909091 0.57272727 0.76818182 0.57272727
|
|
0.71818182 0.70909091 0.51818182 0.60909091]
|
|
|
|
mean value: 0.655909090909091
|
|
|
|
key: train_roc_auc
|
|
value: [0.70526316 0.71578947 0.71145833 0.71655702 0.72203947 0.69577851
|
|
0.74325658 0.72280702 0.73338816 0.70718202]
|
|
|
|
mean value: 0.7173519736842104
|
|
|
|
key: test_jcc
|
|
value: [0.5 0.47058824 0.83333333 0.4 0.64285714 0.4
|
|
0.53846154 0.6 0.41176471 0.52941176]
|
|
|
|
mean value: 0.5326416720534368
|
|
|
|
key: train_jcc
|
|
value: [0.57575758 0.59090909 0.58955224 0.60294118 0.59541985 0.57037037
|
|
0.57758621 0.58267717 0.6015625 0.56923077]
|
|
|
|
mean value: 0.585600694112349
|
|
|
|
key: TN
|
|
value: 59
|
|
|
|
mean value: 59.0
|
|
|
|
key: FP
|
|
value: 26
|
|
|
|
mean value: 26.0
|
|
|
|
key: FN
|
|
value: 47
|
|
|
|
mean value: 47.0
|
|
|
|
key: TP
|
|
value: 80
|
|
|
|
mean value: 80.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.35
|
|
|
|
Accuracy on Blind test: 0.71
|
|
|
|
Running classifier: 4
|
|
Model_name: Naive Bayes
|
|
Model func: BernoulliNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', BernoulliNB())])
|
|
|
|
key: fit_time
|
|
value: [0.00917554 0.00898218 0.00888371 0.00896883 0.00891352 0.00882316
|
|
0.00896239 0.00886655 0.00893283 0.00890827]
|
|
|
|
mean value: 0.00894169807434082
|
|
|
|
key: score_time
|
|
value: [0.00879407 0.00865746 0.00865412 0.00859976 0.00862265 0.00858164
|
|
0.00859523 0.00866842 0.00866795 0.0087111 ]
|
|
|
|
mean value: 0.008655238151550292
|
|
|
|
key: test_mcc
|
|
value: [ 0.09245003 0.18898224 -0.05504819 0.13483997 0.43007562 -0.23636364
|
|
0.18090681 0.13762047 -0.35527986 -0.03015113]
|
|
|
|
mean value: 0.0488032319211866
|
|
|
|
key: train_mcc
|
|
value: [0.50386393 0.41134755 0.45548246 0.47824733 0.44500555 0.51226436
|
|
0.48461189 0.55362565 0.48123065 0.53504756]
|
|
|
|
mean value: 0.48607269365804306
|
|
|
|
key: test_fscore
|
|
value: [0.5 0.52631579 0.42105263 0.47058824 0.66666667 0.38095238
|
|
0.47058824 0.60869565 0.41666667 0.35294118]
|
|
|
|
mean value: 0.4814467434571082
|
|
|
|
key: train_fscore
|
|
value: [0.72093023 0.69565217 0.72916667 0.70175439 0.68604651 0.77073171
|
|
0.70930233 0.73809524 0.71590909 0.74285714]
|
|
|
|
mean value: 0.7210445475490609
|
|
|
|
key: test_precision
|
|
value: [0.55555556 0.625 0.44444444 0.57142857 0.75 0.36363636
|
|
0.66666667 0.58333333 0.38461538 0.5 ]
|
|
|
|
mean value: 0.544468031968032
|
|
|
|
key: train_precision
|
|
value: [0.80519481 0.71910112 0.72916667 0.8 0.77631579 0.72477064
|
|
0.79220779 0.84931507 0.77777778 0.8125 ]
|
|
|
|
mean value: 0.7786349665611217
|
|
|
|
key: test_recall
|
|
value: [0.45454545 0.45454545 0.4 0.4 0.6 0.4
|
|
0.36363636 0.63636364 0.45454545 0.27272727]
|
|
|
|
mean value: 0.44363636363636355
|
|
|
|
key: train_recall
|
|
value: [0.65263158 0.67368421 0.72916667 0.625 0.61458333 0.82291667
|
|
0.64210526 0.65263158 0.66315789 0.68421053]
|
|
|
|
mean value: 0.6760087719298246
|
|
|
|
key: test_accuracy
|
|
value: [0.54545455 0.59090909 0.47619048 0.57142857 0.71428571 0.38095238
|
|
0.57142857 0.57142857 0.33333333 0.47619048]
|
|
|
|
mean value: 0.523160173160173
|
|
|
|
key: train_accuracy
|
|
value: [0.74736842 0.70526316 0.72774869 0.73298429 0.71727749 0.7539267
|
|
0.7382199 0.76963351 0.7382199 0.76439791]
|
|
|
|
mean value: 0.739503995591072
|
|
|
|
key: test_roc_auc
|
|
value: [0.54545455 0.59090909 0.47272727 0.56363636 0.70909091 0.38181818
|
|
0.58181818 0.56818182 0.32727273 0.48636364]
|
|
|
|
mean value: 0.5227272727272727
|
|
|
|
key: train_roc_auc
|
|
value: [0.74736842 0.70526316 0.72774123 0.73355263 0.71781798 0.7535636
|
|
0.7377193 0.76902412 0.73782895 0.76398026]
|
|
|
|
mean value: 0.7393859649122807
|
|
|
|
key: test_jcc
|
|
value: [0.33333333 0.35714286 0.26666667 0.30769231 0.5 0.23529412
|
|
0.30769231 0.4375 0.26315789 0.21428571]
|
|
|
|
mean value: 0.32227651991970874
|
|
|
|
key: train_jcc
|
|
value: [0.56363636 0.53333333 0.57377049 0.54054054 0.52212389 0.62698413
|
|
0.54954955 0.58490566 0.55752212 0.59090909]
|
|
|
|
mean value: 0.5643275174832757
|
|
|
|
key: TN
|
|
value: 64
|
|
|
|
mean value: 64.0
|
|
|
|
key: FP
|
|
value: 59
|
|
|
|
mean value: 59.0
|
|
|
|
key: FN
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: TP
|
|
value: 47
|
|
|
|
mean value: 47.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.06
|
|
|
|
Accuracy on Blind test: 0.49
|
|
|
|
Running classifier: 5
|
|
Model_name: K-Nearest Neighbors
|
|
Model func: KNeighborsClassifier()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', KNeighborsClassifier())])
|
|
|
|
key: fit_time
|
|
value: [0.00854278 0.00899529 0.00837398 0.00849628 0.00835133 0.00847888
|
|
0.00856066 0.00836992 0.00841522 0.00843906]
|
|
|
|
mean value: 0.008502340316772461
|
|
|
|
key: score_time
|
|
value: [0.01104236 0.01535344 0.0098412 0.00971603 0.00971007 0.00972986
|
|
0.00974631 0.00971174 0.00977564 0.00984955]
|
|
|
|
mean value: 0.01044762134552002
|
|
|
|
key: test_mcc
|
|
value: [ 0.09245003 0.36514837 0.23636364 0.63305416 0.52727273 0.03015113
|
|
0.14545455 0.43007562 -0.05504819 -0.05504819]
|
|
|
|
mean value: 0.2349873851761913
|
|
|
|
key: train_mcc
|
|
value: [0.58950634 0.56845255 0.51847791 0.52878605 0.54973133 0.56027205
|
|
0.55060406 0.53932217 0.61350202 0.55103649]
|
|
|
|
mean value: 0.556969097894801
|
|
|
|
key: test_fscore
|
|
value: [0.5 0.69565217 0.6 0.81818182 0.76190476 0.375
|
|
0.57142857 0.75 0.52173913 0.52173913]
|
|
|
|
mean value: 0.611564558629776
|
|
|
|
key: train_fscore
|
|
value: [0.79581152 0.78306878 0.75789474 0.76683938 0.77720207 0.78350515
|
|
0.77948718 0.76595745 0.81025641 0.76502732]
|
|
|
|
mean value: 0.7785050002608345
|
|
|
|
key: test_precision
|
|
value: [0.55555556 0.66666667 0.6 0.75 0.72727273 0.5
|
|
0.6 0.69230769 0.5 0.5 ]
|
|
|
|
mean value: 0.6091802641802643
|
|
|
|
key: train_precision
|
|
value: [0.79166667 0.78723404 0.76595745 0.7628866 0.77319588 0.7755102
|
|
0.76 0.77419355 0.79 0.79545455]
|
|
|
|
mean value: 0.7776098928178448
|
|
|
|
key: test_recall
|
|
value: [0.45454545 0.72727273 0.6 0.9 0.8 0.3
|
|
0.54545455 0.81818182 0.54545455 0.54545455]
|
|
|
|
mean value: 0.6236363636363637
|
|
|
|
key: train_recall
|
|
value: [0.8 0.77894737 0.75 0.77083333 0.78125 0.79166667
|
|
0.8 0.75789474 0.83157895 0.73684211]
|
|
|
|
mean value: 0.7799013157894736
|
|
|
|
key: test_accuracy
|
|
value: [0.54545455 0.68181818 0.61904762 0.80952381 0.76190476 0.52380952
|
|
0.57142857 0.71428571 0.47619048 0.47619048]
|
|
|
|
mean value: 0.6179653679653679
|
|
|
|
key: train_accuracy
|
|
value: [0.79473684 0.78421053 0.7591623 0.76439791 0.77486911 0.78010471
|
|
0.77486911 0.76963351 0.80628272 0.77486911]
|
|
|
|
mean value: 0.7783135850096445
|
|
|
|
key: test_roc_auc
|
|
value: [0.54545455 0.68181818 0.61818182 0.81363636 0.76363636 0.51363636
|
|
0.57272727 0.70909091 0.47272727 0.47272727]
|
|
|
|
mean value: 0.6163636363636364
|
|
|
|
key: train_roc_auc
|
|
value: [0.79473684 0.78421053 0.75921053 0.76436404 0.77483553 0.78004386
|
|
0.775 0.76957237 0.80641447 0.77467105]
|
|
|
|
mean value: 0.7783059210526316
|
|
|
|
key: test_jcc
|
|
value: [0.33333333 0.53333333 0.42857143 0.69230769 0.61538462 0.23076923
|
|
0.4 0.6 0.35294118 0.35294118]
|
|
|
|
mean value: 0.453958198664081
|
|
|
|
key: train_jcc
|
|
value: [0.66086957 0.64347826 0.61016949 0.62184874 0.63559322 0.6440678
|
|
0.63865546 0.62068966 0.68103448 0.61946903]
|
|
|
|
mean value: 0.6375875700721912
|
|
|
|
key: TN
|
|
value: 65
|
|
|
|
mean value: 65.0
|
|
|
|
key: FP
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: FN
|
|
value: 41
|
|
|
|
mean value: 41.0
|
|
|
|
key: TP
|
|
value: 66
|
|
|
|
mean value: 66.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.26
|
|
|
|
Accuracy on Blind test: 0.62
|
|
|
|
Running classifier: 6
|
|
Model_name: SVC
|
|
Model func: SVC(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SVC(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01135945 0.01094675 0.01100111 0.01088858 0.01091003 0.01119184
|
|
0.01110053 0.01104259 0.01101637 0.01096416]
|
|
|
|
mean value: 0.011042141914367675
|
|
|
|
key: score_time
|
|
value: [0.00942183 0.00925756 0.00914049 0.00912261 0.00915551 0.00912094
|
|
0.00912237 0.00918698 0.0099318 0.00918579]
|
|
|
|
mean value: 0.009264588356018066
|
|
|
|
key: test_mcc
|
|
value: [0.54772256 0.46225016 0.52295779 0.71818182 0.52727273 0.23373675
|
|
0.55161872 0.45226702 0.03015113 0.13483997]
|
|
|
|
mean value: 0.4180998650339932
|
|
|
|
key: train_mcc
|
|
value: [0.65266774 0.6843622 0.63426775 0.73019067 0.71757751 0.68585526
|
|
0.66740308 0.60269927 0.72927062 0.68652637]
|
|
|
|
mean value: 0.6790820480507576
|
|
|
|
key: test_fscore
|
|
value: [0.76190476 0.75 0.73684211 0.85714286 0.76190476 0.55555556
|
|
0.73684211 0.76923077 0.61538462 0.64 ]
|
|
|
|
mean value: 0.7184807531649636
|
|
|
|
key: train_fscore
|
|
value: [0.82539683 0.84375 0.82233503 0.87 0.86153846 0.84375
|
|
0.83838384 0.80412371 0.86734694 0.84536082]
|
|
|
|
mean value: 0.842198562555782
|
|
|
|
key: test_precision
|
|
value: [0.8 0.69230769 0.77777778 0.81818182 0.72727273 0.625
|
|
0.875 0.66666667 0.53333333 0.57142857]
|
|
|
|
mean value: 0.7086968586968586
|
|
|
|
key: train_precision
|
|
value: [0.82978723 0.83505155 0.8019802 0.83653846 0.84848485 0.84375
|
|
0.80582524 0.78787879 0.84158416 0.82828283]
|
|
|
|
mean value: 0.8259163305773323
|
|
|
|
key: test_recall
|
|
value: [0.72727273 0.81818182 0.7 0.9 0.8 0.5
|
|
0.63636364 0.90909091 0.72727273 0.72727273]
|
|
|
|
mean value: 0.7445454545454546
|
|
|
|
key: train_recall
|
|
value: [0.82105263 0.85263158 0.84375 0.90625 0.875 0.84375
|
|
0.87368421 0.82105263 0.89473684 0.86315789]
|
|
|
|
mean value: 0.8595065789473683
|
|
|
|
key: test_accuracy
|
|
value: [0.77272727 0.72727273 0.76190476 0.85714286 0.76190476 0.61904762
|
|
0.76190476 0.71428571 0.52380952 0.57142857]
|
|
|
|
mean value: 0.7071428571428571
|
|
|
|
key: train_accuracy
|
|
value: [0.82631579 0.84210526 0.81675393 0.86387435 0.85863874 0.84293194
|
|
0.83246073 0.80104712 0.86387435 0.84293194]
|
|
|
|
mean value: 0.8390934141636814
|
|
|
|
key: test_roc_auc
|
|
value: [0.77272727 0.72727273 0.75909091 0.85909091 0.76363636 0.61363636
|
|
0.76818182 0.70454545 0.51363636 0.56363636]
|
|
|
|
mean value: 0.7045454545454545
|
|
|
|
key: train_roc_auc
|
|
value: [0.82631579 0.84210526 0.81661184 0.86365132 0.85855263 0.84292763
|
|
0.83267544 0.80115132 0.86403509 0.84303728]
|
|
|
|
mean value: 0.8391063596491228
|
|
|
|
key: test_jcc
|
|
value: [0.61538462 0.6 0.58333333 0.75 0.61538462 0.38461538
|
|
0.58333333 0.625 0.44444444 0.47058824]
|
|
|
|
mean value: 0.5672083961789844
|
|
|
|
key: train_jcc
|
|
value: [0.7027027 0.72972973 0.69827586 0.7699115 0.75675676 0.72972973
|
|
0.72173913 0.67241379 0.76576577 0.73214286]
|
|
|
|
mean value: 0.7279167831859517
|
|
|
|
key: TN
|
|
value: 71
|
|
|
|
mean value: 71.0
|
|
|
|
key: FP
|
|
value: 27
|
|
|
|
mean value: 27.0
|
|
|
|
key: FN
|
|
value: 35
|
|
|
|
mean value: 35.0
|
|
|
|
key: TP
|
|
value: 79
|
|
|
|
mean value: 79.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value:/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.43
|
|
|
|
Accuracy on Blind test: 0.74
|
|
|
|
Running classifier: 7
|
|
Model_name: MLP
|
|
Model func: MLPClassifier(max_iter=500, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MLPClassifier(max_iter=500, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.69027948 1.232337 0.704772 0.73980832 0.75131059 0.69982934
|
|
0.72087932 0.74564934 0.70578551 0.71666169]
|
|
|
|
mean value: 0.770731258392334
|
|
|
|
key: score_time
|
|
value: [0.01450229 0.0136013 0.01349425 0.01363969 0.01377344 0.01384234
|
|
0.01815867 0.01383829 0.01365829 0.01377439]
|
|
|
|
mean value: 0.014228296279907227
|
|
|
|
key: test_mcc
|
|
value: [ 0.45454545 0.54772256 0.82572282 0.66332496 0.61818182 0.23636364
|
|
0.63305416 0.66332496 0.23636364 -0.08528029]
|
|
|
|
mean value: 0.4793323721352055
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.72727273 0.7826087 0.90909091 0.75 0.8 0.6
|
|
0.8 0.84615385 0.63636364 0.59259259]
|
|
|
|
mean value: 0.7444082407125886
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.72727273 0.75 0.83333333 1. 0.8 0.6
|
|
0.88888889 0.73333333 0.63636364 0.5 ]
|
|
|
|
mean value: 0.746919191919192
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.72727273 0.81818182 1. 0.6 0.8 0.6
|
|
0.72727273 1. 0.63636364 0.72727273]
|
|
|
|
mean value: 0.7636363636363637
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.72727273 0.77272727 0.9047619 0.80952381 0.80952381 0.61904762
|
|
0.80952381 0.80952381 0.61904762 0.47619048]
|
|
|
|
mean value: 0.7357142857142858
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.72727273 0.77272727 0.90909091 0.8 0.80909091 0.61818182
|
|
0.81363636 0.8 0.61818182 0.46363636]
|
|
|
|
mean value: 0.7331818181818182
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.57142857 0.64285714 0.83333333 0.6 0.66666667 0.42857143
|
|
0.66666667 0.73333333 0.46666667 0.42105263]
|
|
|
|
mean value: 0.6030576441102757
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 75
|
|
|
|
mean value: 75.0
|
|
|
|
key: FP
|
|
value: 25
|
|
|
|
mean value: 25.0
|
|
|
|
key: FN
|
|
value: 31
|
|
|
|
mean value: 31.0
|
|
|
|
key: TP
|
|
value: 81
|
|
|
|
mean value: 81.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.63
|
|
|
|
Accuracy on Blind test: 0.84
|
|
|
|
Running classifier: 8
|
|
Model_name: Decision Tree
|
|
Model func: DecisionTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', DecisionTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.0173099 0.01580215 0.01309872 0.01248264 0.01198626 0.01239681
|
|
0.01150823 0.01147032 0.0118463 0.01268888]
|
|
|
|
mean value: 0.013059020042419434
|
|
|
|
key: score_time
|
|
value: [0.01170039 0.00911403 0.0089891 0.00858307 0.00857282 0.00855732
|
|
0.00863171 0.00857186 0.00849724 0.00859404]
|
|
|
|
mean value: 0.008981156349182128
|
|
|
|
key: test_mcc
|
|
value: [1. 1. 0.80909091 0.90829511 0.80909091 0.90829511
|
|
0.71818182 0.90909091 0.80909091 1. ]
|
|
|
|
mean value: 0.887113566700395
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 1. 0.9 0.94736842 0.9 0.94736842
|
|
0.85714286 0.95238095 0.90909091 1. ]
|
|
|
|
mean value: 0.9413351560719981
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.9 1. 0.9 1.
