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logs.log
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2023-11-09 19:00:57,786:INFO:PyCaret Supervised Module
2023-11-09 19:00:57,788:INFO:ML Usecase: classification
2023-11-09 19:00:57,788:INFO:version 2.3.10
2023-11-09 19:00:57,788:INFO:Initializing setup()
2023-11-09 19:00:57,789:INFO:setup(target=made_purchase, ml_usecase=classification, available_plots={'parameter': 'Hyperparameters', 'auc': 'AUC', 'confusion_matrix': 'Confusion Matrix', 'threshold': 'Threshold', 'pr': 'Precision Recall', 'error': 'Prediction Error', 'class_report': 'Class Report', 'rfe': 'Feature Selection', 'learning': 'Learning Curve', 'manifold': 'Manifold Learning', 'calibration': 'Calibration Curve', 'vc': 'Validation Curve', 'dimension': 'Dimensions', 'feature': 'Feature Importance', 'feature_all': 'Feature Importance (All)', 'boundary': 'Decision Boundary', 'lift': 'Lift Chart', 'gain': 'Gain Chart', 'tree': 'Decision Tree', 'ks': 'KS Statistic Plot'}, train_size=0.8, test_data=None, preprocess=True, imputation_type=simple, iterative_imputation_iters=5, categorical_features=['country_code'], categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features={'member_rating': ['1', '2', '3', '4', '5']}, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=['tag_count', 'tag_count_by_optin_day', 'tag_aws_webinar', 'tag_learning_lab', 'tag_learning_lab_05', 'tag_learning_lab_09', 'tag_learning_lab_11', 'tag_learning_lab_12', 'tag_learning_lab_13', 'tag_learning_lab_14', 'tag_learning_lab_15', 'tag_learning_lab_16', 'tag_learning_lab_17', 'tag_learning_lab_18', 'tag_learning_lab_19', 'tag_learning_lab_20', 'tag_learning_lab_21', 'tag_learning_lab_22', 'tag_learning_lab_23', 'tag_learning_lab_24', 'tag_learning_lab_25', 'tag_learning_lab_26', 'tag_learning_lab_27', 'tag_learning_lab_28', 'tag_learning_lab_29', 'tag_learning_lab_30', 'tag_learning_lab_31', 'tag_learning_lab_32', 'tag_learning_lab_33', 'tag_learning_lab_34', 'tag_learning_lab_35', 'tag_learning_lab_36', 'tag_learning_lab_37', 'tag_learning_lab_38', 'tag_learning_lab_39', 'tag_learning_lab_40', 'tag_learning_lab_41', 'tag_learning_lab_42', 'tag_learning_lab_43', 'tag_learning_lab_44', 'tag_learning_lab_45', 'tag_learning_lab_46', 'tag_learning_lab_47', 'tag_time_series_webinar', 'tag_webinar', 'tag_webinar_01', 'tag_webinar_no_degree', 'tag_webinar_no_degree_02', 'optin_days'], numeric_imputation=mean, numeric_iterative_imputer=lightgbm, date_features=None, ignore_features=None, normalize=False, normalize_method=zscore, transformation=False, transformation_method=yeo-johnson, handle_unknown_categorical=True, unknown_categorical_method=least_frequent, pca=False, pca_method=linear, pca_components=None, ignore_low_variance=False, combine_rare_levels=True, rare_level_threshold=0.005, bin_numeric_features=None, remove_outliers=False, outliers_threshold=0.05, remove_multicollinearity=False, multicollinearity_threshold=0.9, remove_perfect_collinearity=True, create_clusters=False, cluster_iter=20, polynomial_features=False, polynomial_degree=2, trigonometry_features=False, polynomial_threshold=0.1, group_features=None, group_names=None, feature_selection=False, feature_selection_threshold=0.8, feature_selection_method=classic, feature_interaction=False, feature_ratio=False, interaction_threshold=0.01, fix_imbalance=False, fix_imbalance_method=None, transform_target=False, transform_target_method=box-cox, data_split_shuffle=True, data_split_stratify=False, fold_strategy=stratifiedkfold, fold=5, fold_shuffle=False, fold_groups=None, n_jobs=1, use_gpu=False, custom_pipeline=None, html=True, session_id=123, log_experiment=True, experiment_name=email_lead_scoring_0, experiment_custom_tags=None, log_plots=False, log_profile=False, log_data=False, silent=False, verbose=True, profile=False, profile_kwargs=None, display=None)
2023-11-09 19:00:57,790:INFO:Checking environment
2023-11-09 19:00:57,791:INFO:python_version: 3.7.1
2023-11-09 19:00:57,791:INFO:python_build: ('default', 'Dec 14 2018 13:28:58')
2023-11-09 19:00:57,791:INFO:machine: x86_64
2023-11-09 19:00:57,791:INFO:platform: Darwin-21.6.0-x86_64-i386-64bit
2023-11-09 19:00:57,792:INFO:Memory: svmem(total=8589934592, available=2186022912, percent=74.6, used=4106989568, free=19251200, active=2167889920, inactive=2164211712, wired=1939099648)
2023-11-09 19:00:57,793:INFO:Physical Core: 2
2023-11-09 19:00:57,793:INFO:Logical Core: 4
2023-11-09 19:00:57,793:INFO:Checking libraries
2023-11-09 19:00:57,794:INFO:pd==1.2.3
2023-11-09 19:00:57,794:INFO:numpy==1.19.5
2023-11-09 19:00:57,794:INFO:sklearn==0.23.2
2023-11-09 19:00:57,795:INFO:lightgbm==3.3.5
2023-11-09 19:01:02,388:INFO:catboost==1.1.1
2023-11-09 19:01:02,391:INFO:xgboost==1.6.2
2023-11-09 19:01:02,391:INFO:mlflow==1.28.0
2023-11-09 19:01:02,391:INFO:Checking Exceptions
2023-11-09 19:01:02,397:INFO:Declaring global variables
2023-11-09 19:01:02,397:INFO:USI: c25b
2023-11-09 19:01:02,397:INFO:pycaret_globals: {'target_param', 'imputation_classifier', 'n_jobs_param', 'fix_imbalance_param', 'fold_groups_param', 'transform_target_param', 'data_before_preprocess', '_all_models_internal', 'logging_param', 'fix_imbalance_method_param', 'html_param', '_ml_usecase', 'stratify_param', '_internal_pipeline', 'X', 'y_test', 'fold_shuffle_param', 'USI', 'transform_target_method_param', '_gpu_n_jobs_param', 'pycaret_globals', '_all_models', 'X_train', 'fold_groups_param_full', 'dashboard_logger', 'fold_generator', 'experiment__', 'X_test', 'iterative_imputation_iters_param', 'prep_pipe', 'imputation_regressor', '_available_plots', 'master_model_container', 'log_plots_param', 'fold_param', 'seed', 'display_container', '_all_metrics', 'gpu_param', 'y', 'exp_name_log', 'create_model_container', 'y_train'}
2023-11-09 19:01:02,397:INFO:Preparing display monitor
2023-11-09 19:01:02,398:INFO:Preparing display monitor
2023-11-09 19:01:02,670:INFO:Importing libraries
2023-11-09 19:01:02,670:INFO:Copying data for preprocessing
2023-11-09 19:01:02,781:INFO:Declaring preprocessing parameters
2023-11-09 19:01:02,810:INFO:Creating preprocessing pipeline
2023-11-09 19:01:02,943:INFO:Preprocessing pipeline created successfully
2023-11-09 19:01:02,944:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2023-11-09 19:01:02,945:INFO:Creating global containers
2023-11-09 19:01:02,951:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2023-11-09 19:14:19,438:INFO:Creating grid variables
2023-11-09 19:14:19,652:INFO:Logging experiment in MLFlow
2023-11-09 19:14:20,541:INFO:SubProcess save_model() called ==================================
2023-11-09 19:14:20,730:INFO:Initializing save_model()
2023-11-09 19:14:20,731:INFO:save_model(model=Pipeline(memory=None,
steps=[('dtypes',
DataTypes_Auto_infer(categorical_features=['country_code'],
display_types=True, features_todrop=[],
id_columns=[],
ml_usecase='classification',
numerical_features=['tag_count',
'tag_count_by_optin_day',
'tag_aws_webinar',
'tag_learning_lab',
'tag_learning_lab_05',
'tag_learning_lab_09',
'tag_learning_lab_11',
'tag_learning...
