logs.log

2021-07-16 05:30:03,444:INFO:PyCaret Supervised Module
2021-07-16 05:30:03,445:INFO:ML Usecase: classification
2021-07-16 05:30:03,445:INFO:version 2.3.2
2021-07-16 05:30:03,445:INFO:Initializing setup()
2021-07-16 05:30:03,446:INFO:setup(target=stabf, 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=0.7, test_data=None, preprocess=True, imputation_type=simple, iterative_imputation_iters=5, categorical_features=None, categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=10, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=False, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-07-16 05:30:03,446:INFO:Checking environment
2021-07-16 05:30:03,447:INFO:python_version: 3.7.6
2021-07-16 05:30:03,447:INFO:python_build: ('default', 'Jan  8 2020 19:59:22')
2021-07-16 05:30:03,447:INFO:machine: x86_64
2021-07-16 05:30:03,449:INFO:platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-debian-bullseye-sid
2021-07-16 05:30:03,456:INFO:Memory: svmem(total=13208035328, available=9544912896, percent=27.7, used=3335426048, free=1768898560, active=5182873600, inactive=5284470784, buffers=620634112, cached=7483076608, shared=86016, slab=845668352)
2021-07-16 05:30:03,459:INFO:Physical Core: 6
2021-07-16 05:30:03,459:INFO:Logical Core: 12
2021-07-16 05:30:03,460:INFO:Checking libraries
2021-07-16 05:30:03,460:INFO:pd==1.2.4
2021-07-16 05:30:03,460:INFO:numpy==1.19.5
2021-07-16 05:30:03,460:INFO:sklearn==0.23.2
2021-07-16 05:30:03,460:INFO:lightgbm==3.2.1
2021-07-16 05:30:03,461:WARNING:catboost not found
2021-07-16 05:30:03,462:WARNING:xgboost not found
2021-07-16 05:30:03,756:INFO:mlflow==1.19.0
2021-07-16 05:30:03,757:INFO:Checking Exceptions
2021-07-16 05:30:03,758:INFO:Declaring global variables
2021-07-16 05:30:03,758:INFO:USI: 7c93
2021-07-16 05:30:03,759:INFO:pycaret_globals: {'prep_pipe', '_internal_pipeline', 'y_test', 'experiment__', '_all_models_internal', 'USI', 'logging_param', 'gpu_param', '_all_metrics', 'display_container', 'imputation_classifier', 'fold_groups_param_full', '_all_models', 'imputation_regressor', 'fold_groups_param', 'stratify_param', 'transform_target_method_param', 'master_model_container', 'pycaret_globals', 'X', '_ml_usecase', 'fold_param', 'exp_name_log', 'X_test', 'iterative_imputation_iters_param', 'y', 'y_train', 'data_before_preprocess', 'log_plots_param', '_gpu_n_jobs_param', 'fix_imbalance_param', 'html_param', 'create_model_container', 'fold_generator', 'fold_shuffle_param', 'seed', 'n_jobs_param', 'transform_target_param', 'target_param', '_available_plots', 'fix_imbalance_method_param', 'X_train'}
2021-07-16 05:30:03,759:INFO:Preparing display monitor
2021-07-16 05:30:03,759:INFO:Preparing display monitor
2021-07-16 05:30:03,774:INFO:Importing libraries
2021-07-16 05:30:03,775:INFO:Copying data for preprocessing
2021-07-16 05:30:03,783:INFO:Declaring preprocessing parameters
2021-07-16 05:30:03,787:INFO:Creating preprocessing pipeline
2021-07-16 05:30:03,808:INFO:Preprocessing pipeline created successfully
2021-07-16 05:30:03,808:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-07-16 05:30:03,808:INFO:Creating global containers
2021-07-16 05:30:03,809:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-07-16 05:30:08,924:WARNING:Couldn't import xgboost.XGBClassifier
2021-07-16 05:30:08,925:WARNING:Couldn't import catboost.CatBoostClassifier
2021-07-16 05:30:08,983:WARNING:Couldn't import xgboost.XGBClassifier
2021-07-16 05:30:08,984:WARNING:Couldn't import catboost.CatBoostClassifier
2021-07-16 05:30:08,985:INFO:Creating grid variables
2021-07-16 05:30:09,001:INFO:Logging experiment in MLFlow
2021-07-16 05:30:10,237:INFO:SubProcess save_model() called ==================================
2021-07-16 05:30:10,245:INFO:Initializing save_model()
2021-07-16 05:30:10,246:INFO:save_model(model=Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=True, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strate...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('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=[],
                                      display_types=True, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strate...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False), verbose=False, kwargs={})
2021-07-16 05:30:10,246:INFO:Adding model into prep_pipe
2021-07-16 05:30:10,247:WARNING:Only Model saved as it was a pipeline.