|
|
0.9 1. 0.90909091 1. ]
|
|
|
|
mean value: 0.9609090909090907
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.9 0.9 0.9 0.9
|
|
0.81818182 0.90909091 0.90909091 1. ]
|
|
|
|
mean value: 0.9236363636363636
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 1. 0.9047619 0.95238095 0.9047619 0.95238095
|
|
0.85714286 0.95238095 0.9047619 1. ]
|
|
|
|
mean value: 0.9428571428571428
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 1. 0.90454545 0.95 0.90454545 0.95
|
|
0.85909091 0.95454545 0.90454545 1. ]
|
|
|
|
mean value: 0.9427272727272727
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 1. 0.81818182 0.9 0.81818182 0.9
|
|
0.75 0.90909091 0.83333333 1. ]
|
|
|
|
mean value: 0.8928787878787879
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 102
|
|
|
|
mean value: 102.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: TP
|
|
value: 98
|
|
|
|
mean value: 98.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.78
|
|
|
|
Accuracy on Blind test: 0.89
|
|
|
|
Running classifier: 9
|
|
Model_name: Extra Trees
|
|
Model func: ExtraTreesClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreesClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.09913039 0.09283686 0.09363127 0.09323001 0.09556413 0.09252
|
|
0.09354663 0.09332609 0.09272718 0.09323263]
|
|
|
|
mean value: 0.09397451877593994
|
|
|
|
key: score_time
|
|
value: [0.01736093 0.01741695 0.01741195 0.01764488 0.01735115 0.01733899
|
|
0.01745343 0.01734233 0.017344 0.01745343]
|
|
|
|
mean value: 0.01741180419921875
|
|
|
|
key: test_mcc
|
|
value: [0.36514837 0.54772256 0.90829511 0.71818182 0.80909091 0.33709993
|
|
0.61818182 0.80909091 0.23636364 0.23636364]
|
|
|
|
mean value: 0.5585538693908872
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.66666667 0.7826087 0.94736842 0.85714286 0.9 0.58823529
|
|
0.81818182 0.90909091 0.63636364 0.63636364]
|
|
|
|
mean value: 0.7742021934631977
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.7 0.75 1. 0.81818182 0.9 0.71428571
|
|
0.81818182 0.90909091 0.63636364 0.63636364]
|
|
|
|
mean value: 0.7882467532467533
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.63636364 0.81818182 0.9 0.9 0.9 0.5
|
|
0.81818182 0.90909091 0.63636364 0.63636364]
|
|
|
|
mean value: 0.7654545454545455
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.68181818 0.77272727 0.95238095 0.85714286 0.9047619 0.66666667
|
|
0.80952381 0.9047619 0.61904762 0.61904762]
|
|
|
|
mean value: 0.7787878787878787
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.68181818 0.77272727 0.95 0.85909091 0.90454545 0.65909091
|
|
0.80909091 0.90454545 0.61818182 0.61818182]
|
|
|
|
mean value: 0.7777272727272727
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.5 0.64285714 0.9 0.75 0.81818182 0.41666667
|
|
0.69230769 0.83333333 0.46666667 0.46666667]
|
|
|
|
mean value: 0.6486679986679986
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 84
|
|
|
|
mean value: 84.0
|
|
|
|
key: FP
|
|
value: 25
|
|
|
|
mean value: 25.0
|
|
|
|
key: FN
|
|
value: 22
|
|
|
|
mean value: 22.0
|
|
|
|
key: TP
|
|
value: 81
|
|
|
|
mean value: 81.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.52
|
|
|
|
Accuracy on Blind test: 0.75
|
|
|
|
Running classifier: 10
|
|
Model_name: Extra Tree
|
|
Model func: ExtraTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.008847 0.00880098 0.00887179 0.00869989 0.0088563 0.00886416
|
|
0.00878453 0.00897479 0.00888658 0.00894952]
|
|
|
|
mean value: 0.008853554725646973
|
|
|
|
key: score_time
|
|
value: [0.00852537 0.00856757 0.00852895 0.00844431 0.00855374 0.00850129
|
|
0.00854421 0.00863028 0.00860786 0.00860882]
|
|
|
|
mean value: 0.008551239967346191
|
|
|
|
key: test_mcc
|
|
value: [ 0.09245003 0.36514837 0.42727273 0.43007562 0.71818182 -0.15894099
|
|
0.42727273 -0.06741999 0.13483997 -0.04545455]
|
|
|
|
mean value: 0.23234257449958196
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.5 0.69565217 0.7 0.66666667 0.85714286 0.33333333
|
|
0.72727273 0.56 0.64 0.47619048]
|
|
|
|
mean value: 0.6156258234519104
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.55555556 0.66666667 0.7 0.75 0.81818182 0.375
|
|
0.72727273 0.5 0.57142857 0.5 ]
|
|
|
|
mean value: 0.6164105339105339
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.45454545 0.72727273 0.7 0.6 0.9 0.3
|
|
0.72727273 0.63636364 0.72727273 0.45454545]
|
|
|
|
mean value: 0.6227272727272727
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.54545455 0.68181818 0.71428571 0.71428571 0.85714286 0.42857143
|
|
0.71428571 0.47619048 0.57142857 0.47619048]
|
|
|
|
mean value: 0.6179653679653679
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.54545455 0.68181818 0.71363636 0.70909091 0.85909091 0.42272727
|
|
0.71363636 0.46818182 0.56363636 0.47727273]
|
|
|
|
mean value: 0.6154545454545455
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.33333333 0.53333333 0.53846154 0.5 0.75 0.2
|
|
0.57142857 0.38888889 0.47058824 0.3125 ]
|
|
|
|
mean value: 0.4598533900739783
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 65
|
|
|
|
mean value: 65.0
|
|
|
|
key: FP
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: FN
|
|
value: 41
|
|
|
|
mean value: 41.0
|
|
|
|
key: TP
|
|
value: 66
|
|
|
|
mean value: 66.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.11
|
|
|
|
Accuracy on Blind test: 0.55
|
|
|
|
Running classifier: 11
|
|
Model_name: Random Forest
|
|
Model func: RandomForestClassifier(n_estimators=1000, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(n_estimators=1000, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
[1.24118233 1.25259256 1.26126266 1.25328112 1.22599387 1.22450852
|
|
1.22039127 1.22889853 1.221771 1.22961187]
|
|
|
|
mean value: 1.2359493732452393
|
|
|
|
key: score_time
|
|
value: [0.09635234 0.09605432 0.09466815 0.08944607 0.09384155 0.09182
|
|
0.08958817 0.09731269 0.09603953 0.09355998]
|
|
|
|
mean value: 0.09386827945709228
|
|
|
|
key: test_mcc
|
|
value: [0.83205029 0.56694671 0.90829511 0.90909091 0.90909091 0.61818182
|
|
0.63305416 0.90909091 0.80909091 0.52295779]
|
|
|
|
mean value: 0.7617849518009066
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.91666667 0.8 0.94736842 0.95238095 0.95238095 0.8
|
|
0.8 0.95238095 0.90909091 0.7826087 ]
|
|
|
|
mean value: 0.8812877549605238
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.84615385 0.71428571 1. 0.90909091 0.90909091 0.8
|
|
0.88888889 1. 0.90909091 0.75 ]
|
|
|
|
mean value: 0.8726601176601175
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.90909091 0.9 1. 1. 0.8
|
|
0.72727273 0.90909091 0.90909091 0.81818182]
|
|
|
|
mean value: 0.8972727272727272
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.90909091 0.77272727 0.95238095 0.95238095 0.95238095 0.80952381
|
|
0.80952381 0.95238095 0.9047619 0.76190476]
|
|
|
|
mean value: 0.8777056277056279
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.90909091 0.77272727 0.95 0.95454545 0.95454545 0.80909091
|
|
0.81363636 0.95454545 0.90454545 0.75909091]
|
|
|
|
mean value: 0.8781818181818183
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.84615385 0.66666667 0.9 0.90909091 0.90909091 0.66666667
|
|
0.66666667 0.90909091 0.83333333 0.64285714]
|
|
|
|
mean value: 0.794961704961705
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 91
|
|
|
|
mean value: 91.0
|
|
|
|
key: FP
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: FN
|
|
value: 15
|
|
|
|
mean value: 15.0
|
|
|
|
key: TP
|
|
value: 95
|
|
|
|
mean value: 95.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.8
|
|
|
|
Accuracy on Blind test: 0.9
|
|
|
|
Running classifier: 12
|
|
Model_name: Random Forest2
|
|
Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...age_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10,
|
|
oob_score=True, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.83920407 0.89417028 0.90412354 0.90621185 0.87500978 0.87239695
|
|
0.8454318 0.97728825 0.8785305 0.85419989]
|
|
|
|
mean value: 0.8846566915512085
|
|
|
|
key: score_time
|
|
value: [0.15499449 0.18715811 0.16653514 0.1395185 0.19633818 0.17854738
|
|
0.18856549 0.19819713 0.19455981 0.18306184]
|
|
|
|
mean value: 0.17874760627746583
|
|
|
|
key: test_mcc
|
|
value: [0.75592895 0.46225016 0.90829511 0.82572282 0.90909091 0.52727273
|
|
0.74795759 0.90909091 0.80909091 0.42727273]
|
|
|
|
mean value: 0.7281972813438443
|
|
|
|
key: train_mcc
|
|
value: [0.95810708 0.95874497 0.96863692 0.93798081 0.94810203 0.93798081
|
|
0.94811895 0.95832877 0.94769737 0.95832877]
|
|
|
|
mean value: 0.9522026470211411
|
|
|
|
key: test_fscore
|
|
value: [0.88 0.75 0.94736842 0.90909091 0.95238095 0.76190476
|
|
0.84210526 0.95238095 0.90909091 0.72727273]
|
|
|
|
mean value: 0.8631594896331739
|
|
|
|
key: train_fscore
|
|
value: [0.97916667 0.97938144 0.98445596 0.96938776 0.97435897 0.96938776
|
|
0.97409326 0.97916667 0.97382199 0.97916667]
|
|
|
|
mean value: 0.9762387140188749
|
|
|
|
key: test_precision
|
|
value: [0.78571429 0.69230769 1. 0.83333333 0.90909091 0.72727273
|
|
1. 1. 0.90909091 0.72727273]
|
|
|
|
mean value: 0.8584082584082584
|
|
|
|
key: train_precision
|
|
value: [0.96907216 0.95959596 0.97938144 0.95 0.95959596 0.95
|
|
0.95918367 0.96907216 0.96875 0.96907216]
|
|
|
|
mean value: 0.9633723530805636
|
|
|
|
key: test_recall
|
|
value: [1. 0.81818182 0.9 1. 1. 0.8
|
|
0.72727273 0.90909091 0.90909091 0.72727273]
|
|
|
|
mean value: 0.8790909090909089
|
|
|
|
key: train_recall
|
|
value: [0.98947368 1. 0.98958333 0.98958333 0.98958333 0.98958333
|
|
0.98947368 0.98947368 0.97894737 0.98947368]
|
|
|
|
mean value: 0.9895175438596493
|
|
|
|
key: test_accuracy
|
|
value: [0.86363636 0.72727273 0.95238095 0.9047619 0.95238095 0.76190476
|
|
0.85714286 0.95238095 0.9047619 0.71428571]
|
|
|
|
mean value: 0.859090909090909
|
|
|
|
key: train_accuracy
|
|
value: [0.97894737 0.97894737 0.98429319 0.96858639 0.97382199 0.96858639
|
|
0.97382199 0.97905759 0.97382199 0.97905759]
|
|
|
|
mean value: 0.9758941857260954
|
|
|
|
key: test_roc_auc
|
|
value: [0.86363636 0.72727273 0.95 0.90909091 0.95454545 0.76363636
|
|
0.86363636 0.95454545 0.90454545 0.71363636]
|
|
|
|
mean value: 0.8604545454545454
|
|
|
|
key: train_roc_auc
|
|
value: [0.97894737 0.97894737 0.98426535 0.96847588 0.97373904 0.96847588
|
|
0.97390351 0.97911184 0.97384868 0.97911184]
|
|
|
|
mean value: 0.9758826754385966
|
|
|
|
key: test_jcc
|
|
value: [0.78571429 0.6 0.9 0.83333333 0.90909091 0.61538462
|
|
0.72727273 0.90909091 0.83333333 0.57142857]
|
|
|
|
mean value: 0.7684648684648685
|
|
|
|
key: train_jcc
|
|
value: [0.95918367 0.95959596 0.96938776 0.94059406 0.95 0.94059406
|
|
0.94949495 0.95918367 0.94897959 0.95918367]
|
|
|
|
mean value: 0.9536197395249729
|
|
|
|
key: TN
|
|
value: 89
|
|
|
|
mean value: 89.0
|
|
|
|
key: FP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: FN
|
|
value: 17
|
|
|
|
mean value: 17.0
|
|
|
|
key: TP
|
|
value: 93
|
|
|
|
mean value: 93.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.78
|
|
|
|
Accuracy on Blind test: 0.9
|
|
|
|
Running classifier: 13
|
|
Model_name: XGBoost
|
|
Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...
|
|
interaction_constraints=None, learning_rate=None,
|
|
max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan,
|
|
monotone_constraints=None, n_estimators=100,
|
|
n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None,
|
|
reg_lambda=None, scale_pos_weight=None,
|
|
subsample=None, tree_method=None,
|
|
use_label_encoder=False,
|
|
validate_parameters=None, verbosity=0))])
|
|
|
|
key: fit_time
|
|
value: [0.06178808 0.04554296 0.04399633 0.04482245 0.21215343 0.04420877
|
|
0.04602003 0.04388404 0.04623508 0.04778576]
|
|
|
|
mean value: 0.0636436939239502
|
|
|
|
key: score_time
|
|
value: [0.01035094 0.01071358 0.01052022 0.01035261 0.01173902 0.01051378
|
|
0.01033735 0.01026654 0.01043367 0.01034498]
|
|
|
|
mean value: 0.010557270050048828
|
|
|
|
key: test_mcc
|
|
value: [1. 0.91287093 0.90829511 1. 0.90909091 0.90829511
|
|
0.80909091 0.90909091 0.80909091 0.90829511]
|
|
|
|
mean value: 0.9074119884226655
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.95652174 0.94736842 1. 0.95238095 0.94736842
|
|
0.90909091 0.95238095 0.90909091 0.95652174]
|
|
|
|
mean value: 0.9530724043309856
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.91666667 1. 1. 0.90909091 1.
|
|
0.90909091 1. 0.90909091 0.91666667]
|
|
|
|
mean value: 0.9560606060606058
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.9 1. 1. 0.9
|
|
0.90909091 0.90909091 0.90909091 1. ]
|
|
|
|
mean value: 0.9527272727272725
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.95454545 0.95238095 1. 0.95238095 0.95238095
|
|
0.9047619 0.95238095 0.9047619 0.95238095]
|
|
|
|
mean value: 0.9525974025974027
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.95454545 0.95 1. 0.95454545 0.95
|
|
0.90454545 0.95454545 0.90454545 0.95 ]
|
|
|
|
mean value: 0.9522727272727272
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.91666667 0.9 1. 0.90909091 0.9
|
|
0.83333333 0.90909091 0.83333333 0.91666667]
|
|
|
|
mean value: 0.9118181818181817
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 101
|
|
|
|
mean value: 101.0
|
|
|
|
key: FP
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: FN
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: TP
|
|
value: 101
|
|
|
|
mean value: 101.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.89
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 14
|
|
Model_name: LDA
|
|
Model func: LinearDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LinearDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.02883172 0.05682755 0.05887222 0.06574631 0.0676229 0.05554724
|
|
0.06847453 0.04513168 0.02737117 0.05307961]
|
|
|
|
mean value: 0.05275049209594727
|
|
|
|
key: score_time
|
|
value: [0.02276325 0.02177358 0.02305865 0.02589798 0.01629591 0.02290034
|
|
0.02269125 0.01211476 0.01202202 0.02856231]
|
|
|
|
mean value: 0.02080800533294678
|
|
|
|
key: test_mcc
|
|
value: [0.83205029 0.63636364 0.90909091 0.80909091 0.71562645 0.71818182
|
|
0.44038551 0.90909091 0.61818182 0.62641448]
|
|
|
|
mean value: 0.7214476731026188
|
|
|
|
key: train_mcc
|
|
value: [0.97894737 1. 0.97905702 1. 0.98958333 1.
|
|
1. 0.97927405 1. 1. ]
|
|
|
|
mean value: 0.9926861768392901
|
|
|
|
key: test_fscore
|
|
value: [0.9 0.81818182 0.95238095 0.9 0.84210526 0.85714286
|
|
0.7 0.95238095 0.81818182 0.83333333]
|
|
|
|
mean value: 0.8573706994759627
|
|
|
|
key: train_fscore
|
|
value: [0.98947368 1. 0.98958333 1. 0.9947644 1.
|
|
1. 0.98958333 1. 1. ]
|
|
|
|
mean value: 0.9963404748782952
|
|
|
|
key: test_precision
|
|
value: [1. 0.81818182 0.90909091 0.9 0.88888889 0.81818182
|
|
0.77777778 1. 0.81818182 0.76923077]
|
|
|
|
mean value: 0.86995337995338
|
|
|
|
key: train_precision
|
|
value: [0.98947368 1. 0.98958333 1. 1. 1.
|
|
1. 0.97938144 1. 1. ]
|
|
|
|
mean value: 0.9958438460842828
|
|
|
|
key: test_recall
|
|
value: [0.81818182 0.81818182 1. 0.9 0.8 0.9
|
|
0.63636364 0.90909091 0.81818182 0.90909091]
|
|
|
|
mean value: 0.8509090909090908
|
|
|
|
key: train_recall
|
|
value: [0.98947368 1. 0.98958333 1. 0.98958333 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9968640350877193
|
|
|
|
key: test_accuracy
|
|
value: [0.90909091 0.81818182 0.95238095 0.9047619 0.85714286 0.85714286
|
|
0.71428571 0.95238095 0.80952381 0.80952381]
|
|
|
|
mean value: 0.8584415584415584
|
|
|
|
key: train_accuracy
|
|
value: [0.98947368 1. 0.9895288 1. 0.9947644 1.
|
|
1. 0.9895288 1. 1. ]
|
|
|
|
mean value: 0.9963295673739323
|
|
|
|
key: test_roc_auc
|
|
value: [0.90909091 0.81818182 0.95454545 0.90454545 0.85454545 0.85909091
|
|
0.71818182 0.95454545 0.80909091 0.80454545]
|
|
|
|
mean value: 0.8586363636363636
|
|
|
|
key: train_roc_auc
|
|
value: [0.98947368 1. 0.98952851 1. 0.99479167 1.
|
|
1. 0.98958333 1. 1. ]
|
|
|
|
mean value: 0.9963377192982457
|
|
|
|
key: test_jcc
|
|
value: [0.81818182 0.69230769 0.90909091 0.81818182 0.72727273 0.75
|
|
0.53846154 0.90909091 0.69230769 0.71428571]
|
|
|
|
mean value: 0.756918081918082
|
|
|
|
key: train_jcc
|
|
value: [0.97916667 1. 0.97938144 1. 0.98958333 1.
|
|
1. 0.97938144 1. 1. ]
|
|
|
|
mean value: 0.9927512886597938
|
|
|
|
key: TN
|
|
value: 92
|
|
|
|
mean value: 92.0
|
|
|
|
key: FP
|
|
value: 16
|
|
|
|
mean value: 16.0
|
|
|
|
key: FN
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: TP
|
|
value: 90
|
|
|
|
mean value: 90.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.59
|
|
|
|
Accuracy on Blind test: 0.81
|
|
|
|
Running classifier: 15
|
|
Model_name: Multinomial
|
|
Model func: MultinomialNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MultinomialNB())])
|
|
|
|
key: fit_time
|
|
value: [0.02240634 0.00912261 0.00886822 0.00868654 0.00866103 0.00864649
|
|
0.00873685 0.00869918 0.00888872 0.00869131]
|
|
|
|
mean value: 0.010140728950500489
|
|
|
|
key: score_time
|
|
value: [0.0115993 0.00894094 0.00857878 0.00843859 0.00846052 0.0084939
|
|
0.00852418 0.00846744 0.00855279 0.00850368]
|
|
|
|
mean value: 0.00885601043701172
|
|
|
|
key: test_mcc
|
|
value: [ 0.09090909 0.18257419 0.61818182 0.33636364 0.42727273 0.13483997
|
|
0.24771685 0.43007562 -0.06741999 0.03739788]
|
|
|
|
mean value: 0.24379117869663122
|
|
|
|
key: train_mcc
|
|
value: [0.36850272 0.37896836 0.38226912 0.39449154 0.41362292 0.36131235
|
|
0.41359649 0.36156007 0.45732729 0.37171053]
|
|
|
|
mean value: 0.39033613998907246
|
|
|
|
key: test_fscore
|
|
value: [0.54545455 0.57142857 0.8 0.66666667 0.7 0.47058824
|
|
0.6 0.75 0.56 0.58333333]
|
|
|
|
mean value: 0.6247471352177235
|
|
|
|
key: train_fscore
|
|
value: [0.6875 0.69109948 0.6974359 0.71287129 0.71134021 0.68717949
|
|
0.70526316 0.67027027 0.73737374 0.68421053]
|
|
|
|
mean value: 0.6984544046223988
|
|
|
|
key: test_precision
|
|
value: [0.54545455 0.6 0.8 0.63636364 0.7 0.57142857
|
|
0.66666667 0.69230769 0.5 0.53846154]
|
|
|
|
mean value: 0.6250682650682651
|
|
|
|
key: train_precision
|
|
value: [0.68041237 0.6875 0.68686869 0.67924528 0.70408163 0.67676768
|
|
0.70526316 0.68888889 0.70873786 0.68421053]
|
|
|
|
mean value: 0.6901976087619398
|
|
|
|
key: test_recall
|
|
value: [0.54545455 0.54545455 0.8 0.7 0.7 0.4
|
|
0.54545455 0.81818182 0.63636364 0.63636364]
|
|
|
|
mean value: 0.6327272727272728
|
|
|
|
key: train_recall
|
|
value: [0.69473684 0.69473684 0.70833333 0.75 0.71875 0.69791667
|
|
0.70526316 0.65263158 0.76842105 0.68421053]
|
|
|
|
mean value: 0.7075
|
|
|
|
key: test_accuracy
|
|
value: [0.54545455 0.59090909 0.80952381 0.66666667 0.71428571 0.57142857
|
|
0.61904762 0.71428571 0.47619048 0.52380952]
|
|
|
|
mean value: 0.6231601731601731
|
|
|
|
key: train_accuracy
|
|
value: [0.68421053 0.68947368 0.69109948 0.69633508 0.70680628 0.68062827
|
|
0.70680628 0.68062827 0.72774869 0.68586387]
|
|
|
|
mean value: 0.6949600440892809
|
|
|
|
key: test_roc_auc
|
|
value: [0.54545455 0.59090909 0.80909091 0.66818182 0.71363636 0.56363636
|
|
0.62272727 0.70909091 0.46818182 0.51818182]
|
|
|
|
mean value: 0.6209090909090909
|
|
|
|
key: train_roc_auc
|
|
value: [0.68421053 0.68947368 0.69100877 0.69605263 0.70674342 0.68053728
|
|
0.70679825 0.68048246 0.72796053 0.68585526]
|
|
|
|
mean value: 0.6949122807017544
|
|
|
|
key: test_jcc
|
|
value: [0.375 0.4 0.66666667 0.5 0.53846154 0.30769231
|
|
0.42857143 0.6 0.38888889 0.41176471]
|
|
|
|
mean value: 0.46170455361631835
|
|
|
|
key: train_jcc
|
|
value: [0.52380952 0.528 0.53543307 0.55384615 0.552 0.5234375
|
|
0.54471545 0.50406504 0.584 0.52 ]
|
|
|
|
mean value: 0.5369306736326698
|
|
|
|
key: TN
|
|
value: 65
|
|
|
|
mean value: 65.0
|
|
|
|
key: FP
|
|
value: 39
|
|
|
|
mean value: 39.0
|
|
|
|
key: FN
|
|
value: 41
|
|
|
|
mean value: 41.0
|
|
|
|
key: TP
|
|
value: 67
|
|
|
|
mean value: 67.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.32
|
|
|
|
Accuracy on Blind test: 0.68
|
|
|
|
Running classifier: 16
|
|
Model_name: Passive Aggresive
|
|
Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
PassiveAggressiveClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01359797 0.01863289 0.01495814 0.01887131 0.01808858 0.01531839
|
|
0.01982331 0.01887655 0.01818299 0.01601124]
|
|
|
|
mean value: 0.01723613739013672
|
|
|
|
key: score_time
|
|
value: [0.00850892 0.01137233 0.01131248 0.01158357 0.01162577 0.01158118
|
|
0.01159835 0.0116539 0.01165724 0.01160526]
|
|
|
|
mean value: 0.011249899864196777
|
|
|
|
key: test_mcc
|
|
value: [0.61237244 0.81818182 0.90909091 0.82275335 0.62641448 0.30934411
|
|
0.71818182 0.90829511 0.61818182 0.24120908]
|
|
|
|
mean value: 0.6584024928181599
|
|
|
|
key: train_mcc
|
|
value: [0.85951623 0.96847471 0.91801541 1. 0.92922547 0.39904191
|
|
1. 0.96906883 0.92917291 0.94811895]
|
|
|
|
mean value: 0.8920634419087234
|
|
|
|
key: test_fscore
|
|
value: [0.70588235 0.90909091 0.95238095 0.88888889 0.77777778 0.68965517
|
|
0.85714286 0.95652174 0.81818182 0.69230769]
|
|
|
|
mean value: 0.82478301602563
|
|
|
|
key: train_fscore
|
|
value: [0.92134831 0.98429319 0.95959596 1. 0.96216216 0.73563218
|
|
1. 0.98445596 0.96174863 0.97409326]
|
|
|
|
mean value: 0.9483329670667896
|
|
|
|
key: test_precision
|
|
value: [1. 0.90909091 0.90909091 1. 0.875 0.52631579
|
|
0.9 0.91666667 0.81818182 0.6 ]
|
|
|
|
mean value: 0.8454346092503988
|
|
|
|
key: train_precision
|
|
value: [0.98795181 0.97916667 0.93137255 1. 1. 0.58181818
|
|
1. 0.96938776 1. 0.95918367]
|
|
|
|
mean value: 0.94088806333048
|
|
|
|
key: test_recall
|
|
value: [0.54545455 0.90909091 1. 0.8 0.7 1.