('scaling', 'passthrough'), ('P_transform', 'passthrough'),
('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
('cluster_all', 'passthrough'),
('dummy', Dummify(target='made_purchase')),
('fix_perfect', Remove_100(target='made_purchase')),
('clean_names', Clean_Colum_Names()),
('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
('dfs', 'passthrough'), ('pca', 'passthrough')],
verbose=False), model_name=Transformation Pipeline, prep_pipe_=Pipeline(memory=None,
steps=[('dtypes',
DataTypes_Auto_infer(categorical_features=['country_code'],
display_types=True, features_todrop=[],
id_columns=[],
ml_usecase='classification',
numerical_features=['tag_count',
'tag_count_by_optin_day',
'tag_aws_webinar',
'tag_learning_lab',
'tag_learning_lab_05',
'tag_learning_lab_09',
'tag_learning_lab_11',
'tag_learning...
('scaling', 'passthrough'), ('P_transform', 'passthrough'),
('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
('cluster_all', 'passthrough'),
('dummy', Dummify(target='made_purchase')),
('fix_perfect', Remove_100(target='made_purchase')),
('clean_names', Clean_Colum_Names()),
('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
('dfs', 'passthrough'), ('pca', 'passthrough')],
verbose=False), verbose=False, kwargs={})
2023-11-09 19:14:20,731:INFO:Adding model into prep_pipe
2023-11-09 19:14:20,742:WARNING:Only Model saved as it was a pipeline.
2023-11-09 19:14:20,923:INFO:Transformation Pipeline.pkl saved in current working directory
2023-11-09 19:14:21,058:INFO:Pipeline(memory=None,
steps=[('dtypes',
DataTypes_Auto_infer(categorical_features=['country_code'],
display_types=True, features_todrop=[],
id_columns=[],
ml_usecase='classification',
numerical_features=['tag_count',
'tag_count_by_optin_day',
'tag_aws_webinar',
'tag_learning_lab',
'tag_learning_lab_05',
'tag_learning_lab_09',
'tag_learning_lab_11',
'tag_learning...
('scaling', 'passthrough'), ('P_transform', 'passthrough'),
('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
('cluster_all', 'passthrough'),
('dummy', Dummify(target='made_purchase')),
('fix_perfect', Remove_100(target='made_purchase')),
('clean_names', Clean_Colum_Names()),
('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
('dfs', 'passthrough'), ('pca', 'passthrough')],
verbose=False)
2023-11-09 19:14:21,058:INFO:save_model() successfully completed......................................
2023-11-09 19:14:22,734:INFO:SubProcess save_model() end ==================================
2023-11-09 19:14:22,762:INFO:create_model_container: 0
2023-11-09 19:14:22,762:INFO:master_model_container: 0
2023-11-09 19:14:22,762:INFO:display_container: 1
2023-11-09 19:14:22,773:INFO:Pipeline(memory=None,
steps=[('dtypes',
DataTypes_Auto_infer(categorical_features=['country_code'],
display_types=True, features_todrop=[],
id_columns=[],
ml_usecase='classification',
numerical_features=['tag_count',
'tag_count_by_optin_day',
'tag_aws_webinar',
'tag_learning_lab',
'tag_learning_lab_05',
'tag_learning_lab_09',
'tag_learning_lab_11',
'tag_learning...
('scaling', 'passthrough'), ('P_transform', 'passthrough'),
('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
('cluster_all', 'passthrough'),
('dummy', Dummify(target='made_purchase')),
('fix_perfect', Remove_100(target='made_purchase')),
('clean_names', Clean_Colum_Names()),
('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
('dfs', 'passthrough'), ('pca', 'passthrough')],
verbose=False)
2023-11-09 19:14:22,773:INFO:setup() succesfully completed......................................
2023-11-09 19:15:09,427:INFO:Initializing get_config()
2023-11-09 19:15:09,428:INFO:get_config(variable=data_before_preprocess)
2023-11-09 19:15:09,469:INFO:Global variable: data_before_preprocess returned as member_rating country_code tag_count made_purchase optin_days \
0 2 IN 6 1 -589
1 4 other 0 1 -773
2 2 other 0 1 -773
3 2 CO 3 1 -286
4 2 other 0 0 -261
... ... ... ... ... ...
19914 2 other 0 0 -774
19915 1 BR 0 0 -660
19916 2 IN 0 0 -479
19917 2 other 2 0 -434
19918 2 AE 0 0 -195
tag_count_by_optin_day tag_aws_webinar tag_learning_lab \
0 0.010169 0.0 1.0
1 0.000000 0.0 0.0
2 0.000000 0.0 0.0
3 0.010453 0.0 1.0
4 0.000000 0.0 0.0
... ... ... ...
19914 0.000000 0.0 0.0
19915 0.000000 0.0 0.0
19916 0.000000 0.0 0.0
19917 0.004598 0.0 1.0
19918 0.000000 0.0 0.0
tag_learning_lab_05 tag_learning_lab_09 ... tag_learning_lab_43 \
0 0.0 0.0 ... 0.0
1 0.0 0.0 ... 0.0
2 0.0 0.0 ... 0.0
3 0.0 0.0 ... 0.0
4 0.0 0.0 ... 0.0
... ... ... ... ...
19914 0.0 0.0 ... 0.0
19915 0.0 0.0 ... 0.0
19916 0.0 0.0 ... 0.0
19917 0.0 0.0 ... 0.0
19918 0.0 0.0 ... 0.0
tag_learning_lab_44 tag_learning_lab_45 tag_learning_lab_46 \
0 0.0 0.0 0.0
1 0.0 0.0 0.0
2 0.0 0.0 0.0
3 0.0 0.0 0.0
4 0.0 0.0 0.0
... ... ... ...
19914 0.0 0.0 0.0
19915 0.0 0.0 0.0
19916 0.0 0.0 0.0
19917 0.0 0.0 0.0
19918 0.0 0.0 0.0
tag_learning_lab_47 tag_time_series_webinar tag_webinar \
0 0.0 0.0 1.0
1 0.0 0.0 0.0
2 0.0 0.0 0.0
3 0.0 0.0 0.0
4 0.0 0.0 0.0
... ... ... ...
19914 0.0 0.0 0.0
19915 0.0 0.0 0.0
19916 0.0 0.0 0.0
19917 0.0 0.0 0.0
19918 0.0 0.0 0.0
tag_webinar_01 tag_webinar_no_degree tag_webinar_no_degree_02
0 0.0 0.0 0.0
1 0.0 0.0 0.0
2 0.0 0.0 0.0
3 0.0 0.0 0.0
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2023-11-09 19:15:09,470:INFO:get_config() succesfully completed......................................
2023-11-09 19:16:19,919:INFO:gpu_param set to False
2023-11-09 19:19:55,440:INFO:Initializing compare_models()
2023-11-09 19:19:55,441:INFO:compare_models(include=None, fold=None, round=4, cross_validation=True, sort=AUC, n_select=3, budget_time=3, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, display=None, exclude=None)
2023-11-09 19:19:55,442:INFO:Checking exceptions
2023-11-09 19:19:55,489:INFO:Preparing display monitor
2023-11-09 19:19:55,491:INFO:Preparing display monitor
2023-11-09 19:19:55,674:INFO:Time budget is 3 minutes
2023-11-09 19:19:55,676:INFO:Initializing Logistic Regression
2023-11-09 19:19:55,676:INFO:Total runtime is 2.8085708618164062e-05 minutes
2023-11-09 19:19:55,728:INFO:SubProcess create_model() called ==================================
2023-11-09 19:19:55,732:INFO:Initializing create_model()
2023-11-09 19:19:55,741:INFO:create_model(estimator=lr, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:19:55,756:INFO:Checking exceptions
2023-11-09 19:19:55,757:INFO:Importing libraries
2023-11-09 19:19:55,757:INFO:Copying training dataset
2023-11-09 19:19:55,787:INFO:Defining folds
2023-11-09 19:19:55,788:INFO:Declaring metric variables
2023-11-09 19:19:55,843:INFO:Importing untrained model
2023-11-09 19:19:55,906:INFO:Logistic Regression Imported succesfully
2023-11-09 19:19:56,001:INFO:Starting cross validation
2023-11-09 19:19:56,005:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:20:05,534:INFO:Calculating mean and std
2023-11-09 19:20:05,536:INFO:Creating metrics dataframe
2023-11-09 19:20:05,560:INFO:Uploading results into container
2023-11-09 19:20:05,561:INFO:Uploading model into container now
2023-11-09 19:20:05,561:INFO:create_model_container: 1
2023-11-09 19:20:05,561:INFO:master_model_container: 1
2023-11-09 19:20:05,561:INFO:display_container: 2
2023-11-09 19:20:05,564:INFO:LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=1000,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=123, solver='lbfgs', tol=0.0001, verbose=0,
warm_start=False)
2023-11-09 19:20:05,564:INFO:create_model() succesfully completed......................................