2021-07-16 05:30:10,259:INFO:Transformation Pipeline.pkl saved in current working directory
2021-07-16 05:30:10,263:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=True, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strate...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-07-16 05:30:10,263:INFO:save_model() successfully completed......................................
2021-07-16 05:30:10,346:INFO:SubProcess save_model() end ==================================
2021-07-16 05:30:10,660:INFO:create_model_container: 0
2021-07-16 05:30:10,660:INFO:master_model_container: 0
2021-07-16 05:30:10,661:INFO:display_container: 1
2021-07-16 05:30:10,664:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=True, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strate...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-07-16 05:30:10,665:INFO:setup() succesfully completed......................................
2021-07-16 05:30:20,841:INFO:PyCaret Supervised Module
2021-07-16 05:30:20,842:INFO:ML Usecase: classification
2021-07-16 05:30:20,842:INFO:version 2.3.2
2021-07-16 05:30:20,842:INFO:Initializing setup()
2021-07-16 05:30:20,842:INFO:setup(target=stabf, 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=0.7, test_data=None, preprocess=True, imputation_type=simple, iterative_imputation_iters=5, categorical_features=None, categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=10, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-07-16 05:30:20,843:INFO:Checking environment
2021-07-16 05:30:20,843:INFO:python_version: 3.7.6
2021-07-16 05:30:20,843:INFO:python_build: ('default', 'Jan  8 2020 19:59:22')
2021-07-16 05:30:20,843:INFO:machine: x86_64
2021-07-16 05:30:20,844:INFO:platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-debian-bullseye-sid
2021-07-16 05:30:20,844:INFO:Memory: svmem(total=13208035328, available=9497968640, percent=28.1, used=3382362112, free=1718628352, active=5228965888, inactive=5286699008, buffers=620670976, cached=7486373888, shared=86016, slab=845701120)
2021-07-16 05:30:20,845:INFO:Physical Core: 6
2021-07-16 05:30:20,845:INFO:Logical Core: 12
2021-07-16 05:30:20,845:INFO:Checking libraries
2021-07-16 05:30:20,845:INFO:pd==1.2.4
2021-07-16 05:30:20,846:INFO:numpy==1.19.5
2021-07-16 05:30:20,846:INFO:sklearn==0.23.2
2021-07-16 05:30:20,846:INFO:lightgbm==3.2.1
2021-07-16 05:30:20,847:WARNING:catboost not found
2021-07-16 05:30:20,848:WARNING:xgboost not found
2021-07-16 05:30:20,848:INFO:mlflow==1.19.0
2021-07-16 05:30:20,848:INFO:Checking Exceptions
2021-07-16 05:30:20,849:INFO:Declaring global variables
2021-07-16 05:30:20,849:INFO:USI: f9d6
2021-07-16 05:30:20,849:INFO:pycaret_globals: {'prep_pipe', '_internal_pipeline', 'y_test', 'experiment__', '_all_models_internal', 'USI', 'logging_param', 'gpu_param', '_all_metrics', 'display_container', 'imputation_classifier', 'fold_groups_param_full', '_all_models', 'imputation_regressor', 'fold_groups_param', 'stratify_param', 'transform_target_method_param', 'master_model_container', 'pycaret_globals', 'X', '_ml_usecase', 'fold_param', 'exp_name_log', 'X_test', 'iterative_imputation_iters_param', 'y', 'y_train', 'data_before_preprocess', 'log_plots_param', '_gpu_n_jobs_param', 'fix_imbalance_param', 'html_param', 'create_model_container', 'fold_generator', 'fold_shuffle_param', 'seed', 'n_jobs_param', 'transform_target_param', 'target_param', '_available_plots', 'fix_imbalance_method_param', 'X_train'}