|
|
0.81818182 1. 0.81818182 0.81818182]
|
|
|
|
mean value: 0.840909090909091
|
|
|
|
key: train_recall
|
|
value: [0.86315789 0.98947368 0.98958333 1. 0.92708333 1.
|
|
1. 1. 0.92631579 0.98947368]
|
|
|
|
mean value: 0.9685087719298245
|
|
|
|
key: test_accuracy
|
|
value: [0.77272727 0.90909091 0.95238095 0.9047619 0.80952381 0.57142857
|
|
0.85714286 0.95238095 0.80952381 0.61904762]
|
|
|
|
mean value: 0.8158008658008657
|
|
|
|
key: train_accuracy
|
|
value: [0.92631579 0.98421053 0.95811518 1. 0.96335079 0.63874346
|
|
1. 0.98429319 0.96335079 0.97382199]
|
|
|
|
mean value: 0.9392201708459631
|
|
|
|
key: test_roc_auc
|
|
value: [0.77272727 0.90909091 0.95454545 0.9 0.80454545 0.59090909
|
|
0.85909091 0.95 0.80909091 0.60909091]
|
|
|
|
mean value: 0.8159090909090908
|
|
|
|
key: train_roc_auc
|
|
value: [0.92631579 0.98421053 0.95794956 1. 0.96354167 0.63684211
|
|
1. 0.984375 0.96315789 0.97390351]
|
|
|
|
mean value: 0.9390296052631578
|
|
|
|
key: test_jcc
|
|
value: [0.54545455 0.83333333 0.90909091 0.8 0.63636364 0.52631579
|
|
0.75 0.91666667 0.69230769 0.52941176]
|
|
|
|
mean value: 0.7138944337396349
|
|
|
|
key: train_jcc
|
|
value: [0.85416667 0.96907216 0.9223301 1. 0.92708333 0.58181818
|
|
1. 0.96938776 0.92631579 0.94949495]
|
|
|
|
mean value: 0.9099668937924689
|
|
|
|
key: TN
|
|
value: 84
|
|
|
|
mean value: 84.0
|
|
|
|
key: FP
|
|
value: 17
|
|
|
|
mean value: 17.0
|
|
|
|
key: FN
|
|
value: 22
|
|
|
|
mean value: 22.0
|
|
|
|
key: TP
|
|
value: 89
|
|
|
|
mean value: 89.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.7
|
|
|
|
Accuracy on Blind test: 0.87
|
|
|
|
Running classifier: 17
|
|
Model_name: Stochastic GDescent
|
|
Model func: SGDClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SGDClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01456928 0.01509285 0.01491833 0.01472616 0.01364017 0.01490045
|
|
0.01457667 0.01463819 0.0145216 0.01388884]
|
|
|
|
mean value: 0.014547252655029297
|
|
|
|
key: score_time
|
|
value: [0.01182294 0.01178408 0.01197004 0.01183724 0.01172924 0.01182938
|
|
0.01196957 0.01184297 0.01184607 0.0117929 ]
|
|
|
|
mean value: 0.011842441558837891
|
|
|
|
key: test_mcc
|
|
value: [0.68313005 0.73029674 0.90909091 0.90829511 0.45226702 0.55161872
|
|
0.4719399 0.90909091 0.34027852 0.15569979]
|
|
|
|
mean value: 0.6111707676032829
|
|
|
|
key: train_mcc
|
|
value: [0.87856517 0.92884073 0.92795293 1. 0.57673971 0.79066814
|
|
0.79946001 0.87603267 0.77378259 0.70617821]
|
|
|
|
mean value: 0.8258220151047642
|
|
|
|
key: test_fscore
|
|
value: [0.77777778 0.86956522 0.95238095 0.94736842 0.625 0.7826087
|
|
0.66666667 0.95238095 0.73333333 0.68965517]
|
|
|
|
mean value: 0.7996737189049584
|
|
|
|
key: train_fscore
|
|
value: [0.93333333 0.96446701 0.96446701 1. 0.68027211 0.89719626
|
|
0.87573964 0.93478261 0.88785047 0.85585586]
|
|
|
|
mean value: 0.899396429082304
|
|
|
|
key: test_precision
|
|
value: [1. 0.83333333 0.90909091 1. 0.83333333 0.69230769
|
|
0.85714286 1. 0.57894737 0.55555556]
|
|
|
|
mean value: 0.8259711049184734
|
|
|
|
key: train_precision
|
|
value: [0.98823529 0.93137255 0.94059406 1. 0.98039216 0.81355932
|
|
1. 0.96629213 0.79831933 0.7480315 ]
|
|
|
|
mean value: 0.9166796340065385
|
|
|
|
key: test_recall
|
|
value: [0.63636364 0.90909091 1. 0.9 0.5 0.9
|
|
0.54545455 0.90909091 1. 0.90909091]
|
|
|
|
mean value: 0.8209090909090909
|
|
|
|
key: train_recall
|
|
value: [0.88421053 1. 0.98958333 1. 0.52083333 1.
|
|
0.77894737 0.90526316 1. 1. ]
|
|
|
|
mean value: 0.9078837719298246
|
|
|
|
key: test_accuracy
|
|
value: [0.81818182 0.86363636 0.95238095 0.95238095 0.71428571 0.76190476
|
|
0.71428571 0.95238095 0.61904762 0.57142857]
|
|
|
|
mean value: 0.7919913419913419
|
|
|
|
key: train_accuracy
|
|
value: [0.93684211 0.96315789 0.96335079 1. 0.7539267 0.88481675
|
|
0.89005236 0.93717277 0.87434555 0.83246073]
|
|
|
|
mean value: 0.9036125654450261
|
|
|
|
key: test_roc_auc
|
|
value: [0.81818182 0.86363636 0.95454545 0.95 0.70454545 0.76818182
|
|
0.72272727 0.95454545 0.6 0.55454545]
|
|
|
|
mean value: 0.7890909090909091
|
|
|
|
key: train_roc_auc
|
|
value: [0.93684211 0.96315789 0.96321272 1. 0.75515351 0.88421053
|
|
0.88947368 0.93700658 0.875 0.83333333]
|
|
|
|
mean value: 0.9037390350877195
|
|
|
|
key: test_jcc
|
|
value: [0.63636364 0.76923077 0.90909091 0.9 0.45454545 0.64285714
|
|
0.5 0.90909091 0.57894737 0.52631579]
|
|
|
|
mean value: 0.6826441979073559
|
|
|
|
key: train_jcc
|
|
value: [0.875 0.93137255 0.93137255 1. 0.51546392 0.81355932
|
|
0.77894737 0.87755102 0.79831933 0.7480315 ]
|
|
|
|
mean value: 0.8269617550222188
|
|
|
|
key: TN
|
|
value: 81
|
|
|
|
mean value: 81.0
|
|
|
|
key: FP
|
|
value: 19
|
|
|
|
mean value: 19.0
|
|
|
|
key: FN
|
|
value: 25
|
|
|
|
mean value: 25.0
|
|
|
|
key: TP
|
|
value: 87
|
|
|
|
mean value: 87.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.57
|
|
|
|
Accuracy on Blind test: 0.81
|
|
|
|
Running classifier: 18
|
|
Model_name: AdaBoost Classifier
|
|
Model func: AdaBoostClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', AdaBoostClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.12517595 0.10848331 0.10823202 0.1077652 0.10984206 0.10818481
|
|
0.10810757 0.1085639 0.10866189 0.10933065]
|
|
|
|
mean value: 0.11023473739624023
|
|
|
|
key: score_time
|
|
value: [0.01501179 0.01486182 0.01477385 0.01486659 0.01476145 0.01496387
|
|
0.01477718 0.01481032 0.0147624 0.01616335]
|
|
|
|
mean value: 0.014975261688232423
|
|
|
|
key: test_mcc
|
|
value: [1. 0.63636364 0.80909091 1. 0.90909091 0.90909091
|
|
0.80909091 0.90909091 0.80909091 0.90829511]
|
|
|
|
mean value: 0.8699204197138339
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.81818182 0.9 1. 0.95238095 0.95238095
|
|
0.90909091 0.95238095 0.90909091 0.95652174]
|
|
|
|
mean value: 0.9350028232636929
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.81818182 0.9 1. 0.90909091 0.90909091
|
|
0.90909091 1. 0.90909091 0.91666667]
|
|
|
|
mean value: 0.927121212121212
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.81818182 0.9 1. 1. 1.
|
|
0.90909091 0.90909091 0.90909091 1. ]
|
|
|
|
mean value: 0.9445454545454546
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.81818182 0.9047619 1. 0.95238095 0.95238095
|
|
0.9047619 0.95238095 0.9047619 0.95238095]
|
|
|
|
mean value: 0.9341991341991343
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.81818182 0.90454545 1. 0.95454545 0.95454545
|
|
0.90454545 0.95454545 0.90454545 0.95 ]
|
|
|
|
mean value: 0.9345454545454543
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.69230769 0.81818182 1. 0.90909091 0.90909091
|
|
0.83333333 0.90909091 0.83333333 0.91666667]
|
|
|
|
mean value: 0.8821095571095571
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 98
|
|
|
|
mean value: 98.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 100
|
|
|
|
mean value: 100.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.84
|
|
|
|
Accuracy on Blind test: 0.93
|
|
|
|
Running classifier: 19
|
|
Model_name: Bagging Classifier
|
|
Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
BaggingClassifier(n_jobs=10, oob_score=True,
|
|
random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.03018928 0.03429747 0.03949785 0.05620551 0.03740382 0.03586698
|
|
0.03402066 0.03567505 0.03418803 0.04045534]
|
|
|
|
mean value: 0.03777999877929687
|
|
|
|
key: score_time
|
|
value: [0.01721668 0.02065182 0.02762413 0.02365732 0.0261569 0.01647234
|
|
0.02367759 0.0223124 0.0182445 0.02581906]
|
|
|
|
mean value: 0.02218327522277832
|
|
|
|
key: test_mcc
|
|
value: [1. 0.83205029 0.90829511 0.82275335 0.80909091 1.
|
|
0.80909091 0.90909091 0.80909091 0.90829511]
|
|
|
|
mean value: 0.8807757494367419
|
|
|
|
key: train_mcc
|
|
value: [1. 0.98952851 1. 0.97905702 0.97905702 0.97905702
|
|
1. 0.98958333 0.9895822 1. ]
|
|
|
|
mean value: 0.9905865090197933
|
|
|
|
key: test_fscore
|
|
value: [1. 0.91666667 0.94736842 0.88888889 0.9 1.
|
|
0.90909091 0.95238095 0.90909091 0.95652174]
|
|
|
|
mean value: 0.9380008486301392
|
|
|
|
key: train_fscore
|
|
value: [1. 0.9947644 1. 0.98958333 0.98958333 0.98958333
|
|
1. 0.9947644 0.99470899 1. ]
|
|
|
|
mean value: 0.9952987790520513
|
|
|
|
key: test_precision
|
|
value: [1. 0.84615385 1. 1. 0.9 1.
|
|
0.90909091 1. 0.90909091 0.91666667]
|
|
|
|
mean value: 0.948100233100233
|
|
|
|
key: train_precision
|
|
value: [1. 0.98958333 1. 0.98958333 0.98958333 0.98958333
|
|
1. 0.98958333 1. 1. ]
|
|
|
|
mean value: 0.9947916666666667
|
|
|
|
key: test_recall
|
|
value: [1. 1. 0.9 0.8 0.9 1.
|
|
0.90909091 0.90909091 0.90909091 1. ]
|
|
|
|
mean value: 0.9327272727272726
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 0.98958333 0.98958333 0.98958333
|
|
1. 1. 0.98947368 1. ]
|
|
|
|
mean value: 0.9958223684210527
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.90909091 0.95238095 0.9047619 0.9047619 1.
|
|
0.9047619 0.95238095 0.9047619 0.95238095]
|
|
|
|
mean value: 0.9385281385281387
|
|
|
|
key: train_accuracy
|
|
value: [1. 0.99473684 1. 0.9895288 0.9895288 0.9895288
|
|
1. 0.9947644 0.9947644 1. ]
|
|
|
|
mean value: 0.9952852025351338
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.90909091 0.95 0.9 0.90454545 1.
|
|
0.90454545 0.95454545 0.90454545 0.95 ]
|
|
|
|
mean value: 0.9377272727272727
|
|
|
|
key: train_roc_auc
|
|
value: [1. 0.99473684 1. 0.98952851 0.98952851 0.98952851
|
|
1. 0.99479167 0.99473684 1. ]
|
|
|
|
mean value: 0.9952850877192982
|
|
|
|
key: test_jcc
|
|
value: [1. 0.84615385 0.9 0.8 0.81818182 1.
|
|
0.83333333 0.90909091 0.83333333 0.91666667]
|
|
|
|
mean value: 0.8856759906759907
|
|
|
|
key: train_jcc
|
|
value: [1. 0.98958333 1. 0.97938144 0.97938144 0.97938144
|
|
1. 0.98958333 0.98947368 1. ]
|
|
|
|
mean value: 0.99067846807741
|
|
|
|
key: TN
|
|
value: 100
|
|
|
|
mean value: 100.0
|
|
|
|
key: FP
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: FN
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: TP
|
|
value: 99
|
|
|
|
mean value: 99.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.87
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
Running classifier: 20
|
|
Model_name: Gaussian Process
|
|
Model func: GaussianProcessClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianProcessClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.03967857 0.04909682 0.06145763 0.02647614 0.02489495 0.07871032
|
|
0.03897619 0.02560925 0.02572608 0.06365681]
|
|
|
|
mean value: 0.04342827796936035
|
|
|
|
key: score_time
|
|
value: [0.02542925 0.02239847 0.01280093 0.0128448 0.01276064 0.02025652
|
|
0.01270938 0.01264906 0.01257157 0.01288128]
|
|
|
|
mean value: 0.01573019027709961
|
|
|
|
key: test_mcc
|
|
value: [ 0.09245003 0.27272727 0.33028913 0.71818182 0.61818182 0.13483997
|
|
0.24771685 0.23373675 -0.05504819 -0.35527986]
|
|
|
|
mean value: 0.22377955940216845
|
|
|
|
key: train_mcc
|
|
value: [0.94784115 0.94742091 0.94769737 0.94769164 0.93717105 0.94769737
|
|
0.91641992 0.95831967 0.95832877 0.97905702]
|
|
|
|
mean value: 0.9487644872499189
|
|
|
|
key: test_fscore
|
|
value: [0.5 0.63636364 0.63157895 0.85714286 0.8 0.47058824
|
|
0.6 0.66666667 0.52173913 0.41666667]
|
|
|
|
mean value: 0.6100746139937148
|
|
|
|
key: train_fscore
|
|
value: [0.97326203 0.97382199 0.97382199 0.97409326 0.96875 0.97382199
|
|
0.95744681 0.9787234 0.97916667 0.98947368]
|
|
|
|
mean value: 0.9742381828563804
|
|
|
|
key: test_precision
|
|
value: [0.55555556 0.63636364 0.66666667 0.81818182 0.8 0.57142857
|
|
0.66666667 0.61538462 0.5 0.38461538]
|
|
|
|
mean value: 0.6214862914862915
|
|
|
|
key: train_precision
|
|
value: [0.98913043 0.96875 0.97894737 0.96907216 0.96875 0.97894737
|
|
0.96774194 0.98924731 0.96907216 0.98947368]
|
|
|
|
mean value: 0.9769132433043974
|
|
|
|
key: test_recall
|
|
value: [0.45454545 0.63636364 0.6 0.9 0.8 0.4
|
|
0.54545455 0.72727273 0.54545455 0.45454545]
|
|
|
|
mean value: 0.6063636363636363
|
|
|
|
key: train_recall
|
|
value: [0.95789474 0.97894737 0.96875 0.97916667 0.96875 0.96875
|
|
0.94736842 0.96842105 0.98947368 0.98947368]
|
|
|
|
mean value: 0.9716995614035089
|
|
|
|
key: test_accuracy
|
|
value: [0.54545455 0.63636364 0.66666667 0.85714286 0.80952381 0.57142857
|
|
0.61904762 0.61904762 0.47619048 0.33333333]
|
|
|
|
mean value: 0.6134199134199133
|
|
|
|
key: train_accuracy
|
|
value: [0.97368421 0.97368421 0.97382199 0.97382199 0.96858639 0.97382199
|
|
0.95811518 0.97905759 0.97905759 0.9895288 ]
|
|
|
|
mean value: 0.9743179939377239
|
|
|
|
key: test_roc_auc
|
|
value: [0.54545455 0.63636364 0.66363636 0.85909091 0.80909091 0.56363636
|
|
0.62272727 0.61363636 0.47272727 0.32727273]
|
|
|
|
mean value: 0.6113636363636362
|
|
|
|
key: train_roc_auc
|
|
value: [0.97368421 0.97368421 0.97384868 0.97379386 0.96858553 0.97384868
|
|
0.95805921 0.97900219 0.97911184 0.98952851]
|
|
|
|
mean value: 0.9743146929824562
|
|
|
|
key: test_jcc
|
|
value: [0.33333333 0.46666667 0.46153846 0.75 0.66666667 0.30769231
|
|
0.42857143 0.5 0.35294118 0.26315789]
|
|
|
|
mean value: 0.4530567935676295
|
|
|
|
key: train_jcc
|
|
value: [0.94791667 0.94897959 0.94897959 0.94949495 0.93939394 0.94897959
|
|
0.91836735 0.95833333 0.95918367 0.97916667]
|
|
|
|
mean value: 0.9498795351473923
|
|
|
|
key: TN
|
|
value: 66
|
|
|
|
mean value: 66.0
|
|
|
|
key: FP
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: FN
|
|
value: 40
|
|
|
|
mean value: 40.0
|
|
|
|
key: TP
|
|
value: 64
|
|
|
|
mean value: 64.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.26
|
|
|
|
Accuracy on Blind test: 0.61
|
|
|
|
Running classifier: 21
|
|
Model_name: Gradient Boosting
|
|
Model func: GradientBoostingClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GradientBoostingClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.37137389 0.33760333 0.33995962 0.34063864 0.3470366 0.3378973
|
|
0.3002677 0.34190512 0.30660367 0.34101677]
|
|
|
|
mean value: 0.3364302635192871
|
|
|
|
key: score_time
|
|
value: [0.00910854 0.00920963 0.00930381 0.00901294 0.00916195 0.00908446
|
|
0.00947428 0.00910878 0.00905728 0.00914216]
|
|
|
|
mean value: 0.009166383743286132
|
|
|
|
key: test_mcc
|
|
value: [1. 0.83205029 0.90909091 0.82275335 0.80909091 0.90909091
|
|
0.71818182 1. 0.80909091 0.90829511]
|
|
|
|
mean value: 0.8717644206319989
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.91666667 0.95238095 0.88888889 0.9 0.95238095
|
|
0.85714286 1. 0.90909091 0.95652174]
|
|
|
|
mean value: 0.9333072965681662
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.84615385 0.90909091 1. 0.9 0.90909091
|
|
0.9 1. 0.90909091 0.91666667]
|
|
|
|
mean value: 0.9290093240093238
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 1. 1. 0.8 0.9 1.