2023-11-09 19:20:06,139:INFO:SubProcess create_model() end ==================================
2023-11-09 19:20:06,140:INFO:Creating metrics dataframe
2023-11-09 19:20:06,193:INFO:Initializing K Neighbors Classifier
2023-11-09 19:20:06,193:INFO:Total runtime is 0.17531283299128217 minutes
2023-11-09 19:20:06,248:INFO:SubProcess create_model() called ==================================
2023-11-09 19:20:06,249:INFO:Initializing create_model()
2023-11-09 19:20:06,250:INFO:create_model(estimator=knn, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:20:06,250:INFO:Checking exceptions
2023-11-09 19:20:06,250:INFO:Importing libraries
2023-11-09 19:20:06,250:INFO:Copying training dataset
2023-11-09 19:20:06,259:INFO:Defining folds
2023-11-09 19:20:06,259:INFO:Declaring metric variables
2023-11-09 19:20:06,308:INFO:Importing untrained model
2023-11-09 19:20:06,369:INFO:K Neighbors Classifier Imported succesfully
2023-11-09 19:20:06,436:INFO:Starting cross validation
2023-11-09 19:20:06,436:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:20:10,176:INFO:Calculating mean and std
2023-11-09 19:20:10,177:INFO:Creating metrics dataframe
2023-11-09 19:20:10,228:INFO:Uploading results into container
2023-11-09 19:20:10,235:INFO:Uploading model into container now
2023-11-09 19:20:10,236:INFO:create_model_container: 2
2023-11-09 19:20:10,236:INFO:master_model_container: 2
2023-11-09 19:20:10,238:INFO:display_container: 2
2023-11-09 19:20:10,239:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
2023-11-09 19:20:10,240:INFO:create_model() succesfully completed......................................
2023-11-09 19:20:10,505:INFO:SubProcess create_model() end ==================================
2023-11-09 19:20:10,506:INFO:Creating metrics dataframe
2023-11-09 19:20:10,592:INFO:Initializing Naive Bayes
2023-11-09 19:20:10,592:INFO:Total runtime is 0.24863404830296837 minutes
2023-11-09 19:20:10,702:INFO:SubProcess create_model() called ==================================
2023-11-09 19:20:10,709:INFO:Initializing create_model()
2023-11-09 19:20:10,709:INFO:create_model(estimator=nb, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:20:10,709:INFO:Checking exceptions
2023-11-09 19:20:10,709:INFO:Importing libraries
2023-11-09 19:20:10,709:INFO:Copying training dataset
2023-11-09 19:20:10,711:INFO:Defining folds
2023-11-09 19:20:10,711:INFO:Declaring metric variables
2023-11-09 19:20:10,760:INFO:Importing untrained model
2023-11-09 19:20:10,796:INFO:Naive Bayes Imported succesfully
2023-11-09 19:20:10,881:INFO:Starting cross validation
2023-11-09 19:20:10,885:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:20:11,540:INFO:Calculating mean and std
2023-11-09 19:20:11,541:INFO:Creating metrics dataframe
2023-11-09 19:20:11,558:INFO:Uploading results into container
2023-11-09 19:20:11,559:INFO:Uploading model into container now
2023-11-09 19:20:11,559:INFO:create_model_container: 3
2023-11-09 19:20:11,559:INFO:master_model_container: 3
2023-11-09 19:20:11,579:INFO:display_container: 2
2023-11-09 19:20:11,580:INFO:GaussianNB(priors=None, var_smoothing=1e-09)
2023-11-09 19:20:11,580:INFO:create_model() succesfully completed......................................
2023-11-09 19:20:11,743:INFO:SubProcess create_model() end ==================================
2023-11-09 19:20:11,743:INFO:Creating metrics dataframe
2023-11-09 19:20:11,789:INFO:Initializing Decision Tree Classifier
2023-11-09 19:20:11,789:INFO:Total runtime is 0.2685871998469035 minutes
2023-11-09 19:20:11,819:INFO:SubProcess create_model() called ==================================
2023-11-09 19:20:11,820:INFO:Initializing create_model()
2023-11-09 19:20:11,820:INFO:create_model(estimator=dt, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:20:11,821:INFO:Checking exceptions
2023-11-09 19:20:11,821:INFO:Importing libraries
2023-11-09 19:20:11,821:INFO:Copying training dataset
2023-11-09 19:20:11,824:INFO:Defining folds
2023-11-09 19:20:11,825:INFO:Declaring metric variables
2023-11-09 19:20:11,887:INFO:Importing untrained model
2023-11-09 19:20:11,925:INFO:Decision Tree Classifier Imported succesfully
2023-11-09 19:20:12,021:INFO:Starting cross validation
2023-11-09 19:20:12,022:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:20:13,119:INFO:Calculating mean and std
2023-11-09 19:20:13,120:INFO:Creating metrics dataframe
2023-11-09 19:20:13,128:INFO:Uploading results into container
2023-11-09 19:20:13,128:INFO:Uploading model into container now
2023-11-09 19:20:13,128:INFO:create_model_container: 4
2023-11-09 19:20:13,129:INFO:master_model_container: 4
2023-11-09 19:20:13,129:INFO:display_container: 2
2023-11-09 19:20:13,130:INFO:DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
max_depth=None, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort='deprecated',
random_state=123, splitter='best')
2023-11-09 19:20:13,130:INFO:create_model() succesfully completed......................................
2023-11-09 19:20:13,286:INFO:SubProcess create_model() end ==================================
2023-11-09 19:20:13,286:INFO:Creating metrics dataframe
2023-11-09 19:20:13,343:INFO:Initializing SVM - Linear Kernel
2023-11-09 19:20:13,343:INFO:Total runtime is 0.29447946945826214 minutes
2023-11-09 19:20:13,444:INFO:SubProcess create_model() called ==================================
2023-11-09 19:20:13,445:INFO:Initializing create_model()
2023-11-09 19:20:13,445:INFO:create_model(estimator=svm, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:20:13,445:INFO:Checking exceptions
2023-11-09 19:20:13,445:INFO:Importing libraries
2023-11-09 19:20:13,445:INFO:Copying training dataset
2023-11-09 19:20:13,471:INFO:Defining folds
2023-11-09 19:20:13,472:INFO:Declaring metric variables
2023-11-09 19:20:13,511:INFO:Importing untrained model
2023-11-09 19:20:13,545:INFO:SVM - Linear Kernel Imported succesfully
2023-11-09 19:20:13,641:INFO:Starting cross validation
2023-11-09 19:20:13,643:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:20:16,958:INFO:Calculating mean and std
2023-11-09 19:20:16,959:INFO:Creating metrics dataframe
2023-11-09 19:20:16,971:INFO:Uploading results into container
2023-11-09 19:20:16,971:INFO:Uploading model into container now
2023-11-09 19:20:16,972:INFO:create_model_container: 5
2023-11-09 19:20:16,972:INFO:master_model_container: 5
2023-11-09 19:20:16,972:INFO:display_container: 2
2023-11-09 19:20:16,973:INFO:SGDClassifier(alpha=0.0001, average=False, class_weight=None,
early_stopping=False, epsilon=0.1, eta0=0.001, fit_intercept=True,
l1_ratio=0.15, learning_rate='optimal', loss='hinge',
max_iter=1000, n_iter_no_change=5, n_jobs=1, penalty='l2',
power_t=0.5, random_state=123, shuffle=True, tol=0.001,
validation_fraction=0.1, verbose=0, warm_start=False)
2023-11-09 19:20:16,973:INFO:create_model() succesfully completed......................................