2021-07-16 05:30:20,850:INFO:Preparing display monitor
2021-07-16 05:30:20,850:INFO:Preparing display monitor
2021-07-16 05:30:20,865:INFO:Importing libraries
2021-07-16 05:30:20,865:INFO:Copying data for preprocessing
2021-07-16 05:30:20,876:INFO:Declaring preprocessing parameters
2021-07-16 05:30:20,879:INFO:Creating preprocessing pipeline
2021-07-16 05:30:20,911:INFO:Preprocessing pipeline created successfully
2021-07-16 05:30:20,912:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-07-16 05:30:20,912:INFO:Creating global containers
2021-07-16 05:30:20,912:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-07-16 05:30:21,140:WARNING:Couldn't import xgboost.XGBClassifier
2021-07-16 05:30:21,141:WARNING:Couldn't import catboost.CatBoostClassifier
2021-07-16 05:30:21,198:WARNING:Couldn't import xgboost.XGBClassifier
2021-07-16 05:30:21,200:WARNING:Couldn't import catboost.CatBoostClassifier
2021-07-16 05:30:21,200:INFO:Creating grid variables
2021-07-16 05:30:21,215:INFO:Logging experiment in MLFlow
2021-07-16 05:30:21,254:WARNING:Couldn't create mlflow experiment. Exception:
2021-07-16 05:30:21,265:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 1702, in setup
    mlflow.create_experiment(exp_name_log)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/tracking/fluent.py", line 875, in create_experiment
    return MlflowClient().create_experiment(name, artifact_location)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/tracking/client.py", line 500, in create_experiment
    return self._tracking_client.create_experiment(name, artifact_location)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/tracking/_tracking_service/client.py", line 167, in create_experiment
    return self.store.create_experiment(name=name, artifact_location=artifact_location,)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/store/tracking/file_store.py", line 313, in create_experiment
    self._validate_experiment_name(name)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/store/tracking/file_store.py", line 308, in _validate_experiment_name
    databricks_pb2.RESOURCE_ALREADY_EXISTS,
mlflow.exceptions.MlflowException: Experiment 'electrical_grid_1' already exists.

2021-07-16 05:30:22,355:INFO:SubProcess save_model() called ==================================
2021-07-16 05:30:22,361:INFO:Initializing save_model()
2021-07-16 05:30:22,362:INFO:save_model(model=Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('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=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False), verbose=False, kwargs={})
2021-07-16 05:30:22,362:INFO:Adding model into prep_pipe
2021-07-16 05:30:22,363:WARNING:Only Model saved as it was a pipeline.
2021-07-16 05:30:22,375:INFO:Transformation Pipeline.pkl saved in current working directory
2021-07-16 05:30:22,378:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-07-16 05:30:22,379:INFO:save_model() successfully completed......................................
2021-07-16 05:30:22,461:INFO:SubProcess save_model() end ==================================
2021-07-16 05:30:22,786:INFO:create_model_container: 0
2021-07-16 05:30:22,786:INFO:master_model_container: 0
2021-07-16 05:30:22,786:INFO:display_container: 1
2021-07-16 05:30:22,790:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-07-16 05:30:22,790:INFO:setup() succesfully completed......................................