|
|
0.81818182 1. 0.90909091 1. ]
|
|
|
|
mean value: 0.9427272727272726
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.90909091 0.95238095 0.9047619 0.9047619 0.95238095
|
|
0.85714286 1. 0.9047619 0.95238095]
|
|
|
|
mean value: 0.9337662337662339
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.90909091 0.95454545 0.9 0.90454545 0.95454545
|
|
0.85909091 1. 0.90454545 0.95 ]
|
|
|
|
mean value: 0.9336363636363636
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.84615385 0.90909091 0.8 0.81818182 0.90909091
|
|
0.75 1. 0.83333333 0.91666667]
|
|
|
|
mean value: 0.8782517482517482
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 98
|
|
|
|
mean value: 98.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: TP
|
|
value: 100
|
|
|
|
mean value: 100.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.84
|
|
|
|
Accuracy on Blind test: 0.92
|
|
|
|
Running classifier: 22
|
|
Model_name: QDA
|
|
Model func: QuadraticDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', QuadraticDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.01949501 0.02266788 0.02243805 0.02208471 0.02231646 0.02282453
|
|
0.02309084 0.0220356 0.02272868 0.02262187]
|
|
|
|
mean value: 0.022230362892150878
|
|
|
|
key: score_time
|
|
value: [0.01193953 0.01205492 0.01245713 0.01253104 0.01255751 0.01247668
|
|
0.01246691 0.01270652 0.01268959 0.01259542]
|
|
|
|
mean value: 0.012447524070739745
|
|
|
|
key: test_mcc
|
|
value: [-0.09245003 0.20412415 0.18090681 0.18090681 0.21968621 0.06741999
|
|
0.03739788 -0.13762047 -0.13762047 -0.03015113]
|
|
|
|
mean value: 0.04925997289082072
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.5 0.66666667 0.64 0.64 0.66666667 0.58333333
|
|
0.58333333 0.4 0.4 0.35294118]
|
|
|
|
mean value: 0.5432941176470588
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.46153846 0.5625 0.53333333 0.53333333 0.52941176 0.5
|
|
0.53846154 0.44444444 0.44444444 0.5 ]
|
|
|
|
mean value: 0.5047467320261438
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.54545455 0.81818182 0.8 0.8 0.9 0.7
|
|
0.63636364 0.36363636 0.36363636 0.27272727]
|
|
|
|
mean value: 0.62
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.45454545 0.59090909 0.57142857 0.57142857 0.57142857 0.52380952
|
|
0.52380952 0.42857143 0.42857143 0.47619048]
|
|
|
|
mean value: 0.5140692640692641
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.45454545 0.59090909 0.58181818 0.58181818 0.58636364 0.53181818
|
|
0.51818182 0.43181818 0.43181818 0.48636364]
|
|
|
|
mean value: 0.5195454545454545
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.33333333 0.5 0.47058824 0.47058824 0.5 0.41176471
|
|
0.41176471 0.25 0.25 0.21428571]
|
|
|
|
mean value: 0.38123249299719886
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 42
|
|
|
|
mean value: 42.0
|
|
|
|
key: FP
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: FN
|
|
value: 64
|
|
|
|
mean value: 64.0
|
|
|
|
key: TP
|
|
value: 63
|
|
|
|
mean value: 63.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.13
|
|
|
|
Accuracy on Blind test: 0.57
|
|
|
|
Running classifier: 23
|
|
Model_name: Ridge Classifier
|
|
Model func: RidgeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.0221839 0.03195024 0.03572011 0.03568769 0.03169751 0.03557086
|
|
0.03364134 0.03437328 0.03475642 0.03443766]
|
|
|
|
mean value: 0.03300189971923828
|
|
|
|
key: score_time
|
|
value: [0.02333236 0.02400351 0.02397799 0.02430987 0.02144504 0.02073979
|
|
0.02383351 0.02371478 0.02098441 0.02247763]
|
|
|
|
mean value: 0.02288188934326172
|
|
|
|
key: test_mcc
|
|
value: [0.81818182 0.73029674 1. 1. 0.90909091 0.71818182
|
|
0.71818182 1. 0.61818182 0.62641448]
|
|
|
|
mean value: 0.813852940846407
|
|
|
|
key: train_mcc
|
|
value: [0.94784115 0.95810708 0.95831967 0.9690588 0.96863692 0.95831967
|
|
0.95832877 0.95832877 0.97927405 0.96864035]
|
|
|
|
mean value: 0.9624855237784811
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.86956522 1. 1. 0.95238095 0.85714286
|
|
0.85714286 1. 0.81818182 0.83333333]
|
|
|
|
mean value: 0.9096837944664034
|
|
|
|
key: train_fscore
|
|
value: [0.97409326 0.97916667 0.97938144 0.98461538 0.98445596 0.97938144
|
|
0.97916667 0.97916667 0.98958333 0.98429319]
|
|
|
|
mean value: 0.9813304021061862
|
|
|
|
key: test_precision
|
|
value: [0.90909091 0.83333333 1. 1. 0.90909091 0.81818182
|
|
0.9 1. 0.81818182 0.76923077]
|
|
|
|
mean value: 0.8957109557109557
|
|
|
|
key: train_precision
|
|
value: [0.95918367 0.96907216 0.96938776 0.96969697 0.97938144 0.96938776
|
|
0.96907216 0.96907216 0.97938144 0.97916667]
|
|
|
|
mean value: 0.9712802201480404
|
|
|
|
key: test_recall
|
|
value: [0.90909091 0.90909091 1. 1. 1. 0.9
|
|
0.81818182 1. 0.81818182 0.90909091]
|
|
|
|
mean value: 0.9263636363636364
|
|
|
|
key: train_recall
|
|
value: [0.98947368 0.98947368 0.98958333 1. 0.98958333 0.98958333
|
|
0.98947368 0.98947368 1. 0.98947368]
|
|
|
|
mean value: 0.9916118421052632
|
|
|
|
key: test_accuracy
|
|
value: [0.90909091 0.86363636 1. 1. 0.95238095 0.85714286
|
|
0.85714286 1. 0.80952381 0.80952381]
|
|
|
|
mean value: 0.905844155844156
|
|
|
|
key: train_accuracy
|
|
value: [0.97368421 0.97894737 0.97905759 0.98429319 0.98429319 0.97905759
|
|
0.97905759 0.97905759 0.9895288 0.98429319]
|
|
|
|
mean value: 0.9811270322402865
|
|
|
|
key: test_roc_auc
|
|
value: [0.90909091 0.86363636 1. 1. 0.95454545 0.85909091
|
|
0.85909091 1. 0.80909091 0.80454545]
|
|
|
|
mean value: 0.9059090909090909
|
|
|
|
key: train_roc_auc
|
|
value: [0.97368421 0.97894737 0.97900219 0.98421053 0.98426535 0.97900219
|
|
0.97911184 0.97911184 0.98958333 0.98432018]
|
|
|
|
mean value: 0.981123903508772
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.76923077 1. 1. 0.90909091 0.75
|
|
0.75 1. 0.69230769 0.71428571]
|
|
|
|
mean value: 0.841824841824842
|
|
|
|
key: train_jcc
|
|
value: [0.94949495 0.95918367 0.95959596 0.96969697 0.96938776 0.95959596
|
|
0.95918367 0.95918367 0.97938144 0.96907216]
|
|
|
|
mean value: 0.9633776222141466
|
|
|
|
key: TN
|
|
value: 94
|
|
|
|
mean value: 94.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: TP
|
|
value: 98
|
|
|
|
mean value: 98.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.74
|
|
|
|
Accuracy on Blind test: 0.88
|
|
|
|
Running classifier: 24
|
|
Model_name: Ridge ClassifierCV
|
|
Model func: RidgeClassifierCV(cv=10)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifierCV(cv=10))])
|
|
|
|
key: fit_time
|
|
value: [0.2252419 0.22955441 0.22431469 0.22547555 0.24449944 0.23760462
|
|
0.29956794 0.22531152 0.23448348 0.22501922]
|
|
|
|
mean value: 0.2371072769165039
|
|
|
|
key: score_time
|
|
value: [0.02095652 0.02331495 0.02272773 0.02966738 0.02431035 0.02423763
|
|
0.02446938 0.02391839 0.02422833 0.0242424 ]
|
|
|
|
mean value: 0.024207305908203126
|
|
|
|
key: test_mcc
|
|
value: [0.81818182 0.73029674 1. 1. 0.90909091 0.71818182
|
|
0.71818182 1. 0.61818182 0.62641448]
|
|
|
|
mean value: 0.813852940846407
|
|
|
|
key: train_mcc
|
|
value: [0.94784115 0.95810708 0.95831967 0.9690588 0.96863692 0.95831967
|
|
0.95832877 0.95832877 0.97927405 0.96864035]
|
|
|
|
mean value: 0.9624855237784811
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.86956522 1. 1. 0.95238095 0.85714286
|
|
0.85714286 1. 0.81818182 0.83333333]
|
|
|
|
mean value: 0.9096837944664034
|
|
|
|
key: train_fscore
|
|
value: [0.97409326 0.97916667 0.97938144 0.98461538 0.98445596 0.97938144
|
|
0.97916667 0.97916667 0.98958333 0.98429319]
|
|
|
|
mean value: 0.9813304021061862
|
|
|
|
key: test_precision
|
|
value: [0.90909091 0.83333333 1. 1. 0.90909091 0.81818182
|
|
0.9 1. 0.81818182 0.76923077]
|
|
|
|
mean value: 0.8957109557109557
|
|
|
|
key: train_precision
|
|
value: [0.95918367 0.96907216 0.96938776 0.96969697 0.97938144 0.96938776
|
|
0.96907216 0.96907216 0.97938144 0.97916667]
|
|
|
|
mean value: 0.9712802201480404
|
|
|
|
key: test_recall
|
|
value: [0.90909091 0.90909091 1. 1. 1. 0.9
|
|
0.81818182 1. 0.81818182 0.90909091]
|
|
|
|
mean value: 0.9263636363636364
|
|
|
|
key: train_recall
|
|
value: [0.98947368 0.98947368 0.98958333 1. 0.98958333 0.98958333
|
|
0.98947368 0.98947368 1. 0.98947368]
|
|
|
|
mean value: 0.9916118421052632
|
|
|
|
key: test_accuracy
|
|
value: [0.90909091 0.86363636 1. 1. 0.95238095 0.85714286
|
|
0.85714286 1. 0.80952381 0.80952381]
|
|
|
|
mean value: 0.905844155844156
|
|
|
|
key: train_accuracy
|
|
value: [0.97368421 0.97894737 0.97905759 0.98429319 0.98429319 0.97905759
|
|
0.97905759 0.97905759 0.9895288 0.98429319]
|
|
|
|
mean value: 0.9811270322402865
|
|
|
|
key: test_roc_auc
|
|
value: [0.90909091 0.86363636 1. 1. 0.95454545 0.85909091
|
|
0.85909091 1. 0.80909091 0.80454545]
|
|
|
|
mean value: 0.9059090909090909
|
|
|
|
key: train_roc_auc
|
|
value: [0.97368421 0.97894737 0.97900219 0.98421053 0.98426535 0.97900219
|
|
0.97911184 0.97911184 0.98958333 0.98432018]
|
|
|
|
mean value: 0.981123903508772
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.76923077 1. 1. 0.90909091 0.75
|
|
0.75 1. 0.69230769 0.71428571]
|
|
|
|
mean value: 0.841824841824842
|
|
|
|
key: train_jcc
|
|
value: [0.94949495 0.95918367 0.95959596 0.96969697 0.96938776 0.95959596
|
|
0.95918367 0.95918367 0.97938144 0.96907216]
|
|
|
|
mean value: 0.9633776222141466
|
|
|
|
key: TN
|
|
value: 94
|
|
|
|
mean value: 94.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 12
|
|
|
|
mean value: 12.0
|
|
|
|
key: TP
|
|
value: 98
|
|
|
|
mean value: 98.0
|
|
|
|
key: trainingY_neg
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: trainingY_pos
|
|
value: 106
|
|
|
|
mean value: 106.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:356: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rus_CV['Data_source'] = 'CV'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:357: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rus_CV['Resampling'] = rs_rus
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:362: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rus_BT['Data_source'] = 'BT'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:363: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rus_BT['Resampling'] = rs_rus
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
|
|
MCC on Blind test: 0.74
|
|
|
|
Accuracy on Blind test: 0.88
|
|
|
|
PASS: sorting df by score that is mapped onto the order I want
|
|
|
|
==============================================================
|
|
|
|
Running several classification models (n): 24
|
|
List of models:
|
|
('Logistic Regression', LogisticRegression(random_state=42))
|
|
('Logistic RegressionCV', LogisticRegressionCV(random_state=42))
|
|
('Gaussian NB', GaussianNB())
|
|
('Naive Bayes', BernoulliNB())
|
|
('K-Nearest Neighbors', KNeighborsClassifier())
|
|
('SVC', SVC(random_state=42))
|
|
('MLP', MLPClassifier(max_iter=500, random_state=42))
|
|
('Decision Tree', DecisionTreeClassifier(random_state=42))
|
|
('Extra Trees', ExtraTreesClassifier(random_state=42))
|
|
('Extra Tree', ExtraTreeClassifier(random_state=42))
|
|
('Random Forest', RandomForestClassifier(n_estimators=1000, random_state=42))
|
|
('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42))
|
|
('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0))
|
|
('LDA', LinearDiscriminantAnalysis())
|
|
('Multinomial', MultinomialNB())
|
|
('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=10, random_state=42))
|
|
('Stochastic GDescent', SGDClassifier(n_jobs=10, random_state=42))
|
|
('AdaBoost Classifier', AdaBoostClassifier(random_state=42))
|
|
('Bagging Classifier', BaggingClassifier(n_jobs=10, oob_score=True, random_state=42))
|
|
('Gaussian Process', GaussianProcessClassifier(random_state=42))
|
|
('Gradient Boosting', GradientBoostingClassifier(random_state=42))
|
|
('QDA', QuadraticDiscriminantAnalysis())
|
|
('Ridge Classifier', RidgeClassifier(random_state=42))
|
|
('Ridge ClassifierCV', RidgeClassifierCV(cv=10))
|
|
|
|
================================================================
|
|
|
|
|
|
Running classifier: 1
|
|
Model_name: Logistic Regression
|
|
Model func: LogisticRegression(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegression(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.03361392 0.02674818 0.03462672 0.03609133 0.03604364 0.03495097
|
|
0.03474545 0.03721118 0.05540252 0.05710363]
|
|
|
|
mean value: 0.03865375518798828
|
|
|
|
key: score_time
|
|
value: [0.01211977 0.01200271 0.01244116 0.01268816 0.0126245 0.01258707
|
|
0.01261234 0.01320767 0.01244402 0.01252508]
|
|
|
|
mean value: 0.012525248527526855
|
|
|
|
key: test_mcc
|
|
value: [0.80952381 0.8660254 0.7098505 0.90238095 0.70714286 0.85441771
|
|
0.80907152 0.95238095 0.86240942 0.95238095]
|
|
|
|
mean value: 0.8425584072097785
|
|
|
|
key: train_mcc
|
|
value: [0.90317784 0.92454046 0.89769524 0.91915141 0.91380162 0.93553077
|
|
0.90846996 0.91421693 0.92473841 0.93025158]
|
|
|
|
mean value: 0.9171574208092543
|
|
|
|
key: test_fscore
|
|
value: [0.9047619 0.92307692 0.84210526 0.95 0.85 0.92307692
|
|
0.9 0.97560976 0.93333333 0.97560976]
|
|
|
|
mean value: 0.9177573859602102
|
|
|
|
key: train_fscore
|
|
value: [0.95054945 0.96174863 0.94850949 0.95956873 0.95675676 0.9673913
|
|
0.95367847 0.95604396 0.96174863 0.96438356]
|
|
|
|
mean value: 0.9580378989464318
|
|
|
|
key: test_precision
|
|
value: [0.9047619 1. 0.88888889 0.95 0.85 0.94736842
|
|
0.94736842 1. 0.875 1. ]
|
|
|
|
mean value: 0.9363387635756057
|
|
|
|
key: train_precision
|
|
value: [0.96648045 0.97237569 0.95628415 0.96216216 0.96195652 0.97802198
|
|
0.96153846 0.97206704 0.97237569 0.97777778]
|
|
|
|
mean value: 0.9681039921493962
|
|
|
|
key: test_recall
|
|
value: [0.9047619 0.85714286 0.8 0.95 0.85 0.9
|
|
0.85714286 0.95238095 1. 0.95238095]
|
|
|
|
mean value: 0.9023809523809524
|
|
|
|
key: train_recall
|
|
value: [0.93513514 0.95135135 0.94086022 0.95698925 0.9516129 0.95698925
|
|
0.94594595 0.94054054 0.95135135 0.95135135]
|
|
|
|
mean value: 0.9482127288578901
|
|
|
|
key: test_accuracy
|
|
value: [0.9047619 0.92857143 0.85365854 0.95121951 0.85365854 0.92682927
|
|
0.90243902 0.97560976 0.92682927 0.97560976]
|
|
|
|
mean value: 0.9199186991869919
|
|
|
|
key: train_accuracy
|
|
value: [0.95135135 0.96216216 0.94878706 0.95956873 0.95687332 0.96765499
|
|
0.9541779 0.95687332 0.96226415 0.96495957]
|
|
|
|
mean value: 0.9584672543163109
|
|
|
|
key: test_roc_auc
|
|
value: [0.9047619 0.92857143 0.85238095 0.95119048 0.85357143 0.92619048
|
|
0.90357143 0.97619048 0.925 0.97619048]
|
|
|
|
mean value: 0.9197619047619048
|
|
|
|
key: train_roc_auc
|
|
value: [0.95135135 0.96216216 0.94880849 0.9595757 0.95688753 0.96768381
|
|
0.95415577 0.95682941 0.96223482 0.96492299]
|
|
|
|
mean value: 0.9584612031386225
|
|
|
|
key: test_jcc
|
|
value: [0.82608696 0.85714286 0.72727273 0.9047619 0.73913043 0.85714286
|
|
0.81818182 0.95238095 0.875 0.95238095]
|
|
|
|
mean value: 0.8509481460568417
|
|
|
|
key: train_jcc
|
|
value: [0.90575916 0.92631579 0.90206186 0.92227979 0.91709845 0.93684211
|
|
0.91145833 0.91578947 0.92631579 0.93121693]
|
|
|
|
mean value: 0.9195137678760738
|
|
|
|
key: TN
|
|
value: 193
|
|
|
|
mean value: 193.0
|
|
|
|
key: FP
|
|
value: 20
|
|
|
|
mean value: 20.0
|
|
|
|
key: FN
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: TP
|
|
value: 186
|
|
|
|
mean value: 186.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.77
|
|
|
|
Accuracy on Blind test: 0.9
|
|
|
|
Running classifier: 2
|
|
Model_name: Logistic RegressionCV
|
|
Model func: LogisticRegressionCV(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LogisticRegressionCV(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.72588873 0.72638083 0.79159212 0.71572828 0.72554207 0.8414104
|
|
0.73313403 1.04889321 0.86867905 0.7414546 ]
|
|
|
|
mean value: 0.7918703317642212
|
|
|
|
key: score_time
|
|
value: [0.0126152 0.01262069 0.01263237 0.01266813 0.01270723 0.01270628
|
|
0.0126307 0.01389456 0.01262283 0.01270795]
|
|
|
|
mean value: 0.012780594825744628
|
|
|
|
key: test_mcc
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
|
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
|
|
|
Increase the number of iterations (max_iter) or scale the data as shown in:
|
|
https://scikit-learn.org/stable/modules/preprocessing.html
|
|
Please also refer to the documentation for alternative solver options:
|
|
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
|
n_iter_i = _check_optimize_result(
|
|
[0.85811633 0.90889326 0.90649828 0.90649828 0.95238095 0.90692382
|
|
0.90692382 0.90692382 0.90238095 0.95238095]
|
|
|
|
mean value: 0.9107920477231237
|
|
|
|
key: train_mcc
|
|
value: [1. 0.98918919 0.98927544 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9978464631087196
|
|
|
|
key: test_fscore
|
|
value: [0.93023256 0.95 0.94736842 0.94736842 0.97560976 0.95238095
|
|
0.95 0.95 0.95238095 0.97560976]
|
|
|
|
mean value: 0.9530950817201823
|
|
|
|
key: train_fscore
|
|
value: [1. 0.99459459 0.99465241 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9989247001011707
|
|
|
|
key: test_precision
|
|
value: [0.90909091 1. 1. 1. 0.95238095 0.90909091
|
|
1. 1. 0.95238095 1. ]
|
|
|
|
mean value: 0.9722943722943723
|
|
|
|
key: train_precision
|
|
value: [1. 0.99459459 0.9893617 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9983956296722255
|
|
|
|
key: test_recall
|
|
value: [0.95238095 0.9047619 0.9 0.9 1. 1.
|
|
0.9047619 0.9047619 0.95238095 0.95238095]
|
|
|
|
mean value: 0.9371428571428572
|
|
|
|
key: train_recall
|
|
value: [1. 0.99459459 1. 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9994594594594595
|
|
|
|
key: test_accuracy
|
|
value: [0.92857143 0.95238095 0.95121951 0.95121951 0.97560976 0.95121951
|
|
0.95121951 0.95121951 0.95121951 0.97560976]
|
|
|
|
mean value: 0.9539488966318235
|
|
|
|
key: train_accuracy
|
|
value: [1. 0.99459459 0.99460916 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.998920375901508
|
|
|
|
key: test_roc_auc
|
|
value: [0.92857143 0.95238095 0.95 0.95 0.97619048 0.95238095
|
|
0.95238095 0.95238095 0.95119048 0.97619048]
|
|
|
|
mean value: 0.9541666666666666
|
|
|
|
key: train_roc_auc
|
|
value: [1. 0.99459459 0.99459459 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.998918918918919
|
|
|
|
key: test_jcc
|
|
value: [0.86956522 0.9047619 0.9 0.9 0.95238095 0.90909091
|
|
0.9047619 0.9047619 0.90909091 0.95238095]
|
|
|
|
mean value: 0.910679465462074
|
|
|
|
key: train_jcc
|
|
value: [1. 0.98924731 0.9893617 1. 1. 1.