2023-11-09 19:20:17,117:INFO:SubProcess create_model() end ==================================
2023-11-09 19:20:17,118:INFO:Creating metrics dataframe
2023-11-09 19:20:17,174:INFO:Initializing Ridge Classifier
2023-11-09 19:20:17,174:INFO:Total runtime is 0.35833713610967 minutes
2023-11-09 19:20:17,256:INFO:SubProcess create_model() called ==================================
2023-11-09 19:20:17,257:INFO:Initializing create_model()
2023-11-09 19:20:17,257:INFO:create_model(estimator=ridge, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:20:17,257:INFO:Checking exceptions
2023-11-09 19:20:17,261:INFO:Importing libraries
2023-11-09 19:20:17,262:INFO:Copying training dataset
2023-11-09 19:20:17,274:INFO:Defining folds
2023-11-09 19:20:17,274:INFO:Declaring metric variables
2023-11-09 19:20:17,319:INFO:Importing untrained model
2023-11-09 19:20:17,352:INFO:Ridge Classifier Imported succesfully
2023-11-09 19:20:17,626:INFO:Starting cross validation
2023-11-09 19:20:17,632:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:20:18,651:INFO:Calculating mean and std
2023-11-09 19:20:18,659:INFO:Creating metrics dataframe
2023-11-09 19:20:18,858:INFO:Uploading results into container
2023-11-09 19:20:18,859:INFO:Uploading model into container now
2023-11-09 19:20:18,860:INFO:create_model_container: 6
2023-11-09 19:20:18,860:INFO:master_model_container: 6
2023-11-09 19:20:18,860:INFO:display_container: 2
2023-11-09 19:20:18,861:INFO:RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,
max_iter=None, normalize=False, random_state=123, solver='auto',
tol=0.001)
2023-11-09 19:20:18,861:INFO:create_model() succesfully completed......................................
2023-11-09 19:20:19,107:INFO:SubProcess create_model() end ==================================
2023-11-09 19:20:19,107:INFO:Creating metrics dataframe
2023-11-09 19:20:19,153:INFO:Initializing Random Forest Classifier
2023-11-09 19:20:19,153:INFO:Total runtime is 0.39132013320922854 minutes
2023-11-09 19:20:19,178:INFO:SubProcess create_model() called ==================================
2023-11-09 19:20:19,179:INFO:Initializing create_model()
2023-11-09 19:20:19,180:INFO:create_model(estimator=rf, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:20:19,184:INFO:Checking exceptions
2023-11-09 19:20:19,185:INFO:Importing libraries
2023-11-09 19:20:19,185:INFO:Copying training dataset
2023-11-09 19:20:19,189:INFO:Defining folds
2023-11-09 19:20:19,189:INFO:Declaring metric variables
2023-11-09 19:20:19,255:INFO:Importing untrained model
2023-11-09 19:20:19,295:INFO:Random Forest Classifier Imported succesfully
2023-11-09 19:20:19,375:INFO:Starting cross validation
2023-11-09 19:20:19,385:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:20:35,839:INFO:Calculating mean and std
2023-11-09 19:20:35,841:INFO:Creating metrics dataframe
2023-11-09 19:20:35,856:INFO:Uploading results into container
2023-11-09 19:20:35,857:INFO:Uploading model into container now
2023-11-09 19:20:35,857:INFO:create_model_container: 7
2023-11-09 19:20:35,857:INFO:master_model_container: 7
2023-11-09 19:20:35,858:INFO:display_container: 2
2023-11-09 19:20:35,860:INFO:RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,
oob_score=False, random_state=123, verbose=0,
warm_start=False)
2023-11-09 19:20:35,860:INFO:create_model() succesfully completed......................................
2023-11-09 19:20:36,001:INFO:SubProcess create_model() end ==================================
2023-11-09 19:20:36,001:INFO:Creating metrics dataframe
2023-11-09 19:20:36,040:INFO:Initializing Quadratic Discriminant Analysis
2023-11-09 19:20:36,041:INFO:Total runtime is 0.6727781534194947 minutes
2023-11-09 19:20:36,092:INFO:SubProcess create_model() called ==================================
2023-11-09 19:20:36,096:INFO:Initializing create_model()
2023-11-09 19:20:36,102:INFO:create_model(estimator=qda, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:20:36,109:INFO:Checking exceptions
2023-11-09 19:20:36,110:INFO:Importing libraries
2023-11-09 19:20:36,111:INFO:Copying training dataset
2023-11-09 19:20:36,119:INFO:Defining folds
2023-11-09 19:20:36,119:INFO:Declaring metric variables
2023-11-09 19:20:36,158:INFO:Importing untrained model
2023-11-09 19:20:36,219:INFO:Quadratic Discriminant Analysis Imported succesfully
2023-11-09 19:20:36,290:INFO:Starting cross validation
2023-11-09 19:20:36,295:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:20:37,701:INFO:Calculating mean and std
2023-11-09 19:20:37,705:INFO:Creating metrics dataframe
2023-11-09 19:20:37,715:INFO:Uploading results into container
2023-11-09 19:20:37,715:INFO:Uploading model into container now
2023-11-09 19:20:37,716:INFO:create_model_container: 8
2023-11-09 19:20:37,716:INFO:master_model_container: 8
2023-11-09 19:20:37,717:INFO:display_container: 2
2023-11-09 19:20:37,718:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,
store_covariance=False, tol=0.0001)
2023-11-09 19:20:37,718:INFO:create_model() succesfully completed......................................
2023-11-09 19:20:37,855:INFO:SubProcess create_model() end ==================================
2023-11-09 19:20:37,855:INFO:Creating metrics dataframe
2023-11-09 19:20:37,894:INFO:Initializing Ada Boost Classifier
2023-11-09 19:20:37,895:INFO:Total runtime is 0.7036728342374166 minutes
2023-11-09 19:20:37,946:INFO:SubProcess create_model() called ==================================
2023-11-09 19:20:37,961:INFO:Initializing create_model()
2023-11-09 19:20:37,964:INFO:create_model(estimator=ada, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:20:37,974:INFO:Checking exceptions
2023-11-09 19:20:37,974:INFO:Importing libraries
2023-11-09 19:20:37,975:INFO:Copying training dataset
2023-11-09 19:20:37,980:INFO:Defining folds
2023-11-09 19:20:37,981:INFO:Declaring metric variables
2023-11-09 19:20:38,023:INFO:Importing untrained model
2023-11-09 19:20:38,055:INFO:Ada Boost Classifier Imported succesfully
2023-11-09 19:20:38,160:INFO:Starting cross validation
2023-11-09 19:20:38,173:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:20:44,961:INFO:Calculating mean and std
2023-11-09 19:20:44,962:INFO:Creating metrics dataframe
2023-11-09 19:20:44,975:INFO:Uploading results into container
2023-11-09 19:20:44,975:INFO:Uploading model into container now
2023-11-09 19:20:44,976:INFO:create_model_container: 9
2023-11-09 19:20:44,976:INFO:master_model_container: 9
2023-11-09 19:20:44,976:INFO:display_container: 2
2023-11-09 19:20:44,977:INFO:AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,
n_estimators=50, random_state=123)
2023-11-09 19:20:44,977:INFO:create_model() succesfully completed......................................
2023-11-09 19:20:45,121:INFO:SubProcess create_model() end ==================================
2023-11-09 19:20:45,121:INFO:Creating metrics dataframe
2023-11-09 19:20:45,160:INFO:Initializing Gradient Boosting Classifier
2023-11-09 19:20:45,161:INFO:Total runtime is 0.8247751355171203 minutes
2023-11-09 19:20:45,224:INFO:SubProcess create_model() called ==================================
2023-11-09 19:20:45,227:INFO:Initializing create_model()
2023-11-09 19:20:45,227:INFO:create_model(estimator=gbc, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:20:45,227:INFO:Checking exceptions
2023-11-09 19:20:45,227:INFO:Importing libraries
2023-11-09 19:20:45,227:INFO:Copying training dataset
2023-11-09 19:20:45,253:INFO:Defining folds
2023-11-09 19:20:45,255:INFO:Declaring metric variables
2023-11-09 19:20:45,294:INFO:Importing untrained model
2023-11-09 19:20:45,334:INFO:Gradient Boosting Classifier Imported succesfully
2023-11-09 19:20:45,421:INFO:Starting cross validation
2023-11-09 19:20:45,425:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:20:58,025:INFO:Calculating mean and std
2023-11-09 19:20:58,027:INFO:Creating metrics dataframe
2023-11-09 19:20:58,045:INFO:Uploading results into container
2023-11-09 19:20:58,045:INFO:Uploading model into container now
2023-11-09 19:20:58,045:INFO:create_model_container: 10
2023-11-09 19:20:58,046:INFO:master_model_container: 10
2023-11-09 19:20:58,046:INFO:display_container: 2
2023-11-09 19:20:58,046:INFO:GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=123, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False)
2023-11-09 19:20:58,047:INFO:create_model() succesfully completed......................................