|
|
1. 1. 1. 1. ]
|
|
|
|
mean value: 0.9978609013955616
|
|
|
|
key: TN
|
|
value: 200
|
|
|
|
mean value: 200.0
|
|
|
|
key: FP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: FN
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: TP
|
|
value: 193
|
|
|
|
mean value: 193.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.86
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
Running classifier: 3
|
|
Model_name: Gaussian NB
|
|
Model func: GaussianNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianNB())])
|
|
|
|
key: fit_time
|
|
value: [0.01363659 0.01345634 0.00994134 0.00960851 0.00949359 0.00958729
|
|
0.0094173 0.00937819 0.00946307 0.00946736]
|
|
|
|
mean value: 0.010344958305358887
|
|
|
|
key: score_time
|
|
value: [0.01365209 0.00971627 0.00930667 0.00895095 0.0087657 0.0089097
|
|
0.00887871 0.00876594 0.00890136 0.00898695]
|
|
|
|
mean value: 0.009483432769775391
|
|
|
|
key: test_mcc
|
|
value: [0.23809524 0.58834841 0.23472895 0.56836003 0.32612121 0.36666667
|
|
0.41428571 0.60952381 0.51966679 0.31655495]
|
|
|
|
mean value: 0.4182351767630109
|
|
|
|
key: train_mcc
|
|
value: [0.51446836 0.49423687 0.50746481 0.49629637 0.53221435 0.49149084
|
|
0.49254324 0.50655846 0.50342023 0.50200402]
|
|
|
|
mean value: 0.5040697536773684
|
|
|
|
key: test_fscore
|
|
value: [0.61904762 0.80851064 0.65217391 0.79069767 0.68181818 0.68292683
|
|
0.71428571 0.80952381 0.7826087 0.68181818]
|
|
|
|
mean value: 0.7223411257173928
|
|
|
|
key: train_fscore
|
|
value: [0.77192982 0.75773196 0.76649746 0.73295455 0.77402597 0.75949367
|
|
0.75949367 0.7628866 0.76455696 0.76732673]
|
|
|
|
mean value: 0.7616897399142623
|
|
|
|
key: test_precision
|
|
value: [0.61904762 0.73076923 0.57692308 0.73913043 0.625 0.66666667
|
|
0.71428571 0.80952381 0.72 0.65217391]
|
|
|
|
mean value: 0.6853520465042204
|
|
|
|
key: train_precision
|
|
value: [0.71962617 0.72413793 0.72596154 0.77710843 0.74874372 0.71770335
|
|
0.71428571 0.72906404 0.71904762 0.70776256]
|
|
|
|
mean value: 0.7283441069150348
|
|
|
|
key: test_recall
|
|
value: [0.61904762 0.9047619 0.75 0.85 0.75 0.7
|
|
0.71428571 0.80952381 0.85714286 0.71428571]
|
|
|
|
mean value: 0.766904761904762
|
|
|
|
key: train_recall
|
|
value: [0.83243243 0.79459459 0.81182796 0.69354839 0.80107527 0.80645161
|
|
0.81081081 0.8 0.81621622 0.83783784]
|
|
|
|
mean value: 0.8004795117698343
|
|
|
|
key: test_accuracy
|
|
value: [0.61904762 0.78571429 0.6097561 0.7804878 0.65853659 0.68292683
|
|
0.70731707 0.80487805 0.75609756 0.65853659]
|
|
|
|
mean value: 0.7063298490127757
|
|
|
|
key: train_accuracy
|
|
value: [0.75405405 0.74594595 0.75202156 0.74663073 0.76549865 0.74393531
|
|
0.74393531 0.75202156 0.74932615 0.74663073]
|
|
|
|
mean value: 0.7500000000000001
|
|
|
|
key: test_roc_auc
|
|
value: [0.61904762 0.78571429 0.61309524 0.78214286 0.66071429 0.68333333
|
|
0.70714286 0.8047619 0.75357143 0.65714286]
|
|
|
|
mean value: 0.7066666666666668
|
|
|
|
key: train_roc_auc
|
|
value: [0.75405405 0.74594595 0.75185992 0.74677419 0.7654025 0.74376635
|
|
0.74411508 0.75215054 0.74950596 0.74687591]
|
|
|
|
mean value: 0.750045045045045
|
|
|
|
key: test_jcc
|
|
value: [0.44827586 0.67857143 0.48387097 0.65384615 0.51724138 0.51851852
|
|
0.55555556 0.68 0.64285714 0.51724138]
|
|
|
|
mean value: 0.569597838778039
|
|
|
|
key: train_jcc
|
|
value: [0.62857143 0.60995851 0.62139918 0.57847534 0.63135593 0.6122449
|
|
0.6122449 0.61666667 0.61885246 0.62248996]
|
|
|
|
mean value: 0.6152259261717272
|
|
|
|
key: TN
|
|
value: 133
|
|
|
|
mean value: 133.0
|
|
|
|
key: FP
|
|
value: 48
|
|
|
|
mean value: 48.0
|
|
|
|
key: FN
|
|
value: 73
|
|
|
|
mean value: 73.0
|
|
|
|
key: TP
|
|
value: 158
|
|
|
|
mean value: 158.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.38
|
|
|
|
Accuracy on Blind test: 0.72
|
|
|
|
Running classifier: 4
|
|
Model_name: Naive Bayes
|
|
Model func: BernoulliNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', BernoulliNB())])
|
|
|
|
key: fit_time
|
|
value: [0.00960875 0.00961447 0.00955772 0.00955391 0.00958133 0.00977755
|
|
0.00968075 0.00956798 0.01065421 0.00960755]
|
|
|
|
mean value: 0.009720420837402344
|
|
|
|
key: score_time
|
|
value: [0.00918698 0.00876713 0.00875735 0.00879526 0.00878072 0.00886106
|
|
0.00873804 0.00886297 0.00907731 0.00888038]
|
|
|
|
mean value: 0.008870720863342285
|
|
|
|
key: test_mcc
|
|
value: [ 0.1490712 0.3478328 0.12293728 0.29009627 0.44466675 0.51320273
|
|
0.21904762 0.35737186 -0.06905393 0.35038478]
|
|
|
|
mean value: 0.27255573395110366
|
|
|
|
key: train_mcc
|
|
value: [0.39922511 0.42369499 0.42411077 0.39660058 0.40199754 0.39002634
|
|
0.4362526 0.42849689 0.41863013 0.41598338]
|
|
|
|
mean value: 0.413501833326159
|
|
|
|
key: test_fscore
|
|
value: [0.5 0.61111111 0.4375 0.51612903 0.625 0.73684211
|
|
0.61904762 0.48275862 0.35294118 0.58823529]
|
|
|
|
mean value: 0.5469564958957842
|
|
|
|
key: train_fscore
|
|
value: [0.63022508 0.6407767 0.64536741 0.61639344 0.64375 0.62619808
|
|
0.65822785 0.65830721 0.64984227 0.65420561]
|
|
|
|
mean value: 0.6423293654148221
|
|
|
|
key: test_precision
|
|
value: [0.6 0.73333333 0.58333333 0.72727273 0.83333333 0.77777778
|
|
0.61904762 0.875 0.46153846 0.76923077]
|
|
|
|
mean value: 0.6979867354867355
|
|
|
|
key: train_precision
|
|
value: [0.77777778 0.7983871 0.79527559 0.78991597 0.76865672 0.77165354
|
|
0.79389313 0.78358209 0.78030303 0.77205882]
|
|
|
|
mean value: 0.7831503764370378
|
|
|
|
key: test_recall
|
|
value: [0.42857143 0.52380952 0.35 0.4 0.5 0.7
|
|
0.61904762 0.33333333 0.28571429 0.47619048]
|
|
|
|
mean value: 0.4616666666666666
|
|
|
|
key: train_recall
|
|
value: [0.52972973 0.53513514 0.54301075 0.50537634 0.55376344 0.52688172
|
|
0.56216216 0.56756757 0.55675676 0.56756757]
|
|
|
|
mean value: 0.5447951176983434
|
|
|
|
key: test_accuracy
|
|
value: [0.57142857 0.66666667 0.56097561 0.63414634 0.70731707 0.75609756
|
|
0.6097561 0.63414634 0.46341463 0.65853659]
|
|
|
|
mean value: 0.6262485481997677
|
|
|
|
key: train_accuracy
|
|
value: [0.68918919 0.7 0.70080863 0.68463612 0.69272237 0.68463612
|
|
0.70889488 0.70619946 0.70080863 0.70080863]
|
|
|
|
mean value: 0.6968704013987033
|
|
|
|
key: test_roc_auc
|
|
value: [0.57142857 0.66666667 0.55595238 0.62857143 0.70238095 0.7547619
|
|
0.60952381 0.64166667 0.46785714 0.66309524]
|
|
|
|
mean value: 0.6261904761904762
|
|
|
|
key: train_roc_auc
|
|
value: [0.68918919 0.7 0.70123511 0.6851206 0.69309794 0.68506248
|
|
0.70850044 0.70582679 0.70042139 0.70045045]
|
|
|
|
mean value: 0.6968904388259227
|
|
|
|
key: test_jcc
|
|
value: [0.33333333 0.44 0.28 0.34782609 0.45454545 0.58333333
|
|
0.44827586 0.31818182 0.21428571 0.41666667]
|
|
|
|
mean value: 0.38364482693718077
|
|
|
|
key: train_jcc
|
|
value: [0.4600939 0.47142857 0.47641509 0.44549763 0.47465438 0.45581395
|
|
0.49056604 0.49065421 0.48130841 0.48611111]
|
|
|
|
mean value: 0.47325432898515085
|
|
|
|
key: TN
|
|
value: 163
|
|
|
|
mean value: 163.0
|
|
|
|
key: FP
|
|
value: 111
|
|
|
|
mean value: 111.0
|
|
|
|
key: FN
|
|
value: 43
|
|
|
|
mean value: 43.0
|
|
|
|
key: TP
|
|
value: 95
|
|
|
|
mean value: 95.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.25
|
|
|
|
Accuracy on Blind test: 0.61
|
|
|
|
Running classifier: 5
|
|
Model_name: K-Nearest Neighbors
|
|
Model func: KNeighborsClassifier()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', KNeighborsClassifier())])
|
|
|
|
key: fit_time
|
|
value: [0.00936174 0.00998497 0.01001406 0.00935721 0.00906157 0.0099709
|
|
0.0100174 0.01004004 0.01016951 0.01013422]
|
|
|
|
mean value: 0.009811162948608398
|
|
|
|
key: score_time
|
|
value: [0.01630521 0.01282525 0.012254 0.01248646 0.01178503 0.01724839
|
|
0.017205 0.01558185 0.01626253 0.0120213 ]
|
|
|
|
mean value: 0.014397501945495605
|
|
|
|
key: test_mcc
|
|
value: [0.14285714 0.46352358 0.21957752 0.27179142 0.46300848 0.31960727
|
|
0.57570364 0.56836003 0.27338837 0.53864117]
|
|
|
|
mean value: 0.38364586184785077
|
|
|
|
key: train_mcc
|
|
value: [0.61730841 0.61209986 0.58747618 0.57456037 0.58691428 0.61839695
|
|
0.55346014 0.60226766 0.60102483 0.5810842 ]
|
|
|
|
mean value: 0.5934592871144924
|
|
|
|
key: test_fscore
|
|
value: [0.57142857 0.64705882 0.55555556 0.57142857 0.71794872 0.61111111
|
|
0.80851064 0.76923077 0.61538462 0.72222222]
|
|
|
|
mean value: 0.6589879596137418
|
|
|
|
key: train_fscore
|
|
value: [0.80222841 0.79888268 0.78309859 0.76878613 0.78431373 0.8033241
|
|
0.76880223 0.79329609 0.7826087 0.78089888]
|
|
|
|
mean value: 0.7866239527605026
|
|
|
|
key: test_precision
|
|
value: [0.57142857 0.84615385 0.625 0.66666667 0.73684211 0.6875
|
|
0.73076923 0.83333333 0.66666667 0.86666667]
|
|
|
|
mean value: 0.7231027086948141
|
|
|
|
key: train_precision
|
|
value: [0.82758621 0.8265896 0.82248521 0.83125 0.81871345 0.82857143
|
|
0.79310345 0.82080925 0.84375 0.8128655 ]
|
|
|
|
mean value: 0.822572408214349
|
|
|
|
key: test_recall
|
|
value: [0.57142857 0.52380952 0.5 0.5 0.7 0.55
|
|
0.9047619 0.71428571 0.57142857 0.61904762]
|
|
|
|
mean value: 0.6154761904761905
|
|
|
|
key: train_recall
|
|
value: [0.77837838 0.77297297 0.74731183 0.71505376 0.75268817 0.77956989
|
|
0.74594595 0.76756757 0.72972973 0.75135135]
|
|
|
|
mean value: 0.7540569601859926
|
|
|
|
key: test_accuracy
|
|
value: [0.57142857 0.71428571 0.6097561 0.63414634 0.73170732 0.65853659
|
|
0.7804878 0.7804878 0.63414634 0.75609756]
|
|
|
|
mean value: 0.6871080139372822
|
|
|
|
key: train_accuracy
|
|
value: [0.80810811 0.80540541 0.79245283 0.78436658 0.79245283 0.80862534
|
|
0.77628032 0.80053908 0.79784367 0.78975741]
|
|
|
|
mean value: 0.7955831572812705
|
|
|
|
key: test_roc_auc
|
|
value: [0.57142857 0.71428571 0.60714286 0.63095238 0.73095238 0.65595238
|
|
0.77738095 0.78214286 0.63571429 0.75952381]
|
|
|
|
mean value: 0.6865476190476191
|
|
|
|
key: train_roc_auc
|
|
value: [0.80810811 0.80540541 0.79257483 0.78455391 0.7925603 0.80870387
|
|
0.77619878 0.80045045 0.79766056 0.78965417]
|
|
|
|
mean value: 0.7955870386515548
|
|
|
|
key: test_jcc
|
|
value: [0.4 0.47826087 0.38461538 0.4 0.56 0.44
|
|
0.67857143 0.625 0.44444444 0.56521739]
|
|
|
|
mean value: 0.4976109518500823
|
|
|
|
key: train_jcc
|
|
value: [0.66976744 0.66511628 0.64351852 0.62441315 0.64516129 0.6712963
|
|
0.62443439 0.65740741 0.64285714 0.640553 ]
|
|
|
|
mean value: 0.6484524906404061
|
|
|
|
key: TN
|
|
value: 156
|
|
|
|
mean value: 156.0
|
|
|
|
key: FP
|
|
value: 79
|
|
|
|
mean value: 79.0
|
|
|
|
key: FN
|
|
value: 50
|
|
|
|
mean value: 50.0
|
|
|
|
key: TP
|
|
value: 127
|
|
|
|
mean value: 127.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.31
|
|
|
|
Accuracy on Blind test: 0.65
|
|
|
|
Running classifier: 6
|
|
Model_name: SVC
|
|
Model func: SVC(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SVC(random_state=42))])
|
|
|
|
key: fit_time
|
|
value:/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
|
|
warnings.warn(
|
|
[0.01748967 0.01689219 0.0167439 0.01735997 0.0168469 0.01769972
|
|
0.01706409 0.01706934 0.01685452 0.01693988]
|
|
|
|
mean value: 0.01709601879119873
|
|
|
|
key: score_time
|
|
value: [0.0109098 0.01085544 0.01085448 0.01093626 0.01070666 0.01160288
|
|
0.01079202 0.01068521 0.01070261 0.01083326]
|
|
|
|
mean value: 0.010887861251831055
|
|
|
|
key: test_mcc
|
|
value: [0.62187434 0.76980036 0.51966679 0.65952381 0.60952381 0.7098505
|
|
0.8047619 0.71121921 0.7633652 0.78072006]
|
|
|
|
mean value: 0.6950305975867748
|
|
|
|
key: train_mcc
|
|
value: [0.77930111 0.78419633 0.81230044 0.77400792 0.80592851 0.76820692
|
|
0.77427749 0.76318023 0.78148627 0.78453492]
|
|
|
|
mean value: 0.7827420129069609
|
|
|
|
key: test_fscore
|
|
value: [0.81818182 0.87179487 0.72222222 0.82926829 0.8 0.84210526
|
|
0.9047619 0.85 0.88888889 0.86486486]
|
|
|
|
mean value: 0.8392088126555393
|
|
|
|
key: train_fscore
|
|
value: [0.88642659 0.89010989 0.90358127 0.8852459 0.90322581 0.88409704
|
|
0.8839779 0.87912088 0.88450704 0.89071038]
|
|
|
|
mean value: 0.8891002697697242
|
|
|
|
key: test_precision
|
|
value: [0.7826087 0.94444444 0.8125 0.80952381 0.8 0.88888889
|
|
0.9047619 0.89473684 0.83333333 1. ]
|
|
|
|
mean value: 0.8670797918709818
|
|
|
|
key: train_precision
|
|
value: [0.90909091 0.90502793 0.92655367 0.9 0.90322581 0.88648649
|
|
0.9039548 0.89385475 0.92352941 0.90055249]
|
|
|
|
mean value: 0.9052276256122077
|
|
|
|
key: test_recall
|
|
value: [0.85714286 0.80952381 0.65 0.85 0.8 0.8
|
|
0.9047619 0.80952381 0.95238095 0.76190476]
|
|
|
|
mean value: 0.8195238095238097
|
|
|
|
key: train_recall
|
|
value: [0.86486486 0.87567568 0.88172043 0.87096774 0.90322581 0.88172043
|
|
0.86486486 0.86486486 0.84864865 0.88108108]
|
|
|
|
mean value: 0.8737634408602151
|
|
|
|
key: test_accuracy
|
|
value: [0.80952381 0.88095238 0.75609756 0.82926829 0.80487805 0.85365854
|
|
0.90243902 0.85365854 0.87804878 0.87804878]
|
|
|
|
mean value: 0.8446573751451801
|
|
|
|
key: train_accuracy
|
|
value: [0.88918919 0.89189189 0.90566038 0.88679245 0.90296496 0.88409704
|
|
0.88679245 0.88140162 0.88948787 0.89218329]
|
|
|
|
mean value: 0.8910461134989438
|
|
|
|
key: test_roc_auc
|
|
value: [0.80952381 0.88095238 0.75357143 0.8297619 0.8047619 0.85238095
|
|
0.90238095 0.8547619 0.87619048 0.88095238]
|
|
|
|
mean value: 0.8445238095238097
|
|
|
|
key: train_roc_auc
|
|
value: [0.88918919 0.89189189 0.90572508 0.88683522 0.90296425 0.88410346
|
|
0.88673351 0.88135716 0.88937809 0.89215344]
|
|
|
|
mean value: 0.8910331299040977
|
|
|
|
key: test_jcc
|
|
value: [0.69230769 0.77272727 0.56521739 0.70833333 0.66666667 0.72727273
|
|
0.82608696 0.73913043 0.8 0.76190476]
|
|
|
|
mean value: 0.725964723682115
|
|
|
|
key: train_jcc
|
|
value: [0.7960199 0.8019802 0.8241206 0.79411765 0.82352941 0.79227053
|
|
0.79207921 0.78431373 0.79292929 0.80295567]
|
|
|
|
mean value: 0.8004316183121798
|
|
|
|
key: TN
|
|
value: 179
|
|
|
|
mean value: 179.0
|
|
|
|
key: FP
|
|
value: 37
|
|
|
|
mean value: 37.0
|
|
|
|
key: FN
|
|
value: 27
|
|
|
|
mean value: 27.0
|
|
|
|
key: TP
|
|
value: 169
|
|
|
|
mean value: 169.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.57
|
|
|
|
Accuracy on Blind test: 0.81
|
|
|
|
Running classifier: 7
|
|
Model_name: MLP
|
|
Model func: MLPClassifier(max_iter=500, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MLPClassifier(max_iter=500, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [1.4426558 1.25770402 1.43716693 1.26745296 1.44111037 1.38606858
|
|
1.27516031 1.38661623 1.26520538 1.44973159]
|
|
|
|
mean value: 1.3608872175216675
|
|
|
|
key: score_time
|
|
value: [0.0147295 0.01381016 0.0137701 0.01382208 0.01384902 0.01415205
|
|
0.01397204 0.01392603 0.01390982 0.01387882]
|
|
|
|
mean value: 0.013981962203979492
|
|
|
|
key: test_mcc
|
|
value: [0.80952381 0.90889326 0.90649828 0.8547619 0.80907152 0.8047619
|
|
0.90692382 0.90692382 0.90649828 1. ]
|
|
|
|
mean value: 0.8813856601490313
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 0.9946235 1. 0.9946235
|
|
1. 0.99462366 1. 1. ]
|
|
|
|
mean value: 0.9983870656085617
|
|
|
|
key: test_fscore
|
|
value: [0.9047619 0.95 0.94736842 0.92682927 0.9047619 0.9
|
|
0.95 0.95 0.95454545 1. ]
|
|
|
|
mean value: 0.9388266953414579
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 0.99731903 1. 0.99731903
|
|
1. 0.99730458 1. 1. ]
|
|
|
|
mean value: 0.9991942651915335
|
|
|
|
key: test_precision
|
|
value: [0.9047619 1. 1. 0.9047619 0.86363636 0.9
|
|
1. 1. 0.91304348 1. ]
|
|
|
|
mean value: 0.9486203651421043
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 0.99465241 1. 0.99465241
|
|
1. 0.99462366 1. 1. ]
|
|
|
|
mean value: 0.9983928468748203
|
|
|
|
key: test_recall
|
|
value: [0.9047619 0.9047619 0.9 0.95 0.95 0.9 0.9047619
|
|
0.9047619 1. 1. ]
|
|
|
|
mean value: 0.931904761904762
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.9047619 0.95238095 0.95121951 0.92682927 0.90243902 0.90243902
|
|