2023-11-09 19:20:58,193:INFO:SubProcess create_model() end ==================================
2023-11-09 19:20:58,193:INFO:Creating metrics dataframe
2023-11-09 19:20:58,247:INFO:Initializing Linear Discriminant Analysis
2023-11-09 19:20:58,248:INFO:Total runtime is 1.0428899685541788 minutes
2023-11-09 19:20:58,290:INFO:SubProcess create_model() called ==================================
2023-11-09 19:20:58,291:INFO:Initializing create_model()
2023-11-09 19:20:58,292:INFO:create_model(estimator=lda, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:20:58,292:INFO:Checking exceptions
2023-11-09 19:20:58,292:INFO:Importing libraries
2023-11-09 19:20:58,292:INFO:Copying training dataset
2023-11-09 19:20:58,296:INFO:Defining folds
2023-11-09 19:20:58,296:INFO:Declaring metric variables
2023-11-09 19:20:58,385:INFO:Importing untrained model
2023-11-09 19:20:58,410:INFO:Linear Discriminant Analysis Imported succesfully
2023-11-09 19:20:58,506:INFO:Starting cross validation
2023-11-09 19:20:58,508:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:21:00,722:INFO:Calculating mean and std
2023-11-09 19:21:00,723:INFO:Creating metrics dataframe
2023-11-09 19:21:00,738:INFO:Uploading results into container
2023-11-09 19:21:00,743:INFO:Uploading model into container now
2023-11-09 19:21:00,745:INFO:create_model_container: 11
2023-11-09 19:21:00,746:INFO:master_model_container: 11
2023-11-09 19:21:00,746:INFO:display_container: 2
2023-11-09 19:21:00,747:INFO:LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,
solver='svd', store_covariance=False, tol=0.0001)
2023-11-09 19:21:00,747:INFO:create_model() succesfully completed......................................
2023-11-09 19:21:00,898:INFO:SubProcess create_model() end ==================================
2023-11-09 19:21:00,899:INFO:Creating metrics dataframe
2023-11-09 19:21:00,944:INFO:Initializing Extra Trees Classifier
2023-11-09 19:21:00,944:INFO:Total runtime is 1.08783860206604 minutes
2023-11-09 19:21:00,981:INFO:SubProcess create_model() called ==================================
2023-11-09 19:21:00,981:INFO:Initializing create_model()
2023-11-09 19:21:00,982:INFO:create_model(estimator=et, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:21:00,982:INFO:Checking exceptions
2023-11-09 19:21:00,982:INFO:Importing libraries
2023-11-09 19:21:00,983:INFO:Copying training dataset
2023-11-09 19:21:00,992:INFO:Defining folds
2023-11-09 19:21:00,992:INFO:Declaring metric variables
2023-11-09 19:21:01,039:INFO:Importing untrained model
2023-11-09 19:21:01,080:INFO:Extra Trees Classifier Imported succesfully
2023-11-09 19:21:01,161:INFO:Starting cross validation
2023-11-09 19:21:01,162:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:21:16,956:INFO:Calculating mean and std
2023-11-09 19:21:16,958:INFO:Creating metrics dataframe
2023-11-09 19:21:16,964:INFO:Uploading results into container
2023-11-09 19:21:16,965:INFO:Uploading model into container now
2023-11-09 19:21:16,965:INFO:create_model_container: 12
2023-11-09 19:21:16,965:INFO:master_model_container: 12
2023-11-09 19:21:16,965:INFO:display_container: 2
2023-11-09 19:21:16,966:INFO:ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,
oob_score=False, random_state=123, verbose=0,
warm_start=False)
2023-11-09 19:21:16,966:INFO:create_model() succesfully completed......................................
2023-11-09 19:21:17,118:INFO:SubProcess create_model() end ==================================
2023-11-09 19:21:17,118:INFO:Creating metrics dataframe
2023-11-09 19:21:17,177:INFO:Initializing Extreme Gradient Boosting
2023-11-09 19:21:17,177:INFO:Total runtime is 1.3583771030108134 minutes
2023-11-09 19:21:17,223:INFO:SubProcess create_model() called ==================================
2023-11-09 19:21:17,224:INFO:Initializing create_model()
2023-11-09 19:21:17,247:INFO:create_model(estimator=xgboost, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:21:17,257:INFO:Checking exceptions
2023-11-09 19:21:17,258:INFO:Importing libraries
2023-11-09 19:21:17,258:INFO:Copying training dataset
2023-11-09 19:21:17,260:INFO:Defining folds
2023-11-09 19:21:17,260:INFO:Declaring metric variables
2023-11-09 19:21:17,293:INFO:Importing untrained model
2023-11-09 19:21:17,333:INFO:Extreme Gradient Boosting Imported succesfully
2023-11-09 19:21:17,412:INFO:Starting cross validation
2023-11-09 19:21:17,424:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:21:53,802:INFO:Calculating mean and std
2023-11-09 19:21:53,804:INFO:Creating metrics dataframe
2023-11-09 19:21:53,814:INFO:Uploading results into container
2023-11-09 19:21:53,814:INFO:Uploading model into container now
2023-11-09 19:21:53,814:INFO:create_model_container: 13
2023-11-09 19:21:53,814:INFO:master_model_container: 13
2023-11-09 19:21:53,815:INFO:display_container: 2
2023-11-09 19:21:53,817:INFO:XGBClassifier(base_score=None, booster='gbtree', callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, gamma=None,
gpu_id=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=100, n_jobs=1,
num_parallel_tree=None, objective='binary:logistic',
predictor=None, random_state=123, reg_alpha=None, ...)
2023-11-09 19:21:53,817:INFO:create_model() succesfully completed......................................
2023-11-09 19:21:53,938:INFO:SubProcess create_model() end ==================================
2023-11-09 19:21:53,938:INFO:Creating metrics dataframe
2023-11-09 19:21:53,979:INFO:Initializing Light Gradient Boosting Machine
2023-11-09 19:21:53,979:INFO:Total runtime is 1.9717469851175944 minutes
2023-11-09 19:21:54,014:INFO:SubProcess create_model() called ==================================
2023-11-09 19:21:54,015:INFO:Initializing create_model()
2023-11-09 19:21:54,015:INFO:create_model(estimator=lightgbm, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:21:54,015:INFO:Checking exceptions
2023-11-09 19:21:54,015:INFO:Importing libraries
2023-11-09 19:21:54,016:INFO:Copying training dataset
2023-11-09 19:21:54,024:INFO:Defining folds
2023-11-09 19:21:54,024:INFO:Declaring metric variables
2023-11-09 19:21:54,073:INFO:Importing untrained model
2023-11-09 19:21:54,096:INFO:Light Gradient Boosting Machine Imported succesfully
2023-11-09 19:21:54,155:INFO:Starting cross validation
2023-11-09 19:21:54,156:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:21:55,867:INFO:Calculating mean and std
2023-11-09 19:21:55,869:INFO:Creating metrics dataframe
2023-11-09 19:21:55,876:INFO:Uploading results into container
2023-11-09 19:21:55,876:INFO:Uploading model into container now
2023-11-09 19:21:55,877:INFO:create_model_container: 14
2023-11-09 19:21:55,877:INFO:master_model_container: 14
2023-11-09 19:21:55,877:INFO:display_container: 2
2023-11-09 19:21:55,877:INFO:LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,
importance_type='split', learning_rate=0.1, max_depth=-1,
min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
n_estimators=100, n_jobs=1, num_leaves=31, objective=None,
random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent='warn',
subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2023-11-09 19:21:55,878:INFO:create_model() succesfully completed......................................
2023-11-09 19:21:55,997:INFO:SubProcess create_model() end ==================================
2023-11-09 19:21:55,997:INFO:Creating metrics dataframe
2023-11-09 19:21:56,037:INFO:Initializing CatBoost Classifier
2023-11-09 19:21:56,038:INFO:Total runtime is 2.0060600519180296 minutes
2023-11-09 19:21:56,281:INFO:SubProcess create_model() called ==================================
2023-11-09 19:21:56,283:INFO:Initializing create_model()
2023-11-09 19:21:56,284:INFO:create_model(estimator=catboost, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:21:56,284:INFO:Checking exceptions
2023-11-09 19:21:56,284:INFO:Importing libraries
2023-11-09 19:21:56,284:INFO:Copying training dataset
2023-11-09 19:21:56,287:INFO:Defining folds
2023-11-09 19:21:56,287:INFO:Declaring metric variables
2023-11-09 19:21:56,320:INFO:Importing untrained model
2023-11-09 19:21:56,348:INFO:CatBoost Classifier Imported succesfully
2023-11-09 19:21:56,437:INFO:Starting cross validation
2023-11-09 19:21:56,439:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:22:35,904:INFO:Calculating mean and std
2023-11-09 19:22:35,906:INFO:Creating metrics dataframe
2023-11-09 19:22:35,913:INFO:Uploading results into container
2023-11-09 19:22:35,913:INFO:Uploading model into container now
2023-11-09 19:22:35,913:INFO:create_model_container: 15
2023-11-09 19:22:35,913:INFO:master_model_container: 15
2023-11-09 19:22:35,913:INFO:display_container: 2
2023-11-09 19:22:35,913:INFO:<catboost.core.CatBoostClassifier object at 0x7f7b09d7d7b8>
2023-11-09 19:22:35,913:INFO:create_model() succesfully completed......................................
2023-11-09 19:22:36,025:INFO:SubProcess create_model() end ==================================
2023-11-09 19:22:36,026:INFO:Creating metrics dataframe
2023-11-09 19:22:36,071:INFO:Initializing Dummy Classifier
2023-11-09 19:22:36,071:INFO:Total runtime is 2.6732886195182797 minutes
2023-11-09 19:22:36,096:INFO:SubProcess create_model() called ==================================
2023-11-09 19:22:36,096:INFO:Initializing create_model()
2023-11-09 19:22:36,096:INFO:create_model(estimator=dummy, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x7f7b27122940>, return_train_score=False, kwargs={})
2023-11-09 19:22:36,097:INFO:Checking exceptions
2023-11-09 19:22:36,097:INFO:Importing libraries
2023-11-09 19:22:36,097:INFO:Copying training dataset
2023-11-09 19:22:36,105:INFO:Defining folds
2023-11-09 19:22:36,106:INFO:Declaring metric variables
2023-11-09 19:22:36,162:INFO:Importing untrained model
2023-11-09 19:22:36,197:INFO:Dummy Classifier Imported succesfully
2023-11-09 19:22:36,246:INFO:Starting cross validation
2023-11-09 19:22:36,246:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:22:36,366:INFO:Calculating mean and std
2023-11-09 19:22:36,368:INFO:Creating metrics dataframe
2023-11-09 19:22:36,381:INFO:Uploading results into container
2023-11-09 19:22:36,381:INFO:Uploading model into container now
2023-11-09 19:22:36,382:INFO:create_model_container: 16
2023-11-09 19:22:36,382:INFO:master_model_container: 16
2023-11-09 19:22:36,382:INFO:display_container: 2
2023-11-09 19:22:36,383:INFO:DummyClassifier(constant=None, random_state=123, strategy='prior')
2023-11-09 19:22:36,383:INFO:create_model() succesfully completed......................................
2023-11-09 19:22:36,501:INFO:SubProcess create_model() end ==================================
2023-11-09 19:22:36,501:INFO:Creating metrics dataframe
2023-11-09 19:22:36,615:INFO:Initializing create_model()
2023-11-09 19:22:36,616:INFO:create_model(estimator=GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=123, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False), fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=None, return_train_score=False, kwargs={})
2023-11-09 19:22:36,616:INFO:Checking exceptions
2023-11-09 19:22:36,616:INFO:Importing libraries
2023-11-09 19:22:36,616:INFO:Copying training dataset
2023-11-09 19:22:36,628:INFO:Defining folds
2023-11-09 19:22:36,629:INFO:Declaring metric variables
2023-11-09 19:22:36,629:INFO:Importing untrained model
2023-11-09 19:22:36,629:INFO:Declaring custom model
2023-11-09 19:22:36,631:INFO:Gradient Boosting Classifier Imported succesfully
2023-11-09 19:22:36,631:INFO:Cross validation set to False
2023-11-09 19:22:36,631:INFO:Fitting Model
2023-11-09 19:22:38,882:INFO:GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=123, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False)
2023-11-09 19:22:38,882:INFO:create_models() succesfully completed......................................
2023-11-09 19:22:38,988:INFO:Creating Dashboard logs
2023-11-09 19:22:39,001:INFO:Model: Gradient Boosting Classifier
2023-11-09 19:22:39,030:INFO:logged params: {'ccp_alpha': 0.0, 'criterion': 'friedman_mse', 'init': None, 'learning_rate': 0.1, 'loss': 'deviance', 'max_depth': 3, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_iter_no_change': None, 'presort': 'deprecated', 'random_state': 123, 'subsample': 1.0, 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': 0, 'warm_start': False}
2023-11-09 19:22:39,067:INFO:Initializing predict_model()
2023-11-09 19:22:39,068:INFO:predict_model(estimator=GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=123, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False), probability_threshold=None, encoded_labels=False, drift_report=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None, drift_kwargs=None)
2023-11-09 19:22:39,068:INFO:Checking exceptions
2023-11-09 19:22:39,068:INFO:Preloading libraries
2023-11-09 19:22:39,816:INFO:Initializing create_model()
2023-11-09 19:22:39,816:INFO:create_model(estimator=<catboost.core.CatBoostClassifier object at 0x7f7b09d7d7b8>, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=None, return_train_score=False, kwargs={})
2023-11-09 19:22:39,816:INFO:Checking exceptions
2023-11-09 19:22:39,816:INFO:Importing libraries
2023-11-09 19:22:39,816:INFO:Copying training dataset
2023-11-09 19:22:39,819:INFO:Defining folds
2023-11-09 19:22:39,819:INFO:Declaring metric variables
2023-11-09 19:22:39,819:INFO:Importing untrained model
2023-11-09 19:22:39,819:INFO:Declaring custom model
2023-11-09 19:22:39,820:INFO:CatBoost Classifier Imported succesfully
2023-11-09 19:22:39,821:INFO:Cross validation set to False
2023-11-09 19:22:39,822:INFO:Fitting Model
2023-11-09 19:22:48,376:INFO:<catboost.core.CatBoostClassifier object at 0x7f7b099ab6a0>
2023-11-09 19:22:48,376:INFO:create_models() succesfully completed......................................
2023-11-09 19:22:48,484:INFO:Creating Dashboard logs
2023-11-09 19:22:48,498:INFO:Model: CatBoost Classifier
2023-11-09 19:22:48,522:INFO:logged params: {'nan_mode': 'Min', 'eval_metric': 'Logloss', 'iterations': 1000, 'sampling_frequency': 'PerTree', 'leaf_estimation_method': 'Newton', 'grow_policy': 'SymmetricTree', 'penalties_coefficient': 1, 'boosting_type': 'Plain', 'model_shrink_mode': 'Constant', 'feature_border_type': 'GreedyLogSum', 'bayesian_matrix_reg': 0.10000000149011612, 'eval_fraction': 0, 'force_unit_auto_pair_weights': False, 'l2_leaf_reg': 3, 'random_strength': 1, 'rsm': 1, 'boost_from_average': False, 'model_size_reg': 0.5, 'pool_metainfo_options': {'tags': {}}, 'subsample': 0.800000011920929, 'use_best_model': False, 'class_names': [0, 1], 'random_seed': 123, 'depth': 6, 'posterior_sampling': False, 'border_count': 254, 'classes_count': 0, 'auto_class_weights': 'None', 'sparse_features_conflict_fraction': 0, 'leaf_estimation_backtracking': 'AnyImprovement', 'best_model_min_trees': 1, 'model_shrink_rate': 0, 'min_data_in_leaf': 1, 'loss_function': 'Logloss', 'learning_rate': 0.03359999880194664, 'score_function': 'Cosine', 'task_type': 'CPU', 'leaf_estimation_iterations': 10, 'bootstrap_type': 'MVS', 'max_leaves': 64}
2023-11-09 19:22:48,574:INFO:Initializing predict_model()
2023-11-09 19:22:48,574:INFO:predict_model(estimator=<catboost.core.CatBoostClassifier object at 0x7f7b099ab6a0>, probability_threshold=None, encoded_labels=False, drift_report=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None, drift_kwargs=None)
2023-11-09 19:22:48,575:INFO:Checking exceptions
2023-11-09 19:22:48,575:INFO:Preloading libraries
2023-11-09 19:22:49,047:INFO:Initializing create_model()
2023-11-09 19:22:49,047:INFO:create_model(estimator=AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,
n_estimators=50, random_state=123), fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=None, return_train_score=False, kwargs={})
2023-11-09 19:22:49,047:INFO:Checking exceptions
2023-11-09 19:22:49,049:INFO:Importing libraries
2023-11-09 19:22:49,049:INFO:Copying training dataset
2023-11-09 19:22:49,051:INFO:Defining folds
2023-11-09 19:22:49,052:INFO:Declaring metric variables
2023-11-09 19:22:49,052:INFO:Importing untrained model
2023-11-09 19:22:49,052:INFO:Declaring custom model
2023-11-09 19:22:49,053:INFO:Ada Boost Classifier Imported succesfully
2023-11-09 19:22:49,053:INFO:Cross validation set to False
2023-11-09 19:22:49,054:INFO:Fitting Model
2023-11-09 19:22:50,088:INFO:AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,
n_estimators=50, random_state=123)
2023-11-09 19:22:50,089:INFO:create_models() succesfully completed......................................