0.95121951 0.95121951 0.95121951 1. ]
|
|
|
|
mean value: 0.9393728222996515
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 0.99730458 1. 0.99730458
|
|
1. 0.99730458 1. 1. ]
|
|
|
|
mean value: 0.9991913746630727
|
|
|
|
key: test_roc_auc
|
|
value: [0.9047619 0.95238095 0.95 0.92738095 0.90357143 0.90238095
|
|
0.95238095 0.95238095 0.95 1. ]
|
|
|
|
mean value: 0.9395238095238094
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 0.9972973 1. 0.9972973
|
|
1. 0.99731183 1. 1. ]
|
|
|
|
mean value: 0.9991906422551583
|
|
|
|
key: test_jcc
|
|
value: [0.82608696 0.9047619 0.9 0.86363636 0.82608696 0.81818182
|
|
0.9047619 0.9047619 0.91304348 1. ]
|
|
|
|
mean value: 0.8861321287408244
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 0.99465241 1. 0.99465241
|
|
1. 0.99462366 1. 1. ]
|
|
|
|
mean value: 0.9983928468748203
|
|
|
|
key: TN
|
|
value: 195
|
|
|
|
mean value: 195.0
|
|
|
|
key: FP
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: FN
|
|
value: 11
|
|
|
|
mean value: 11.0
|
|
|
|
key: TP
|
|
value: 192
|
|
|
|
mean value: 192.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.8
|
|
|
|
Accuracy on Blind test: 0.91
|
|
|
|
Running classifier: 8
|
|
Model_name: Decision Tree
|
|
Model func: DecisionTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', DecisionTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.02328062 0.01698422 0.01714063 0.01603866 0.01591897 0.01552367
|
|
0.01692295 0.0148356 0.01414895 0.01521873]
|
|
|
|
mean value: 0.016601300239562987
|
|
|
|
key: score_time
|
|
value: [0.01345897 0.00929999 0.00878119 0.00894523 0.00869012 0.00870967
|
|
0.00864792 0.00867152 0.00922465 0.00865269]
|
|
|
|
mean value: 0.009308195114135743
|
|
|
|
key: test_mcc
|
|
value: [0.95346259 0.90889326 1. 1. 0.95238095 0.90238095
|
|
0.90692382 0.90692382 0.95227002 0.90692382]
|
|
|
|
mean value: 0.9390159240291451
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.97560976 0.95 1. 1. 0.97560976 0.95
|
|
0.95 0.95 0.97674419 0.95 ]
|
|
|
|
mean value: 0.9677963698241634
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 1. 0.95238095 0.95
|
|
1. 1. 0.95454545 1. ]
|
|
|
|
mean value: 0.9856926406926407
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.95238095 0.9047619 1. 1. 1. 0.95
|
|
0.9047619 0.9047619 1. 0.9047619 ]
|
|
|
|
mean value: 0.9521428571428572
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.97619048 0.95238095 1. 1. 0.97560976 0.95121951
|
|
0.95121951 0.95121951 0.97560976 0.95121951]
|
|
|
|
mean value: 0.9684668989547036
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.97619048 0.95238095 1. 1. 0.97619048 0.95119048
|
|
0.95238095 0.95238095 0.975 0.95238095]
|
|
|
|
mean value: 0.9688095238095238
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.95238095 0.9047619 1. 1. 0.95238095 0.9047619
|
|
0.9047619 0.9047619 0.95454545 0.9047619 ]
|
|
|
|
mean value: 0.9383116883116884
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 203
|
|
|
|
mean value: 203.0
|
|
|
|
key: FP
|
|
value: 10
|
|
|
|
mean value: 10.0
|
|
|
|
key: FN
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: TP
|
|
value: 196
|
|
|
|
mean value: 196.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.9
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 9
|
|
Model_name: Extra Trees
|
|
Model func: ExtraTreesClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreesClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.10815501 0.10895181 0.1093998 0.10929322 0.10891581 0.10842085
|
|
0.10859251 0.10859179 0.1095922 0.10897279]
|
|
|
|
mean value: 0.10888857841491699
|
|
|
|
key: score_time
|
|
value: [0.01763892 0.01770115 0.01765752 0.01774645 0.01755905 0.01760721
|
|
0.01772714 0.01766253 0.0177381 0.01773667]
|
|
|
|
mean value: 0.01767747402191162
|
|
|
|
key: test_mcc
|
|
value: [0.81322028 0.90889326 0.7565654 1. 0.7098505 0.8047619
|
|
0.90238095 0.8547619 0.80817439 0.8213423 ]
|
|
|
|
mean value: 0.8379950902937058
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.95 0.87179487 1. 0.84210526 0.9
|
|
0.95238095 0.92682927 0.90909091 0.89473684]
|
|
|
|
mean value: 0.9156029015913483
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.86956522 1. 0.89473684 1. 0.88888889 0.9
|
|
0.95238095 0.95 0.86956522 1. ]
|
|
|
|
mean value: 0.9325137118157713
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.95238095 0.9047619 0.85 1. 0.8 0.9
|
|
0.95238095 0.9047619 0.95238095 0.80952381]
|
|
|
|
mean value: 0.9026190476190477
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.9047619 0.95238095 0.87804878 1. 0.85365854 0.90243902
|
|
0.95121951 0.92682927 0.90243902 0.90243902]
|
|
|
|
mean value: 0.9174216027874564
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.9047619 0.95238095 0.87738095 1. 0.85238095 0.90238095
|
|
0.95119048 0.92738095 0.90119048 0.9047619 ]
|
|
|
|
mean value: 0.9173809523809524
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.9047619 0.77272727 1. 0.72727273 0.81818182
|
|
0.90909091 0.86363636 0.83333333 0.80952381]
|
|
|
|
mean value: 0.8471861471861472
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 192
|
|
|
|
mean value: 192.0
|
|
|
|
key: FP
|
|
value: 20
|
|
|
|
mean value: 20.0
|
|
|
|
key: FN
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: TP
|
|
value: 186
|
|
|
|
mean value: 186.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.54
|
|
|
|
Accuracy on Blind test: 0.8
|
|
|
|
Running classifier: 10
|
|
Model_name: Extra Tree
|
|
Model func: ExtraTreeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', ExtraTreeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.00962472 0.00962687 0.00960588 0.00952983 0.00948834 0.00954628
|
|
0.00967455 0.0095439 0.00956202 0.00961089]
|
|
|
|
mean value: 0.009581327438354492
|
|
|
|
key: score_time /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
|
|
value: [0.00869465 0.00862598 0.008672 0.00865126 0.00859308 0.00861502
|
|
0.00867414 0.00862432 0.00860453 0.00868845]
|
|
|
|
mean value: 0.008644342422485352
|
|
|
|
key: test_mcc
|
|
value: [0.82462113 0.68640647 0.44466675 0.80907152 0.80817439 0.37309549
|
|
0.66668392 0.6133669 0.71121921 0.72229808]
|
|
|
|
mean value: 0.6659603856937134
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.89473684 0.81081081 0.625 0.9047619 0.89473684 0.62857143
|
|
0.82051282 0.8 0.85 0.84210526]
|
|
|
|
mean value: 0.8071235912025386
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 0.9375 0.83333333 0.86363636 0.94444444 0.73333333
|
|
0.88888889 0.84210526 0.89473684 0.94117647]
|
|
|
|
mean value: 0.8879154939487757
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.80952381 0.71428571 0.5 0.95 0.85 0.55
|
|
0.76190476 0.76190476 0.80952381 0.76190476]
|
|
|
|
mean value: 0.7469047619047618
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.9047619 0.83333333 0.70731707 0.90243902 0.90243902 0.68292683
|
|
0.82926829 0.80487805 0.85365854 0.85365854]
|
|
|
|
mean value: 0.8274680603948896
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.9047619 0.83333333 0.70238095 0.90357143 0.90119048 0.6797619
|
|
0.83095238 0.80595238 0.8547619 0.85595238]
|
|
|
|
mean value: 0.8272619047619048
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.80952381 0.68181818 0.45454545 0.82608696 0.80952381 0.45833333
|
|
0.69565217 0.66666667 0.73913043 0.72727273]
|
|
|
|
mean value: 0.6868553547901375
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 187
|
|
|
|
mean value: 187.0
|
|
|
|
key: FP
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: FN
|
|
value: 19
|
|
|
|
mean value: 19.0
|
|
|
|
key: TP
|
|
value: 154
|
|
|
|
mean value: 154.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.41
|
|
|
|
Accuracy on Blind test: 0.73
|
|
|
|
Running classifier: 11
|
|
Model_name: Random Forest
|
|
Model func: RandomForestClassifier(n_estimators=1000, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(n_estimators=1000, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [1.52321553 1.51518893 1.51075912 1.50979471 1.50419736 1.51972842
|
|
1.55826187 1.5144372 1.5101018 1.51376414]
|
|
|
|
mean value: 1.51794490814209
|
|
|
|
key: score_time
|
|
value: [0.09260964 0.09168124 0.09199357 0.09185886 0.09356117 0.09131193
|
|
0.09143853 0.09412074 0.09189868 0.09186673]
|
|
|
|
mean value: 0.09223411083221436
|
|
|
|
key: test_mcc
|
|
value: [0.81322028 1. 0.8047619 1. 0.80907152 0.8547619
|
|
0.95238095 0.95227002 0.90649828 1. ]
|
|
|
|
mean value: 0.9092964857581508
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 1. 0.9 1. 0.9047619 0.92682927
|
|
0.97560976 0.97674419 0.95454545 1. ]
|
|
|
|
mean value: 0.9547581478835024
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [0.86956522 1. 0.9 1. 0.86363636 0.9047619
|
|
1. 0.95454545 0.91304348 1. ]
|
|
|
|
mean value: 0.9405552418595896
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.95238095 1. 0.9 1. 0.95 0.95
|
|
0.95238095 1. 1. 1. ]
|
|
|
|
mean value: 0.9704761904761904
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.9047619 1. 0.90243902 1. 0.90243902 0.92682927
|
|
0.97560976 0.97560976 0.95121951 1. ]
|
|
|
|
mean value: 0.9538908246225318
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.9047619 1. 0.90238095 1. 0.90357143 0.92738095
|
|
0.97619048 0.975 0.95 1. ]
|
|
|
|
mean value: 0.9539285714285712
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 1. 0.81818182 1. 0.82608696 0.86363636
|
|
0.95238095 0.95454545 0.91304348 1. ]
|
|
|
|
mean value: 0.9161208356860531
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 193
|
|
|
|
mean value: 193.0
|
|
|
|
key: FP
|
|
value: 6
|
|
|
|
mean value: 6.0
|
|
|
|
key: FN
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: TP
|
|
value: 200
|
|
|
|
mean value: 200.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.85
|
|
|
|
Accuracy on Blind test: 0.94
|
|
|
|
Running classifier: 12
|
|
Model_name: Random Forest2
|
|
Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10, oob_score=True,
|
|
random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...age_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
RandomForestClassifier(max_features='auto', min_samples_leaf=5,
|
|
n_estimators=1000, n_jobs=10,
|
|
oob_score=True, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.89944077 0.96595263 0.91089082 0.95778012 0.93107462 1.00435877
|
|
0.92148137 0.93732476 0.98079419 0.89986396]
|
|
|
|
mean value: 0.9408962011337281
|
|
|
|
key: score_time
|
|
value: [0.22168422 0.22878671 0.21265125 0.23230767 0.20985246 0.24188519
|
|
0.21738148 0.22345281 0.21452069 0.20086122]
|
|
|
|
mean value: 0.22033836841583251
|
|
|
|
key: test_mcc
|
|
value: [0.76980036 0.95346259 0.65952381 0.95238095 0.76500781 0.8047619
|
|
0.95238095 0.95227002 0.86240942 0.95227002]
|
|
|
|
mean value: 0.8624267820275844
|
|
|
|
key: train_mcc
|
|
value: [0.98391316 0.98391316 0.97339739 0.97866283 0.98395537 0.98927544
|
|
0.98927606 0.98395676 0.98395676 0.978494 ]
|
|
|
|
mean value: 0.9828800926027311
|
|
|
|
key: test_fscore
|
|
value: [0.88888889 0.97560976 0.82926829 0.97560976 0.88372093 0.9
|
|
0.97560976 0.97674419 0.93333333 0.97674419]
|
|
|
|
mean value: 0.9315529085523414
|
|
|
|
key: train_fscore
|
|
value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.
|
|
warn(
|
|
[0.9919571 0.9919571 0.9867374 0.9893617 0.992 0.99465241
|
|
0.99462366 0.9919571 0.9919571 0.98924731]
|
|
|
|
mean value: 0.9914450895047775
|
|
|
|
key: test_precision
|
|
value: [0.83333333 1. 0.80952381 0.95238095 0.82608696 0.9
|
|
1. 0.95454545 0.875 0.95454545]
|
|
|
|
mean value: 0.9105415960850743
|
|
|
|
key: train_precision
|
|
value: [0.98404255 0.98404255 0.97382199 0.97894737 0.98412698 0.9893617
|
|
0.98930481 0.98404255 0.98404255 0.98395722]
|
|
|
|
mean value: 0.983569028905601
|
|
|
|
key: test_recall
|
|
value: [0.95238095 0.95238095 0.85 1. 0.95 0.9
|
|
0.95238095 1. 1. 1. ]
|
|
|
|
mean value: 0.9557142857142857
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1.
|
|
1. 1. 1. 0.99459459]
|
|
|
|
mean value: 0.9994594594594595
|
|
|
|
key: test_accuracy
|
|
value: [0.88095238 0.97619048 0.82926829 0.97560976 0.87804878 0.90243902
|
|
0.97560976 0.97560976 0.92682927 0.97560976]
|
|
|
|
mean value: 0.9296167247386758
|
|
|
|
key: train_accuracy
|
|
value: [0.99189189 0.99189189 0.98652291 0.98921833 0.99191375 0.99460916
|
|
0.99460916 0.99191375 0.99191375 0.98921833]
|
|
|
|
mean value: 0.991370292125009
|
|
|
|
key: test_roc_auc
|
|
value: [0.88095238 0.97619048 0.8297619 0.97619048 0.8797619 0.90238095
|
|
0.97619048 0.975 0.925 0.975 ]
|
|
|
|
mean value: 0.9296428571428571
|
|
|
|
key: train_roc_auc
|
|
value: [0.99189189 0.99189189 0.98648649 0.98918919 0.99189189 0.99459459
|
|
0.99462366 0.99193548 0.99193548 0.98923278]
|
|
|
|
mean value: 0.9913673350770125
|
|
|
|
key: test_jcc
|
|
value: [0.8 0.95238095 0.70833333 0.95238095 0.79166667 0.81818182
|
|
0.95238095 0.95454545 0.875 0.95454545]
|
|
|
|
mean value: 0.8759415584415585
|
|
|
|
key: train_jcc
|
|
value: [0.98404255 0.98404255 0.97382199 0.97894737 0.98412698 0.9893617
|
|
0.98930481 0.98404255 0.98404255 0.9787234 ]
|
|
|
|
mean value: 0.9830456474059993
|
|
|
|
key: TN
|
|
value: 186
|
|
|
|
mean value: 186.0
|
|
|
|
key: FP
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: FN
|
|
value: 20
|
|
|
|
mean value: 20.0
|
|
|
|
key: TP
|
|
value: 197
|
|
|
|
mean value: 197.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.77
|
|
|
|
Accuracy on Blind test: 0.9
|
|
|
|
Running classifier: 13
|
|
Model_name: XGBoost
|
|
Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,
|
|
colsample_bynode=None, colsample_bytree=None,
|
|
enable_categorical=False, gamma=None, gpu_id=None,
|
|
importance_type=None, interaction_constraints=None,
|
|
learning_rate=None, max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan, monotone_constraints=None,
|
|
n_estimators=100, n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None, reg_lambda=None,
|
|
scale_pos_weight=None, subsample=None, tree_method=None,
|
|
use_label_encoder=False, validate_parameters=None, verbosity=0)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_p...
|
|
interaction_constraints=None, learning_rate=None,
|
|
max_delta_step=None, max_depth=None,
|
|
min_child_weight=None, missing=nan,
|
|
monotone_constraints=None, n_estimators=100,
|
|
n_jobs=None, num_parallel_tree=None,
|
|
predictor=None, random_state=42, reg_alpha=None,
|
|
reg_lambda=None, scale_pos_weight=None,
|
|
subsample=None, tree_method=None,
|
|
use_label_encoder=False,
|
|
validate_parameters=None, verbosity=0))])
|
|
|
|
key: fit_time
|
|
value: [0.07526803 0.05560231 0.05945444 0.09954143 0.0577004 0.06429505
|
|
0.05793118 0.05943251 0.05828786 0.0576067 ]
|
|
|
|
mean value: 0.06451199054718018
|
|
|
|
key: score_time
|
|
value: [0.01105881 0.01142812 0.01062226 0.01171064 0.01137996 0.01111913
|
|
0.01055002 0.01063538 0.01095271 0.01062679]
|
|
|
|
mean value: 0.011008381843566895
|
|
|
|
key: test_mcc
|
|
value: [0.95346259 0.95346259 0.95227002 1. 0.95238095 1.
|
|
0.95238095 0.95238095 0.95227002 1. ]
|
|
|
|
mean value: 0.9668608066001669
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.97560976 0.97560976 0.97435897 1. 0.97560976 1.
|
|
0.97560976 0.97560976 0.97674419 1. ]
|
|
|
|
mean value: 0.9829151940893291
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 1. 0.95238095 1.
|
|
1. 1. 0.95454545 1. ]
|
|
|
|
mean value: 0.9906926406926407
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.95238095 0.95238095 0.95 1. 1. 1.
|
|
0.95238095 0.95238095 1. 1. ]
|
|
|
|
mean value: 0.9759523809523809
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.97619048 0.97619048 0.97560976 1. 0.97560976 1.
|
|
0.97560976 0.97560976 0.97560976 1. ]
|
|
|
|
mean value: 0.9830429732868758
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.97619048 0.97619048 0.975 1. 0.97619048 1.
|
|
0.97619048 0.97619048 0.975 1. ]
|
|
|
|
mean value: 0.983095238095238
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.95238095 0.95238095 0.95 1. 0.95238095 1.