2023-11-09 19:22:50,209:INFO:Creating Dashboard logs
2023-11-09 19:22:50,223:INFO:Model: Ada Boost Classifier
2023-11-09 19:22:50,249:INFO:logged params: {'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1.0, 'n_estimators': 50, 'random_state': 123}
2023-11-09 19:22:50,274:INFO:Initializing predict_model()
2023-11-09 19:22:50,274:INFO:predict_model(estimator=AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,
n_estimators=50, random_state=123), probability_threshold=None, encoded_labels=False, drift_report=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None, drift_kwargs=None)
2023-11-09 19:22:50,274:INFO:Checking exceptions
2023-11-09 19:22:50,274:INFO:Preloading libraries
2023-11-09 19:22:50,910:INFO:Creating Dashboard logs
2023-11-09 19:22:50,929:INFO:Model: Light Gradient Boosting Machine
2023-11-09 19:22:50,964:INFO:logged params: {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': 1, 'num_leaves': 31, 'objective': None, 'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': 'warn', 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0}
2023-11-09 19:22:51,181:INFO:Creating Dashboard logs
2023-11-09 19:22:51,213:INFO:Model: Linear Discriminant Analysis
2023-11-09 19:22:51,233:INFO:logged params: {'n_components': None, 'priors': None, 'shrinkage': None, 'solver': 'svd', 'store_covariance': False, 'tol': 0.0001}
2023-11-09 19:22:51,447:INFO:Creating Dashboard logs
2023-11-09 19:22:51,476:INFO:Model: Logistic Regression
2023-11-09 19:22:51,509:INFO:logged params: {'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 1000, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': 123, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}
2023-11-09 19:22:51,819:INFO:Creating Dashboard logs
2023-11-09 19:22:51,847:INFO:Model: Extreme Gradient Boosting
2023-11-09 19:22:51,883:INFO:logged params: {'objective': 'binary:logistic', 'use_label_encoder': False, 'base_score': None, 'booster': 'gbtree', 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 100, 'n_jobs': 1, 'num_parallel_tree': None, 'predictor': None, 'random_state': 123, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': 'auto', 'validate_parameters': None, 'verbosity': 0}
2023-11-09 19:22:52,146:INFO:Creating Dashboard logs
2023-11-09 19:22:52,172:INFO:Model: Random Forest Classifier
2023-11-09 19:22:52,202:INFO:logged params: {'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': 1, 'oob_score': False, 'random_state': 123, 'verbose': 0, 'warm_start': False}
2023-11-09 19:22:52,446:INFO:Creating Dashboard logs
2023-11-09 19:22:52,469:INFO:Model: Naive Bayes
2023-11-09 19:22:52,492:INFO:logged params: {'priors': None, 'var_smoothing': 1e-09}
2023-11-09 19:22:52,698:INFO:Creating Dashboard logs
2023-11-09 19:22:52,719:INFO:Model: Extra Trees Classifier
2023-11-09 19:22:52,750:INFO:logged params: {'bootstrap': False, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': 1, 'oob_score': False, 'random_state': 123, 'verbose': 0, 'warm_start': False}
2023-11-09 19:22:53,312:INFO:Creating Dashboard logs
2023-11-09 19:22:53,341:INFO:Model: Quadratic Discriminant Analysis
2023-11-09 19:22:53,395:INFO:logged params: {'priors': None, 'reg_param': 0.0, 'store_covariance': False, 'tol': 0.0001}
2023-11-09 19:22:53,695:INFO:Creating Dashboard logs
2023-11-09 19:22:53,717:INFO:Model: K Neighbors Classifier
2023-11-09 19:22:53,756:INFO:logged params: {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': 1, 'n_neighbors': 5, 'p': 2, 'weights': 'uniform'}
2023-11-09 19:22:53,982:INFO:Creating Dashboard logs
2023-11-09 19:22:54,005:INFO:Model: Decision Tree Classifier
2023-11-09 19:22:54,028:INFO:logged params: {'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'presort': 'deprecated', 'random_state': 123, 'splitter': 'best'}
2023-11-09 19:22:54,278:INFO:Creating Dashboard logs
2023-11-09 19:22:54,294:INFO:Model: Dummy Classifier
2023-11-09 19:22:54,322:INFO:logged params: {'constant': None, 'random_state': 123, 'strategy': 'prior'}
2023-11-09 19:22:54,551:INFO:Creating Dashboard logs
2023-11-09 19:22:54,569:INFO:Model: SVM - Linear Kernel
2023-11-09 19:22:54,596:INFO:logged params: {'alpha': 0.0001, 'average': False, 'class_weight': None, 'early_stopping': False, 'epsilon': 0.1, 'eta0': 0.001, 'fit_intercept': True, 'l1_ratio': 0.15, 'learning_rate': 'optimal', 'loss': 'hinge', 'max_iter': 1000, 'n_iter_no_change': 5, 'n_jobs': 1, 'penalty': 'l2', 'power_t': 0.5, 'random_state': 123, 'shuffle': True, 'tol': 0.001, 'validation_fraction': 0.1, 'verbose': 0, 'warm_start': False}
2023-11-09 19:22:55,041:INFO:Creating Dashboard logs
2023-11-09 19:22:55,066:INFO:Model: Ridge Classifier
2023-11-09 19:22:55,091:INFO:logged params: {'alpha': 1.0, 'class_weight': None, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': False, 'random_state': 123, 'solver': 'auto', 'tol': 0.001}
2023-11-09 19:22:55,403:INFO:create_model_container: 16
2023-11-09 19:22:55,404:INFO:master_model_container: 16
2023-11-09 19:22:55,404:INFO:display_container: 2
2023-11-09 19:22:55,407:INFO:[GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=123, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False), <catboost.core.CatBoostClassifier object at 0x7f7b099ab6a0>, AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,
n_estimators=50, random_state=123)]
2023-11-09 19:22:55,407:INFO:compare_models() succesfully completed......................................
2023-11-09 19:26:17,176:INFO:Initializing predict_model()
2023-11-09 19:26:17,178:INFO:predict_model(estimator=GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=123, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False), probability_threshold=None, encoded_labels=False, drift_report=False, raw_score=False, round=4, verbose=True, ml_usecase=MLUsecase.CLASSIFICATION, display=None, drift_kwargs=None)
2023-11-09 19:26:17,178:INFO:Checking exceptions
2023-11-09 19:26:17,178:INFO:Preloading libraries
2023-11-09 19:26:17,179:INFO:Preparing display monitor
2023-11-09 19:26:37,064:INFO:Initializing predict_model()
2023-11-09 19:26:37,064:INFO:predict_model(estimator=<catboost.core.CatBoostClassifier object at 0x7f7b099ab6a0>, probability_threshold=None, encoded_labels=False, drift_report=False, raw_score=False, round=4, verbose=True, ml_usecase=MLUsecase.CLASSIFICATION, display=None, drift_kwargs=None)
2023-11-09 19:26:37,065:INFO:Checking exceptions
2023-11-09 19:26:37,066:INFO:Preloading libraries
2023-11-09 19:26:37,066:INFO:Preparing display monitor
2023-11-09 19:28:43,069:INFO:Initializing finalize_model()
2023-11-09 19:28:43,070:INFO:finalize_model(estimator=GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=123, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False), fit_kwargs=None, groups=None, model_only=True, display=None, experiment_custom_tags=None, return_train_score=False)
2023-11-09 19:28:43,072:INFO:Finalizing GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=123, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False)
2023-11-09 19:28:43,074:INFO:Initializing create_model()
2023-11-09 19:28:43,074:INFO:create_model(estimator=GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=123, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=False, probability_threshold=None, display=None, return_train_score=False, kwargs={})
2023-11-09 19:28:43,075:INFO:Checking exceptions
2023-11-09 19:28:43,075:INFO:Importing libraries
2023-11-09 19:28:43,076:INFO:Copying training dataset
2023-11-09 19:28:43,103:INFO:Defining folds
2023-11-09 19:28:43,103:INFO:Declaring metric variables
2023-11-09 19:28:43,104:INFO:Importing untrained model
2023-11-09 19:28:43,104:INFO:Declaring custom model
2023-11-09 19:28:43,106:INFO:Gradient Boosting Classifier Imported succesfully
2023-11-09 19:28:43,107:INFO:Starting cross validation
2023-11-09 19:28:43,110:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:29:02,205:INFO:Calculating mean and std
2023-11-09 19:29:02,207:INFO:Creating metrics dataframe
2023-11-09 19:29:02,223:INFO:Finalizing model
2023-11-09 19:29:05,911:INFO:create_model_container: 16
2023-11-09 19:29:05,911:INFO:master_model_container: 16
2023-11-09 19:29:05,911:INFO:display_container: 5
2023-11-09 19:29:05,911:INFO:GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=123, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False)
2023-11-09 19:29:05,911:INFO:create_model() succesfully completed......................................