|
|
0.95238095 0.95238095 0.95454545 1. ]
|
|
|
|
mean value: 0.9666450216450215
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 204
|
|
|
|
mean value: 204.0
|
|
|
|
key: FP
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: FN
|
|
value: 2
|
|
|
|
mean value: 2.0
|
|
|
|
key: TP
|
|
value: 201
|
|
|
|
mean value: 201.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.9
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 14
|
|
Model_name: LDA
|
|
Model func: LinearDiscriminantAnalysis()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', LinearDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.03443933 0.05179811 0.06322622 0.05100846 0.06835747 0.07097554
|
|
0.07047963 0.03397155 0.03488135 0.03616333]
|
|
|
|
mean value: 0.051530098915100096
|
|
|
|
key: score_time
|
|
value: [0.02279735 0.0122478 0.01230407 0.02061558 0.02173162 0.0191772
|
|
0.01230192 0.01241112 0.01224446 0.01825356]
|
|
|
|
mean value: 0.016408467292785646
|
|
|
|
key: test_mcc
|
|
value: [0.85811633 0.8660254 0.86240942 1. 0.90692382 0.80817439
|
|
0.80907152 0.86333169 0.8547619 0.8547619 ]
|
|
|
|
mean value: 0.8683576386302903
|
|
|
|
key: train_mcc
|
|
value: [0.97837838 0.98379816 0.98384144 0.98384144 0.98384144 0.98384144
|
|
0.9784365 0.9784365 0.98921825 0.9784365 ]
|
|
|
|
mean value: 0.9822070037330393
|
|
|
|
key: test_fscore
|
|
value: [0.92682927 0.92307692 0.91891892 1. 0.95238095 0.89473684
|
|
0.9 0.92307692 0.92682927 0.92682927]
|
|
|
|
mean value: 0.9292678364437027
|
|
|
|
key: train_fscore
|
|
value: [0.98918919 0.99191375 0.9919571 0.9919571 0.9919571 0.9919571
|
|
0.98918919 0.98918919 0.99459459 0.98918919]
|
|
|
|
mean value: 0.9911093516212641
|
|
|
|
key: test_precision
|
|
value: [0.95 1. 1. 1. 0.90909091 0.94444444
|
|
0.94736842 1. 0.95 0.95 ]
|
|
|
|
mean value: 0.9650903774587984
|
|
|
|
key: train_precision
|
|
value: [0.98918919 0.98924731 0.98930481 0.98930481 0.98930481 0.98930481
|
|
0.98918919 0.98918919 0.99459459 0.98918919]
|
|
|
|
mean value: 0.9897817914516207
|
|
|
|
key: test_recall
|
|
value: [0.9047619 0.85714286 0.85 1. 1. 0.85
|
|
0.85714286 0.85714286 0.9047619 0.9047619 ]
|
|
|
|
mean value: 0.8985714285714286
|
|
|
|
key: train_recall
|
|
value: [0.98918919 0.99459459 0.99462366 0.99462366 0.99462366 0.99462366
|
|
0.98918919 0.98918919 0.99459459 0.98918919]
|
|
|
|
mean value: 0.992444056960186
|
|
|
|
key: test_accuracy
|
|
value: [0.92857143 0.92857143 0.92682927 1. 0.95121951 0.90243902
|
|
0.90243902 0.92682927 0.92682927 0.92682927]
|
|
|
|
mean value: 0.9320557491289201
|
|
|
|
key: train_accuracy
|
|
value: [0.98918919 0.99189189 0.99191375 0.99191375 0.99191375 0.99191375
|
|
0.98921833 0.98921833 0.99460916 0.98921833]
|
|
|
|
mean value: 0.9911000218547388
|
|
|
|
key: test_roc_auc
|
|
value: [0.92857143 0.92857143 0.925 1. 0.95238095 0.90119048
|
|
0.90357143 0.92857143 0.92738095 0.92738095]
|
|
|
|
mean value: 0.9322619047619047
|
|
|
|
key: train_roc_auc
|
|
value: [0.98918919 0.99189189 0.99190642 0.99190642 0.99190642 0.99190642
|
|
0.98921825 0.98921825 0.99460913 0.98921825]
|
|
|
|
mean value: 0.9910970648067421
|
|
|
|
key: test_jcc
|
|
value: [0.86363636 0.85714286 0.85 1. 0.90909091 0.80952381
|
|
0.81818182 0.85714286 0.86363636 0.86363636]
|
|
|
|
mean value: 0.8691991341991342
|
|
|
|
key: train_jcc
|
|
value: [0.97860963 0.98395722 0.98404255 0.98404255 0.98404255 0.98404255
|
|
0.97860963 0.97860963 0.98924731 0.97860963]
|
|
|
|
mean value: 0.9823813246519049
|
|
|
|
key: TN
|
|
value: 199
|
|
|
|
mean value: 199.0
|
|
|
|
key: FP
|
|
value: 21
|
|
|
|
mean value: 21.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 185
|
|
|
|
mean value: 185.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.68
|
|
|
|
Accuracy on Blind test: 0.86
|
|
|
|
Running classifier: 15
|
|
Model_name: Multinomial
|
|
Model func: MultinomialNB()
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', MultinomialNB())])
|
|
|
|
key: fit_time
|
|
value: [0.0135808 0.01309872 0.00985193 0.00955606 0.00936365 0.01058984
|
|
0.00950861 0.00956726 0.00948119 0.00925517]
|
|
|
|
mean value: 0.01038532257080078
|
|
|
|
key: score_time
|
|
value: [0.01231837 0.0104692 0.00897026 0.00873733 0.00892115 0.00917673
|
|
0.00902939 0.00907087 0.00861287 0.00889134]
|
|
|
|
mean value: 0.009419751167297364
|
|
|
|
key: test_mcc
|
|
value: [0.28603878 0.43052839 0.08051176 0.41428571 0.17142857 0.31666667
|
|
0.37171226 0.47003614 0.41487884 0.46300848]
|
|
|
|
mean value: 0.3419095594012099
|
|
|
|
key: train_mcc
|
|
value: [0.44330153 0.36757294 0.3857278 0.39622203 0.41262884 0.41778553
|
|
0.37478201 0.40162743 0.4503185 0.37478201]
|
|
|
|
mean value: 0.40247486105604446
|
|
|
|
key: test_fscore
|
|
value: [0.63414634 0.72727273 0.57777778 0.7 0.58536585 0.65
|
|
0.66666667 0.71794872 0.72727273 0.74418605]
|
|
|
|
mean value: 0.6730636858572197
|
|
|
|
key: train_fscore
|
|
value: [0.72386059 0.68463612 0.7 0.69892473 0.71240106 0.70967742
|
|
0.68983957 0.70080863 0.71978022 0.68983957]
|
|
|
|
mean value: 0.7029767903859494
|
|
|
|
key: test_precision
|
|
value: [0.65 0.69565217 0.52 0.7 0.57142857 0.65
|
|
0.72222222 0.77777778 0.69565217 0.72727273]
|
|
|
|
mean value: 0.6710005646527386
|
|
|
|
key: train_precision
|
|
value: [0.71808511 0.6827957 0.68556701 0.69892473 0.69948187 0.70967742
|
|
0.68253968 0.69892473 0.73184358 0.68253968]
|
|
|
|
mean value: 0.6990379503120753
|
|
|
|
key: test_recall
|
|
value: [0.61904762 0.76190476 0.65 0.7 0.6 0.65
|
|
0.61904762 0.66666667 0.76190476 0.76190476]
|
|
|
|
mean value: 0.679047619047619
|
|
|
|
key: train_recall
|
|
value: [0.72972973 0.68648649 0.71505376 0.69892473 0.72580645 0.70967742
|
|
0.6972973 0.7027027 0.70810811 0.6972973 ]
|
|
|
|
mean value: 0.7071083987213018
|
|
|
|
key: test_accuracy
|
|
value: [0.64285714 0.71428571 0.53658537 0.70731707 0.58536585 0.65853659
|
|
0.68292683 0.73170732 0.70731707 0.73170732]
|
|
|
|
mean value: 0.6698606271777002
|
|
|
|
key: train_accuracy
|
|
value: [0.72162162 0.68378378 0.69272237 0.69811321 0.70619946 0.70889488
|
|
0.68733154 0.70080863 0.72506739 0.68733154]
|
|
|
|
mean value: 0.7011874408100823
|
|
|
|
key: test_roc_auc
|
|
value: [0.64285714 0.71428571 0.53928571 0.70714286 0.58571429 0.65833333
|
|
0.68452381 0.73333333 0.70595238 0.73095238]
|
|
|
|
mean value: 0.6702380952380953
|
|
|
|
key: train_roc_auc
|
|
value: [0.72162162 0.68378378 0.69266202 0.69811101 0.70614647 0.70889276
|
|
0.68735833 0.70081372 0.7250218 0.68735833]
|
|
|
|
mean value: 0.701176983435048
|
|
|
|
key: test_jcc
|
|
value: [0.46428571 0.57142857 0.40625 0.53846154 0.4137931 0.48148148
|
|
0.5 0.56 0.57142857 0.59259259]
|
|
|
|
mean value: 0.5099721573126745
|
|
|
|
key: train_jcc
|
|
value: [0.56722689 0.5204918 0.53846154 0.53719008 0.55327869 0.55
|
|
0.52653061 0.53941909 0.56223176 0.52653061]
|
|
|
|
mean value: 0.5421361074949125
|
|
|
|
key: TN
|
|
value: 136
|
|
|
|
mean value: 136.0
|
|
|
|
key: FP
|
|
value: 66
|
|
|
|
mean value: 66.0
|
|
|
|
key: FN
|
|
value: 70
|
|
|
|
mean value: 70.0
|
|
|
|
key: TP
|
|
value: 140
|
|
|
|
mean value: 140.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.33
|
|
|
|
Accuracy on Blind test: 0.68
|
|
|
|
Running classifier: 16
|
|
Model_name: Passive Aggresive
|
|
Model func: PassiveAggressiveClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
PassiveAggressiveClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01755261 0.0244925 0.02322912 0.02689171 0.02338552 0.02113056
|
|
0.02441788 0.02576876 0.02323055 0.02049685]
|
|
|
|
mean value: 0.023059606552124023
|
|
|
|
key: score_time
|
|
value: [0.00870085 0.01136637 0.01221657 0.01218843 0.01224065 0.01185799
|
|
0.01182604 0.0121398 0.01182389 0.01190352]
|
|
|
|
mean value: 0.011626410484313964
|
|
|
|
key: test_mcc
|
|
value: [0.81322028 1. 0.90649828 1. 0.95238095 0.8547619
|
|
0.90692382 1. 0.8547619 1. ]
|
|
|
|
mean value: 0.9288547149722348
|
|
|
|
key: train_mcc
|
|
value: [0.95765257 0.98379816 0.9784365 0.97866283 0.9946235 0.97306016
|
|
0.98384144 0.97866529 0.9681586 0.98384191]
|
|
|
|
mean value: 0.9780740950182457
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 1. 0.94736842 1. 0.97560976 0.92682927
|
|
0.95 1. 0.92682927 1. ]
|
|
|
|
mean value: 0.9635727622826469
|
|
|
|
key: train_fscore
|
|
value: [0.97883598 0.99191375 0.98924731 0.9893617 0.99731903 0.98652291
|
|
0.99186992 0.98930481 0.98351648 0.99191375]
|
|
|
|
mean value: 0.9889805647006705
|
|
|
|
key: test_precision
|
|
value: [0.86956522 1. 1. 1. 0.95238095 0.9047619
|
|
1. 1. 0.95 1. ]
|
|
|
|
mean value: 0.9676708074534162
|
|
|
|
key: train_precision
|
|
value: [0.95854922 0.98924731 0.98924731 0.97894737 0.99465241 0.98918919
|
|
0.99456522 0.97883598 1. 0.98924731]
|
|
|
|
mean value: 0.9862481318536436
|
|
|
|
key: test_recall
|
|
value: [0.95238095 1. 0.9 1. 1. 0.95
|
|
0.9047619 1. 0.9047619 1. ]
|
|
|
|
mean value: 0.961190476190476
|
|
|
|
key: train_recall
|
|
value: [1. 0.99459459 0.98924731 1. 1. 0.98387097
|
|
0.98918919 1. 0.96756757 0.99459459]
|
|
|
|
mean value: 0.9919064225515839
|
|
|
|
key: test_accuracy
|
|
value: [0.9047619 1. 0.95121951 1. 0.97560976 0.92682927
|
|
0.95121951 1. 0.92682927 1. ]
|
|
|
|
mean value: 0.9636469221835077
|
|
|
|
key: train_accuracy
|
|
value: [0.97837838 0.99189189 0.98921833 0.98921833 0.99730458 0.98652291
|
|
0.99191375 0.98921833 0.98382749 0.99191375]
|
|
|
|
mean value: 0.9889407736577548
|
|
|
|
key: test_roc_auc
|
|
value: [0.9047619 1. 0.95 1. 0.97619048 0.92738095
|
|
0.95238095 1. 0.92738095 1. ]
|
|
|
|
mean value: 0.9638095238095238
|
|
|
|
key: train_roc_auc
|
|
value: [0.97837838 0.99189189 0.98921825 0.98918919 0.9972973 0.98653008
|
|
0.99190642 0.98924731 0.98378378 0.99192095]
|
|
|
|
mean value: 0.9889363557105494
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 1. 0.9 1. 0.95238095 0.86363636
|
|
0.9047619 1. 0.86363636 1. ]
|
|
|
|
mean value: 0.9317748917748917
|
|
|
|
key: train_jcc
|
|
value: [0.95854922 0.98395722 0.9787234 0.97894737 0.99465241 0.97340426
|
|
0.98387097 0.97883598 0.96756757 0.98395722]
|
|
|
|
mean value: 0.9782465609858717
|
|
|
|
key: TN
|
|
value: 199
|
|
|
|
mean value: 199.0
|
|
|
|
key: FP
|
|
value: 8
|
|
|
|
mean value: 8.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 198
|
|
|
|
mean value: 198.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.82
|
|
|
|
Accuracy on Blind test: 0.92
|
|
|
|
Running classifier: 17
|
|
Model_name: Stochastic GDescent
|
|
Model func: SGDClassifier(n_jobs=10, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', SGDClassifier(n_jobs=10, random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01616383 0.01703405 0.01742053 0.01710224 0.01646566 0.01644945
|
|
0.0160799 0.01625156 0.01585746 0.01778984]
|
|
|
|
mean value: 0.016661453247070312
|
|
|
|
key: score_time
|
|
value: [0.01153302 0.01175213 0.0117445 0.01178885 0.0118649 0.0118494
|
|
0.01189089 0.01182055 0.01171756 0.01178432]
|
|
|
|
mean value: 0.011774611473083497
|
|
|
|
key: test_mcc
|
|
value: [0.8660254 0.8660254 0.85441771 0.8547619 0.7633652 0.70272837
|
|
0.698212 0.86240942 0.85441771 1. ]
|
|
|
|
mean value: 0.8322363111639266
|
|
|
|
key: train_mcc
|
|
value: [0.88528142 0.92567765 0.9681586 0.98927544 0.91221469 0.73643388
|
|
0.73681663 0.79883884 0.95160448 0.93156865]
|
|
|
|
mean value: 0.8835870293159503
|
|
|
|
key: test_fscore
|
|
value: [0.92307692 0.92307692 0.92307692 0.92682927 0.86486486 0.85106383
|
|
0.85714286 0.93333333 0.93023256 1. ]
|
|
|
|
mean value: 0.9132697480791275
|
|
|
|
key: train_fscore
|
|
value: [0.9375 0.96111111 0.98412698 0.99465241 0.95211268 0.87119438
|
|
0.87058824 0.90024331 0.97547684 0.96587927]
|
|
|
|
mean value: 0.9412885205728119
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.94736842 0.9047619 0.94117647 0.74074074
|
|
0.75 0.875 0.90909091 1. ]
|
|
|
|
mean value: 0.9068138446234422
|
|
|
|
key: train_precision
|
|
value: [0.98802395 0.98857143 0.96875 0.9893617 1. 0.77178423
|
|
0.77083333 0.81858407 0.98351648 0.93877551]
|
|
|
|
mean value: 0.92182007130104
|
|
|
|
key: test_recall
|
|
value: [0.85714286 0.85714286 0.9 0.95 0.8 1.
|
|
1. 1. 0.95238095 1. ]
|
|
|
|
mean value: 0.9316666666666666
|
|
|
|
key: train_recall
|
|
value: [0.89189189 0.93513514 1. 1. 0.90860215 1.
|
|
1. 1. 0.96756757 0.99459459]
|
|
|
|
mean value: 0.9697791339726823
|
|
|
|
key: test_accuracy
|
|
value: [0.92857143 0.92857143 0.92682927 0.92682927 0.87804878 0.82926829
|
|
0.82926829 0.92682927 0.92682927 1. ]
|
|
|
|
mean value: 0.9101045296167248
|
|
|
|
key: train_accuracy
|
|
value: [0.94054054 0.96216216 0.98382749 0.99460916 0.9541779 0.85175202
|
|
0.85175202 0.88948787 0.97574124 0.96495957]
|
|
|
|
mean value: 0.9369009980330736
|
|
|
|
key: test_roc_auc
|
|
value: [0.92857143 0.92857143 0.92619048 0.92738095 0.87619048 0.83333333
|
|
0.825 0.925 0.92619048 1. ]
|
|
|
|
mean value: 0.9096428571428572
|
|
|
|
key: train_roc_auc
|
|
value: [0.94054054 0.96216216 0.98378378 0.99459459 0.95430108 0.85135135
|
|
0.85215054 0.88978495 0.97571927 0.96503923]
|
|
|
|
mean value: 0.9369427492008138
|
|
|
|
key: test_jcc
|
|
value: [0.85714286 0.85714286 0.85714286 0.86363636 0.76190476 0.74074074
|
|
0.75 0.875 0.86956522 1. ]
|
|
|
|
mean value: 0.8432275655101742
|
|
|
|
key: train_jcc
|
|
value: [0.88235294 0.92513369 0.96875 0.9893617 0.90860215 0.77178423
|
|
0.77083333 0.81858407 0.95212766 0.93401015]
|
|
|
|
mean value: 0.8921539932035006
|
|
|
|
key: TN
|
|
value: 183
|
|
|
|
mean value: 183.0
|
|
|
|
key: FP
|
|
value: 14
|
|
|
|
mean value: 14.0
|
|
|
|
key: FN
|
|
value: 23
|
|
|
|
mean value: 23.0
|
|
|
|
key: TP
|
|
value: 192
|
|
|
|
mean value: 192.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.81
|
|
|
|
Accuracy on Blind test: 0.92
|
|
|
|
Running classifier: 18
|
|
Model_name: AdaBoost Classifier
|
|
Model func: AdaBoostClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', AdaBoostClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.16330028 0.14592481 0.14636064 0.14555335 0.1465106 0.14619303
|
|
0.14668226 0.14667153 0.14667702 0.14632678]
|
|
|
|
mean value: 0.14802002906799316
|
|
|
|
key: score_time
|
|
value: [0.01503825 0.01503277 0.01506281 0.01544476 0.01512909 0.01512289
|
|
0.01505589 0.01523805 0.01561737 0.01514649]
|
|
|
|
mean value: 0.015188837051391601
|
|
|
|
key: test_mcc
|
|
value: [0.95346259 0.95346259 1. 0.95238095 0.90692382 1.
|
|
0.95238095 1. 0.95227002 1. ]
|
|
|
|
mean value: 0.9670880922421489
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [0.97560976 0.97560976 1. 0.97560976 0.95238095 1.
|
|
0.97560976 1. 0.97674419 1. ]
|
|
|
|
mean value: 0.9831564162817708
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 0.95238095 0.90909091 1.
|
|
1. 1. 0.95454545 1. ]
|
|
|
|
mean value: 0.9816017316017316
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [0.95238095 0.95238095 1. 1. 1. 1.
|
|
0.95238095 1. 1. 1. ]
|
|
|
|
mean value: 0.9857142857142858
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [0.97619048 0.97619048 1. 0.97560976 0.95121951 1.
|
|
0.97560976 1. 0.97560976 1. ]
|
|
|
|
mean value: 0.9830429732868758
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [0.97619048 0.97619048 1. 0.97619048 0.95238095 1.
|
|
0.97619048 1. 0.975 1. ]
|
|
|
|
mean value: 0.9832142857142857
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [0.95238095 0.95238095 1. 0.95238095 0.90909091 1.
|
|
0.95238095 1. 0.95454545 1. ]
|
|
|
|
mean value: 0.9673160173160174
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 202
|
|
|
|
mean value: 202.0
|
|
|
|
key: FP
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: FN
|
|
value: 4
|
|
|
|
mean value: 4.0
|
|
|
|
key: TP
|
|
value: 203
|
|
|
|
mean value: 203.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.93
|
|
|
|
Accuracy on Blind test: 0.97
|
|
|
|
Running classifier: 19
|
|
Model_name: Bagging Classifier
|
|
Model func: BaggingClassifier(n_jobs=10, oob_score=True, random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model',
|
|
BaggingClassifier(n_jobs=10, oob_score=True,
|
|
random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.0419879 0.05815363 0.05153394 0.05133629 0.06185651 0.04853463
|
|
0.04930449 0.05603623 0.04445434 0.05075979]
|
|
|
|
mean value: 0.05139577388763428
|
|
|
|
key: score_time
|
|
value: [0.02328753 0.02999187 0.02305889 0.02622795 0.02008605 0.01859808
|
|
0.0239203 0.03557754 0.02351022 0.02400208]
|
|
|
|
mean value: 0.0248260498046875
|
|
|
|
key: test_mcc
|
|
value: [0.8660254 0.95346259 0.85441771 1. 0.8547619 0.95227002
|
|
0.95238095 0.90692382 0.90649828 0.90692382]
|
|
|
|
mean value: 0.9153664500518636
|
|
|
|
key: train_mcc
|
|
value: [0.989247 1. 0.99462366 0.99462366 0.99462366 0.98921825
|
|
1. 1. 0.99462366 0.97866283]
|
|
|
|
mean value: 0.9935622705535619
|
|
|
|
key: test_fscore
|
|
value: [0.92307692 0.97560976 0.92307692 1. 0.92682927 0.97435897
|
|
0.97560976 0.95 0.95454545 0.95 ]
|
|
|
|
mean value: 0.9553107055546078
|
|
|
|
key: train_fscore
|
|
value: [0.99456522 1. 0.99730458 0.99730458 0.99730458 0.99462366
|
|
1. 1. 0.99730458 0.98907104]
|
|
|
|
mean value: 0.9967478240397618
|
|
|
|
key: test_precision
|
|
value: [1. 1. 0.94736842 1. 0.9047619 1.
|
|
1. 1. 0.91304348 1. ]
|
|
|
|
mean value: 0.9765173804075407
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 0.99462366
|
|
1. 1. 0.99462366 1. ]
|
|
|
|
mean value: 0.9989247311827956
|
|
|
|
key: test_recall
|
|
value: [0.85714286 0.95238095 0.9 1. 0.95 0.95
|
|
0.95238095 0.9047619 1. 0.9047619 ]
|
|
|
|
mean value: 0.9371428571428572
|
|
|
|
key: train_recall
|
|
value: [0.98918919 1. 0.99462366 0.99462366 0.99462366 0.99462366
|
|
1. 1. 1. 0.97837838]
|
|
|
|
mean value: 0.9946062191223481
|
|
|
|
key: test_accuracy
|
|
value: [0.92857143 0.97619048 0.92682927 1. 0.92682927 0.97560976
|
|
0.97560976 0.95121951 0.95121951 0.95121951]
|
|
|
|
mean value: 0.9563298490127758
|
|
|
|
key: train_accuracy
|
|
value: [0.99459459 1. 0.99730458 0.99730458 0.99730458 0.99460916
|
|
1. 1. 0.99730458 0.98921833]
|
|
|
|
mean value: 0.9967640416697019
|
|
|
|
key: test_roc_auc
|
|
value: [0.92857143 0.97619048 0.92619048 1. 0.92738095 0.975
|
|
0.97619048 0.95238095 0.95 0.95238095]
|
|
|
|
mean value: 0.9564285714285713
|
|
|
|
key: train_roc_auc
|
|
value: [0.99459459 1. 0.99731183 0.99731183 0.99731183 0.99460913
|
|
1. 1. 0.99731183 0.98918919]
|
|
|
|
mean value: 0.9967640220866028
|
|
|
|
key: test_jcc
|
|
value: [0.85714286 0.95238095 0.85714286 1. 0.86363636 0.95
|
|
0.95238095 0.9047619 0.91304348 0.9047619 ]
|
|
|
|
mean value: 0.9155251270468663
|
|
|
|
key: train_jcc
|
|
value: [0.98918919 1. 0.99462366 0.99462366 0.99462366 0.98930481
|
|
1. 1. 0.99462366 0.97837838]
|
|
|
|
mean value: 0.9935367004057707
|
|
|
|
key: TN
|
|
value: 201
|
|
|
|
mean value: 201.0
|
|
|
|
key: FP
|
|
value: 13
|
|
|
|
mean value: 13.0
|
|
|
|
key: FN
|
|
value: 5
|
|
|
|
mean value: 5.0
|
|
|
|
key: TP
|
|
value: 193
|
|
|
|
mean value: 193.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.9
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 20
|
|
Model_name: Gaussian Process
|
|
Model func: GaussianProcessClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GaussianProcessClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.09415078 0.11422873 0.14302111 0.11281705 0.11488152 0.11356688
|
|
0.11255336 0.11312151 0.11276913 0.12180877]
|
|
|
|
mean value: 0.1152918815612793
|
|
|
|
key: score_time
|
|
value: [0.02256703 0.0243187 0.02233911 0.02280259 0.02246737 0.02313232
|
|
0.028368 0.02239347 0.02279854 0.02269793]
|
|
|
|
mean value: 0.02338850498199463
|
|
|
|
key: test_mcc
|
|
value: [0.62187434 0.63059263 0.48849265 0.61152662 0.56527676 0.66432098
|
|
0.65871309 0.66668392 0.4373371 0.74124932]
|
|
|
|
mean value: 0.6086067388065854
|
|
|
|
key: train_mcc
|
|
value: [0.90460775 0.92000825 0.92539732 0.90381625 0.93618785 0.91980011
|
|
0.90379406 0.92538015 0.90994228 0.9361732 ]
|
|
|
|
mean value: 0.9185107229970159
|
|
|
|
key: test_fscore
|
|
value: [0.8 0.78947368 0.66666667 0.78947368 0.75675676 0.81081081
|
|
0.8372093 0.82051282 0.66666667 0.83333333]
|
|
|
|
mean value: 0.7770903725493689
|
|
|
|
key: train_fscore
|
|
value: [0.94972067 0.95844875 0.96153846 0.95054945 0.96703297 0.95890411
|
|
0.95027624 0.96132597 0.95264624 0.96685083]
|
|
|
|
mean value: 0.9577293690791937
|
|
|
|
key: test_precision
|
|
value: [0.84210526 0.88235294 0.84615385 0.83333333 0.82352941 0.88235294
|
|
0.81818182 0.88888889 0.8 1. ]
|
|
|
|
mean value: 0.8616898443833427
|
|
|
|
key: train_precision
|
|
value: [0.98265896 0.98295455 0.98314607 0.97191011 0.98876404 0.97765363
|
|
0.97175141 0.98305085 0.98275862 0.98870056]
|
|
|
|
mean value: 0.9813348806544546
|
|
|
|
key: test_recall
|
|
value: [0.76190476 0.71428571 0.55 0.75 0.7 0.75
|
|
0.85714286 0.76190476 0.57142857 0.71428571]
|
|
|
|
mean value: 0.713095238095238
|
|
|
|
key: train_recall
|
|
value: [0.91891892 0.93513514 0.94086022 0.93010753 0.94623656 0.94086022
|
|
0.92972973 0.94054054 0.92432432 0.94594595]
|
|
|
|
mean value: 0.9352659110723627
|
|
|
|
key: test_accuracy
|
|
value: [0.80952381 0.80952381 0.73170732 0.80487805 0.7804878 0.82926829
|
|
0.82926829 0.82926829 0.70731707 0.85365854]
|
|
|
|
mean value: 0.7984901277584204
|
|
|
|
key: train_accuracy
|
|
value: [0.95135135 0.95945946 0.96226415 0.95148248 0.96765499 0.95956873
|
|
0.95148248 0.96226415 0.9541779 0.96765499]
|
|
|
|
mean value: 0.9587360676039921
|
|
|
|
key: test_roc_auc
|
|
value: [0.80952381 0.80952381 0.72738095 0.80357143 0.77857143 0.82738095
|
|
0.82857143 0.83095238 0.71071429 0.85714286]
|
|
|
|
mean value: 0.7983333333333335
|
|
|
|
key: train_roc_auc
|
|
value: [0.95135135 0.95945946 0.962322 0.95154025 0.96771287 0.9596193
|
|
0.951424 0.96220575 0.95409765 0.96759663]
|
|
|
|
mean value: 0.958732926474862
|
|
|
|
key: test_jcc
|
|
value: [0.66666667 0.65217391 0.5 0.65217391 0.60869565 0.68181818
|
|
0.72 0.69565217 0.5 0.71428571]
|
|
|
|
mean value: 0.6391466214944476
|
|
|
|
key: train_jcc
|
|
value: [0.90425532 0.92021277 0.92592593 0.90575916 0.93617021 0.92105263
|
|
0.90526316 0.92553191 0.90957447 0.93582888]
|
|
|
|
mean value: 0.9189574435559686
|
|
|
|
key: TN
|
|
value: 182
|
|
|
|
mean value: 182.0
|
|
|
|
key: FP
|
|
value: 59
|
|
|
|
mean value: 59.0
|
|
|
|
key: FN
|
|
value: 24
|
|
|
|
mean value: 24.0
|
|
|
|
key: TP
|
|
value: 147
|
|
|
|
mean value: 147.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.42
|
|
|
|
Accuracy on Blind test: 0.74
|
|
|
|
Running classifier: 21
|
|
Model_name: Gradient Boosting
|
|
Model func: GradientBoostingClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', GradientBoostingClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.5280242 0.51709318 0.52253103 0.52089429 0.53239608 0.52510905
|
|
0.51605487 0.51583099 0.51282382 0.51904821]
|
|
|
|
mean value: 0.5209805727005005
|
|
|
|
key: score_time
|
|
value: [0.00917411 0.00927401 0.00938559 0.00967669 0.00932646 0.00964832
|
|
0.00938344 0.0091908 0.00919986 0.00924921]
|
|
|
|
mean value: 0.009350848197937012
|
|
|
|
key: test_mcc
|
|
value: [1. 0.95346259 1. 1. 0.95238095 1.
|
|
0.95238095 0.95238095 0.95227002 1. ]
|
|
|
|
mean value: 0.9762875461572262
|
|
|
|
key: train_mcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_fscore
|
|
value: [1. 0.97560976 1. 1. 0.97560976 1.
|
|
0.97560976 0.97560976 0.97674419 1. ]
|
|
|
|
mean value: 0.9879183210436755
|
|
|
|
key: train_fscore
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_precision
|
|
value: [1. 1. 1. 1. 0.95238095 1.
|
|
1. 1. 0.95454545 1. ]
|
|
|
|
mean value: 0.9906926406926407
|
|
|
|
key: train_precision
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_recall
|
|
value: [1. 0.95238095 1. 1. 1. 1.
|
|
0.95238095 0.95238095 1. 1. ]
|
|
|
|
mean value: 0.9857142857142858
|
|
|
|
key: train_recall
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_accuracy
|
|
value: [1. 0.97619048 1. 1. 0.97560976 1.
|
|
0.97560976 0.97560976 0.97560976 1. ]
|
|
|
|
mean value: 0.987862950058072
|
|
|
|
key: train_accuracy
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_roc_auc
|
|
value: [1. 0.97619048 1. 1. 0.97619048 1.
|
|
0.97619048 0.97619048 0.975 1. ]
|
|
|
|
mean value: 0.9879761904761905
|
|
|
|
key: train_roc_auc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: test_jcc
|
|
value: [1. 0.95238095 1. 1. 0.95238095 1.
|
|
0.95238095 0.95238095 0.95454545 1. ]
|
|
|
|
mean value: 0.9764069264069264
|
|
|
|
key: train_jcc
|
|
value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
|
|
|
|
mean value: 1.0
|
|
|
|
key: TN
|
|
value: 204
|
|
|
|
mean value: 204.0
|
|
|
|
key: FP
|
|
value: 3
|
|
|
|
mean value: 3.0
|
|
|
|
key: FN
|
|
value: 2
|
|
|
|
mean value: 2.0
|
|
|
|
key: TP
|
|
value: 203
|
|
|
|
mean value: 203.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.91
|
|
|
|
Accuracy on Blind test: 0.96
|
|
|
|
Running classifier: 22
|
|
Model_name: QDA
|
|
Model func: QuadraticDiscriminantAnalysis()
|
|
|
|
Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear
|
|
warnings.warn("Variables are collinear")
|
|
Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', QuadraticDiscriminantAnalysis())])
|
|
|
|
key: fit_time
|
|
value: [0.02632236 0.02660441 0.03398514 0.02744603 0.02748156 0.02773428
|
|
0.02761316 0.02803922 0.02759814 0.02734256]
|
|
|
|
mean value: 0.02801668643951416
|
|
|
|
key: score_time
|
|
value: [0.01254487 0.01330376 0.01410699 0.01402807 0.01527119 0.01543665
|
|
0.01530933 0.01407933 0.01403427 0.01432133]
|
|
|
|
mean value: 0.014243578910827637
|
|
|
|
key: test_mcc
|
|
value: [0.81322028 0.76980036 0.86333169 0.74124932 0.74124932 0.6133669
|
|
0.7565654 0.65871309 0.70714286 0.80907152]
|
|
|
|
mean value: 0.7473710740902314
|
|
|
|
key: train_mcc
|
|
value: [0.93710863 1. 0.97866283 0.99462366 1. 0.96787795
|
|
0.94746155 0.99462366 0.98927544 0.98395537]
|
|
|
|
mean value: 0.9793589089718562
|
|
|
|
key: test_fscore
|
|
value: [0.90909091 0.88888889 0.93023256 0.86956522 0.86956522 0.80952381
|
|
0.88372093 0.8372093 0.85714286 0.9 ]
|
|
|
|
mean value: 0.8754939690126747
|
|
|
|
key: train_fscore
|
|
value: [0.96648045 1. 0.9893617 0.99730458 1. 0.98404255
|
|
0.97222222 0.99730458 0.99456522 0.99182561]
|
|
|
|
mean value: 0.9893106919359556
|
|
|
|
key: test_precision
|
|
value: [0.86956522 0.83333333 0.86956522 0.76923077 0.76923077 0.77272727
|
|
0.86363636 0.81818182 0.85714286 0.94736842]
|
|
|
|
mean value: 0.8369982039318424
|
|
|
|
key: train_precision
|
|
value: [1. 1. 0.97894737 1. 1. 0.97368421
|
|
1. 0.99462366 1. 1. ]
|
|
|
|
mean value: 0.9947255234861346
|
|
|
|
key: test_recall
|
|
value: [0.95238095 0.95238095 1. 1. 1. 0.85
|
|
0.9047619 0.85714286 0.85714286 0.85714286]
|
|
|
|
mean value: 0.9230952380952381
|
|
|
|
key: train_recall
|
|
value: [0.93513514 1. 1. 0.99462366 1. 0.99462366
|
|
0.94594595 1. 0.98918919 0.98378378]
|
|
|
|
mean value: 0.9843301365882011
|
|
|
|
key: test_accuracy
|
|
value: [0.9047619 0.88095238 0.92682927 0.85365854 0.85365854 0.80487805
|
|
0.87804878 0.82926829 0.85365854 0.90243902]
|
|
|
|
mean value: 0.868815331010453
|
|
|
|
key: train_accuracy
|
|
value: [0.96756757 1. 0.98921833 0.99730458 1. 0.98382749
|
|
0.97304582 0.99730458 0.99460916 0.99191375]
|
|
|
|
mean value: 0.9894791287244116
|
|
|
|
key: test_roc_auc
|
|
value: [0.9047619 0.88095238 0.92857143 0.85714286 0.85714286 0.80595238
|
|
0.87738095 0.82857143 0.85357143 0.90357143]
|
|
|
|
mean value: 0.8697619047619047
|
|
|
|
key: train_roc_auc
|
|
value: [0.96756757 1. 0.98918919 0.99731183 1. 0.98379831
|
|
0.97297297 0.99731183 0.99459459 0.99189189]
|
|
|
|
mean value: 0.9894638186573671
|
|
|
|
key: test_jcc
|
|
value: [0.83333333 0.8 0.86956522 0.76923077 0.76923077 0.68
|
|
0.79166667 0.72 0.75 0.81818182]
|
|
|
|
mean value: 0.780120857403466
|
|
|
|
key: train_jcc
|
|
value: [0.93513514 1. 0.97894737 0.99462366 1. 0.96858639
|
|
0.94594595 0.99462366 0.98918919 0.98378378]
|
|
|
|
mean value: 0.9790835121737619
|
|
|
|
key: TN
|
|
value: 169
|
|
|
|
mean value: 169.0
|
|
|
|
key: FP
|
|
value: 17
|
|
|
|
mean value: 17.0
|
|
|
|
key: FN
|
|
value: 37
|
|
|
|
mean value: 37.0
|
|
|
|
key: TP
|
|
value: 189
|
|
|
|
mean value: 189.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.25
|
|
|
|
Accuracy on Blind test: 0.7
|
|
|
|
Running classifier: 23
|
|
Model_name: Ridge Classifier
|
|
Model func: RidgeClassifier(random_state=42)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifier(random_state=42))])
|
|
|
|
key: fit_time
|
|
value: [0.01563764 0.01484275 0.01484203 0.01481032 0.02531528 0.03736687
|
|
0.03732777 0.03908205 0.03567171 0.05811954]
|
|
|
|
mean value: 0.02930159568786621
|
|
|
|
key: score_time
|
|
value: [0.01234174 0.01208162 0.01213408 0.01210332 0.02343178 0.02073622
|
|
0.02129221 0.0238409 0.02119112 0.02207351]
|
|
|
|
mean value: 0.018122649192810057
|
|
|
|
key: test_mcc
|
|
value: [0.9047619 0.8660254 0.85441771 0.95227002 0.90692382 0.95238095
|
|
0.90692382 1. 0.90649828 1. ]
|
|
|
|
mean value: 0.9250201911273045
|
|
|
|
key: train_mcc
|
|
value: [0.96756757 0.97298719 0.956873 0.96765475 0.95692987 0.96771194
|
|
0.9784365 0.97305937 0.97305937 0.96238811]
|
|
|
|
mean value: 0.9676667677298388
|
|
|
|
key: test_fscore
|
|
value: [0.95238095 0.92307692 0.92307692 0.97435897 0.95238095 0.97560976
|
|
0.95 1. 0.95454545 1. ]
|
|
|
|
mean value: 0.9605429935917741
|
|
|
|
key: train_fscore
|
|
value: [0.98378378 0.98644986 0.97849462 0.98387097 0.97837838 0.98378378
|
|
0.98918919 0.98644986 0.98644986 0.98092643]
|
|
|
|
mean value: 0.9837776750546631
|
|
|
|
key: test_precision
|
|
value: [0.95238095 1. 0.94736842 1. 0.90909091 0.95238095
|
|
1. 1. 0.91304348 1. ]
|
|
|
|
mean value: 0.9674264713166314
|
|
|
|
key: train_precision
|
|
value: [0.98378378 0.98913043 0.97849462 0.98387097 0.98369565 0.98913043
|
|
0.98918919 0.98913043 0.98913043 0.98901099]
|
|
|
|
mean value: 0.9864566944686158
|
|
|
|
key: test_recall
|
|
value: [0.95238095 0.85714286 0.9 0.95 1. 1.
|
|
0.9047619 1. 1. 1. ]
|
|
|
|
mean value: 0.9564285714285713
|
|
|
|
key: train_recall
|
|
value: [0.98378378 0.98378378 0.97849462 0.98387097 0.97311828 0.97849462
|
|
0.98918919 0.98378378 0.98378378 0.97297297]
|
|
|
|
mean value: 0.9811275791920954
|
|
|
|
key: test_accuracy
|
|
value: [0.95238095 0.92857143 0.92682927 0.97560976 0.95121951 0.97560976
|
|
0.95121951 1. 0.95121951 1. ]
|
|
|
|
mean value: 0.9612659698025551
|
|
|
|
key: train_accuracy
|
|
value: [0.98378378 0.98648649 0.97843666 0.98382749 0.97843666 0.98382749
|
|
0.98921833 0.98652291 0.98652291 0.98113208]
|
|
|
|
mean value: 0.9838194798572157
|
|
|
|
key: test_roc_auc
|
|
value: [0.95238095 0.92857143 0.92619048 0.975 0.95238095 0.97619048
|
|
0.95238095 1. 0.95 1. ]
|
|
|
|
mean value: 0.9613095238095237
|
|
|
|
key: train_roc_auc
|
|
value: [0.98378378 0.98648649 0.9784365 0.98382738 0.97845103 0.98384191
|
|
0.98921825 0.98651555 0.98651555 0.98111014]
|
|
|
|
mean value: 0.9838186573670447
|
|
|
|
key: test_jcc
|
|
value: [0.90909091 0.85714286 0.85714286 0.95 0.90909091 0.95238095
|
|
0.9047619 1. 0.91304348 1. ]
|
|
|
|
mean value: 0.9252653867871258
|
|
|
|
key: train_jcc
|
|
value: [0.96808511 0.97326203 0.95789474 0.96825397 0.95767196 0.96808511
|
|
0.97860963 0.97326203 0.97326203 0.96256684]
|
|
|
|
mean value: 0.9680953442378909
|
|
|
|
key: TN
|
|
value: 199
|
|
|
|
mean value: 199.0
|
|
|
|
key: FP
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 197
|
|
|
|
mean value: 197.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: /home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:432: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rouC_CV['Data_source'] = 'CV'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:433: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rouC_CV['Resampling'] = rs_rouC
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:438: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rouC_BT['Data_source'] = 'BT'
|
|
/home/tanu/git/LSHTM_analysis/scripts/ml/./run_7030.py:439: SettingWithCopyWarning:
|
|
A value is trying to be set on a copy of a slice from a DataFrame.
|
|
Try using .loc[row_indexer,col_indexer] = value instead
|
|
|
|
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
|
|
rouC_BT['Resampling'] = rs_rouC
|
|
blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.9
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
Running classifier: 24
|
|
Model_name: Ridge ClassifierCV
|
|
Model func: RidgeClassifierCV(cv=10)
|
|
|
|
Running model pipeline: Pipeline(steps=[('prep',
|
|
ColumnTransformer(remainder='passthrough',
|
|
transformers=[('num', MinMaxScaler(),
|
|
Index(['consurf_score', 'snap2_score', 'provean_score',
|
|
'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2',
|
|
'contacts', 'electro_rr', 'electro_mm',
|
|
...
|
|
'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'logorI',
|
|
'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all',
|
|
'lineage_count_unique'],
|
|
dtype='object', length=168)),
|
|
('cat', OneHotEncoder(),
|
|
Index(['ss_class', 'aa_prop_change', 'electrostatics_change',
|
|
'polarity_change', 'water_change', 'active_site'],
|
|
dtype='object'))])),
|
|
('model', RidgeClassifierCV(cv=10))])
|
|
|
|
key: fit_time
|
|
value: [0.32478094 0.20739317 0.20109296 0.32834768 0.2527113 0.1824615
|
|
0.26740456 0.40533066 0.39677286 0.23047733]
|
|
|
|
mean value: 0.2796772956848145
|
|
|
|
key: score_time
|
|
value: [0.01243234 0.01315236 0.02611136 0.01238418 0.01246881 0.01612306
|
|
0.02184033 0.02457547 0.01257014 0.01241994]
|
|
|
|
mean value: 0.01640779972076416
|
|
|
|
key: test_mcc
|
|
value: [0.9047619 0.8660254 0.85441771 0.95227002 0.90692382 0.95238095
|
|
0.90692382 1. 0.90649828 1. ]
|
|
|
|
mean value: 0.9250201911273045
|
|
|
|
key: train_mcc
|
|
value: [0.96756757 0.97298719 0.956873 0.96765475 0.95692987 0.96771194
|
|
0.9784365 0.97305937 0.97305937 0.96238811]
|
|
|
|
mean value: 0.9676667677298388
|
|
|
|
key: test_fscore
|
|
value: [0.95238095 0.92307692 0.92307692 0.97435897 0.95238095 0.97560976
|
|
0.95 1. 0.95454545 1. ]
|
|
|
|
mean value: 0.9605429935917741
|
|
|
|
key: train_fscore
|
|
value: [0.98378378 0.98644986 0.97849462 0.98387097 0.97837838 0.98378378
|
|
0.98918919 0.98644986 0.98644986 0.98092643]
|
|
|
|
mean value: 0.9837776750546631
|
|
|
|
key: test_precision
|
|
value: [0.95238095 1. 0.94736842 1. 0.90909091 0.95238095
|
|
1. 1. 0.91304348 1. ]
|
|
|
|
mean value: 0.9674264713166314
|
|
|
|
key: train_precision
|
|
value: [0.98378378 0.98913043 0.97849462 0.98387097 0.98369565 0.98913043
|
|
0.98918919 0.98913043 0.98913043 0.98901099]
|
|
|
|
mean value: 0.9864566944686158
|
|
|
|
key: test_recall
|
|
value: [0.95238095 0.85714286 0.9 0.95 1. 1.
|
|
0.9047619 1. 1. 1. ]
|
|
|
|
mean value: 0.9564285714285713
|
|
|
|
key: train_recall
|
|
value: [0.98378378 0.98378378 0.97849462 0.98387097 0.97311828 0.97849462
|
|
0.98918919 0.98378378 0.98378378 0.97297297]
|
|
|
|
mean value: 0.9811275791920954
|
|
|
|
key: test_accuracy
|
|
value: [0.95238095 0.92857143 0.92682927 0.97560976 0.95121951 0.97560976
|
|
0.95121951 1. 0.95121951 1. ]
|
|
|
|
mean value: 0.9612659698025551
|
|
|
|
key: train_accuracy
|
|
value: [0.98378378 0.98648649 0.97843666 0.98382749 0.97843666 0.98382749
|
|
0.98921833 0.98652291 0.98652291 0.98113208]
|
|
|
|
mean value: 0.9838194798572157
|
|
|
|
key: test_roc_auc
|
|
value: [0.95238095 0.92857143 0.92619048 0.975 0.95238095 0.97619048
|
|
0.95238095 1. 0.95 1. ]
|
|
|
|
mean value: 0.9613095238095237
|
|
|
|
key: train_roc_auc
|
|
value: [0.98378378 0.98648649 0.9784365 0.98382738 0.97845103 0.98384191
|
|
0.98921825 0.98651555 0.98651555 0.98111014]
|
|
|
|
mean value: 0.9838186573670447
|
|
|
|
key: test_jcc
|
|
value: [0.90909091 0.85714286 0.85714286 0.95 0.90909091 0.95238095
|
|
0.9047619 1. 0.91304348 1. ]
|
|
|
|
mean value: 0.9252653867871258
|
|
|
|
key: train_jcc
|
|
value: [0.96808511 0.97326203 0.95789474 0.96825397 0.95767196 0.96808511
|
|
0.97860963 0.97326203 0.97326203 0.96256684]
|
|
|
|
mean value: 0.9680953442378909
|
|
|
|
key: TN
|
|
value: 199
|
|
|
|
mean value: 199.0
|
|
|
|
key: FP
|
|
value: 9
|
|
|
|
mean value: 9.0
|
|
|
|
key: FN
|
|
value: 7
|
|
|
|
mean value: 7.0
|
|
|
|
key: TP
|
|
value: 197
|
|
|
|
mean value: 197.0
|
|
|
|
key: trainingY_neg
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: trainingY_pos
|
|
value: 206
|
|
|
|
mean value: 206.0
|
|
|
|
key: blindY_neg
|
|
value: 52
|
|
|
|
mean value: 52.0
|
|
|
|
key: blindY_pos
|
|
value: 103
|
|
|
|
mean value: 103.0
|
|
|
|
MCC on Blind test: 0.9
|
|
|
|
Accuracy on Blind test: 0.95
|
|
|
|
PASS: sorting df by score that is mapped onto the order I want
|
|
|
|
Concatenating dfs with different resampling methods [WF]: 70/30
|
|
No. of dfs combining: 10
|
|
The sampling methods are:
|
|
Resampling
|
|
Logistic Regression none
|
|
Logistic Regression smnc
|
|
Logistic Regression ros
|
|
Logistic Regression rus
|
|
Logistic Regression rouC
|
|
|
|
PASS: 10 dfs successfully combined
|
|
nrows in combined_df_wf: 240
|
|
ncols in combined_df_wf: 9
|
|
|
|
Concatenating dfs with different resampling methods: 70/30
|
|
No. of dfs combining: 5
|
|
The sampling methods are:
|
|
Resampling training_size
|
|
0 none 312
|
|
24 smnc 412
|
|
48 ros 412
|
|
72 rus 212
|
|
96 rouC 412
|
|
|
|
PASS: 5 dfs successfully combined
|
|
nrows in combined_df: 120
|
|
ncols in combined_df: 32
|
|
|
|
File successfully written: /home/tanu/git/Data/isoniazid/output/ml/tts_7030/katg_baselineC_7030.csv
|
|
|
|
File successfully written: /home/tanu/git/Data/isoniazid/output/ml/tts_7030/katg_baselineC_ext_7030.csv
|