2023-11-09 19:29:06,201:INFO:Creating Dashboard logs
2023-11-09 19:29:06,202:INFO:Model: Gradient Boosting Classifier
2023-11-09 19:29:06,230:INFO:logged params: {'ccp_alpha': 0.0, 'criterion': 'friedman_mse', 'init': None, 'learning_rate': 0.1, 'loss': 'deviance', 'max_depth': 3, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_iter_no_change': None, 'presort': 'deprecated', 'random_state': 123, 'subsample': 1.0, 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': 0, 'warm_start': False}
2023-11-09 19:29:06,287:INFO:Initializing predict_model()
2023-11-09 19:29:06,288:INFO:predict_model(estimator=GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=123, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False), probability_threshold=None, encoded_labels=False, drift_report=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None, drift_kwargs=None)
2023-11-09 19:29:06,288:INFO:Checking exceptions
2023-11-09 19:29:06,288:INFO:Preloading libraries
2023-11-09 19:29:06,970:INFO:create_model_container: 16
2023-11-09 19:29:06,970:INFO:master_model_container: 16
2023-11-09 19:29:06,971:INFO:display_container: 4
2023-11-09 19:29:06,971:INFO:GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=123, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False)
2023-11-09 19:29:06,971:INFO:finalize_model() succesfully completed......................................
2023-11-09 19:29:16,820:INFO:Initializing finalize_model()
2023-11-09 19:29:16,826:INFO:finalize_model(estimator=<catboost.core.CatBoostClassifier object at 0x7f7b099ab6a0>, fit_kwargs=None, groups=None, model_only=True, display=None, experiment_custom_tags=None, return_train_score=False)
2023-11-09 19:29:16,827:INFO:Finalizing <catboost.core.CatBoostClassifier object at 0x7f7b099ab6a0>
2023-11-09 19:29:16,827:INFO:Initializing create_model()
2023-11-09 19:29:16,828:INFO:create_model(estimator=<catboost.core.CatBoostClassifier object at 0x7f7b099ab6a0>, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=False, probability_threshold=None, display=None, return_train_score=False, kwargs={})
2023-11-09 19:29:16,828:INFO:Checking exceptions
2023-11-09 19:29:16,828:INFO:Importing libraries
2023-11-09 19:29:16,829:INFO:Copying training dataset
2023-11-09 19:29:16,831:INFO:Defining folds
2023-11-09 19:29:16,832:INFO:Declaring metric variables
2023-11-09 19:29:16,832:INFO:Importing untrained model
2023-11-09 19:29:16,833:INFO:Declaring custom model
2023-11-09 19:29:16,836:INFO:CatBoost Classifier Imported succesfully
2023-11-09 19:29:16,836:INFO:Starting cross validation
2023-11-09 19:29:16,838:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:30:24,212:INFO:Calculating mean and std
2023-11-09 19:30:24,214:INFO:Creating metrics dataframe
2023-11-09 19:30:24,224:INFO:Finalizing model
2023-11-09 19:30:35,638:INFO:create_model_container: 16
2023-11-09 19:30:35,639:INFO:master_model_container: 16
2023-11-09 19:30:35,639:INFO:display_container: 5
2023-11-09 19:30:35,639:INFO:<catboost.core.CatBoostClassifier object at 0x7f7b09f8e5c0>
2023-11-09 19:30:35,639:INFO:create_model() succesfully completed......................................
2023-11-09 19:30:35,812:INFO:Creating Dashboard logs
2023-11-09 19:30:35,812:INFO:Model: CatBoost Classifier
2023-11-09 19:30:35,838:INFO:logged params: {'nan_mode': 'Min', 'eval_metric': 'Logloss', 'iterations': 1000, 'sampling_frequency': 'PerTree', 'leaf_estimation_method': 'Newton', 'grow_policy': 'SymmetricTree', 'penalties_coefficient': 1, 'boosting_type': 'Plain', 'model_shrink_mode': 'Constant', 'feature_border_type': 'GreedyLogSum', 'bayesian_matrix_reg': 0.10000000149011612, 'eval_fraction': 0, 'force_unit_auto_pair_weights': False, 'l2_leaf_reg': 3, 'random_strength': 1, 'rsm': 1, 'boost_from_average': False, 'model_size_reg': 0.5, 'pool_metainfo_options': {'tags': {}}, 'subsample': 0.800000011920929, 'use_best_model': False, 'class_names': [0, 1], 'random_seed': 123, 'depth': 6, 'posterior_sampling': False, 'border_count': 254, 'classes_count': 0, 'auto_class_weights': 'None', 'sparse_features_conflict_fraction': 0, 'leaf_estimation_backtracking': 'AnyImprovement', 'best_model_min_trees': 1, 'model_shrink_rate': 0, 'min_data_in_leaf': 1, 'loss_function': 'Logloss', 'learning_rate': 0.036958999931812286, 'score_function': 'Cosine', 'task_type': 'CPU', 'leaf_estimation_iterations': 10, 'bootstrap_type': 'MVS', 'max_leaves': 64}
2023-11-09 19:30:35,905:INFO:Initializing predict_model()
2023-11-09 19:30:35,905:INFO:predict_model(estimator=<catboost.core.CatBoostClassifier object at 0x7f7b09f8e5c0>, probability_threshold=None, encoded_labels=False, drift_report=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None, drift_kwargs=None)
2023-11-09 19:30:35,905:INFO:Checking exceptions
2023-11-09 19:30:35,906:INFO:Preloading libraries
2023-11-09 19:30:36,751:INFO:create_model_container: 16
2023-11-09 19:30:36,751:INFO:master_model_container: 16
2023-11-09 19:30:36,751:INFO:display_container: 4
2023-11-09 19:30:36,751:INFO:<catboost.core.CatBoostClassifier object at 0x7f7b09f8e5c0>
2023-11-09 19:30:36,751:INFO:finalize_model() succesfully completed......................................
2023-11-09 19:30:45,280:INFO:Initializing finalize_model()
2023-11-09 19:30:45,281:INFO:finalize_model(estimator=AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,
n_estimators=50, random_state=123), fit_kwargs=None, groups=None, model_only=True, display=None, experiment_custom_tags=None, return_train_score=False)
2023-11-09 19:30:45,283:INFO:Finalizing AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,
n_estimators=50, random_state=123)
2023-11-09 19:30:45,284:INFO:Initializing create_model()
2023-11-09 19:30:45,284:INFO:create_model(estimator=AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,
n_estimators=50, random_state=123), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=False, probability_threshold=None, display=None, return_train_score=False, kwargs={})
2023-11-09 19:30:45,284:INFO:Checking exceptions
2023-11-09 19:30:45,285:INFO:Importing libraries
2023-11-09 19:30:45,285:INFO:Copying training dataset
2023-11-09 19:30:45,290:INFO:Defining folds
2023-11-09 19:30:45,291:INFO:Declaring metric variables
2023-11-09 19:30:45,291:INFO:Importing untrained model
2023-11-09 19:30:45,292:INFO:Declaring custom model
2023-11-09 19:30:45,293:INFO:Ada Boost Classifier Imported succesfully
2023-11-09 19:30:45,293:INFO:Starting cross validation
2023-11-09 19:30:45,296:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=1
2023-11-09 19:30:52,880:INFO:Calculating mean and std
2023-11-09 19:30:52,882:INFO:Creating metrics dataframe
2023-11-09 19:30:52,888:INFO:Finalizing model
2023-11-09 19:30:54,061:INFO:create_model_container: 16
2023-11-09 19:30:54,061:INFO:master_model_container: 16