2021-07-16 05:30:31,639:INFO:Initializing compare_models()
2021-07-16 05:30:31,639:INFO:compare_models(include=None, fold=None, round=4, cross_validation=True, sort=Accuracy, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-07-16 05:30:31,640:INFO:Checking exceptions
2021-07-16 05:30:31,641:INFO:Preparing display monitor
2021-07-16 05:30:31,641:INFO:Preparing display monitor
2021-07-16 05:30:31,666:INFO:Initializing Logistic Regression
2021-07-16 05:30:31,666:INFO:Total runtime is 6.767114003499349e-06 minutes
2021-07-16 05:30:31,676:INFO:SubProcess create_model() called ==================================
2021-07-16 05:30:31,676:INFO:Initializing create_model()
2021-07-16 05:30:31,676:INFO:create_model(estimator=lr, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d6538c50>, kwargs={})
2021-07-16 05:30:31,677:INFO:Checking exceptions
2021-07-16 05:30:31,677:INFO:Importing libraries
2021-07-16 05:30:31,677:INFO:Copying training dataset
2021-07-16 05:30:31,678:INFO:Defining folds
2021-07-16 05:30:31,678:INFO:Declaring metric variables
2021-07-16 05:30:31,685:INFO:Importing untrained model
2021-07-16 05:30:31,695:INFO:Logistic Regression Imported succesfully
2021-07-16 05:30:31,712:INFO:Starting cross validation
2021-07-16 05:30:31,713:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:30:34,029:INFO:Calculating mean and std
2021-07-16 05:30:34,031:INFO:Creating metrics dataframe
2021-07-16 05:30:34,043:INFO:Uploading results into container
2021-07-16 05:30:34,046:INFO:Uploading model into container now
2021-07-16 05:30:34,047:INFO:create_model_container: 1
2021-07-16 05:30:34,048:INFO:master_model_container: 1
2021-07-16 05:30:34,048:INFO:display_container: 2
2021-07-16 05:30:34,050: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)
2021-07-16 05:30:34,051:INFO:create_model() succesfully completed......................................
2021-07-16 05:30:34,157:INFO:SubProcess create_model() end ==================================
2021-07-16 05:30:34,157:INFO:Creating metrics dataframe
2021-07-16 05:30:34,175:INFO:Initializing K Neighbors Classifier
2021-07-16 05:30:34,176:INFO:Total runtime is 0.041833579540252686 minutes
2021-07-16 05:30:34,185:INFO:SubProcess create_model() called ==================================
2021-07-16 05:30:34,186:INFO:Initializing create_model()
2021-07-16 05:30:34,186:INFO:create_model(estimator=knn, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d6538c50>, kwargs={})
2021-07-16 05:30:34,187:INFO:Checking exceptions
2021-07-16 05:30:34,187:INFO:Importing libraries
2021-07-16 05:30:34,187:INFO:Copying training dataset
2021-07-16 05:30:34,188:INFO:Defining folds
2021-07-16 05:30:34,188:INFO:Declaring metric variables
2021-07-16 05:30:34,196:INFO:Importing untrained model
2021-07-16 05:30:34,205:INFO:K Neighbors Classifier Imported succesfully
2021-07-16 05:30:34,222:INFO:Starting cross validation
2021-07-16 05:30:34,223:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:30:35,538:INFO:Calculating mean and std
2021-07-16 05:30:35,540:INFO:Creating metrics dataframe
2021-07-16 05:30:35,547:INFO:Uploading results into container
2021-07-16 05:30:35,547:INFO:Uploading model into container now
2021-07-16 05:30:35,547:INFO:create_model_container: 2
2021-07-16 05:30:35,548:INFO:master_model_container: 2
2021-07-16 05:30:35,548:INFO:display_container: 2
2021-07-16 05:30:35,549:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
                     metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
                     weights='uniform')
2021-07-16 05:30:35,549:INFO:create_model() succesfully completed......................................
2021-07-16 05:30:35,640:INFO:SubProcess create_model() end ==================================
2021-07-16 05:30:35,641:INFO:Creating metrics dataframe
2021-07-16 05:30:35,658:INFO:Initializing Naive Bayes
2021-07-16 05:30:35,658:INFO:Total runtime is 0.06654792626698812 minutes
2021-07-16 05:30:35,667:INFO:SubProcess create_model() called ==================================
2021-07-16 05:30:35,668:INFO:Initializing create_model()
2021-07-16 05:30:35,668:INFO:create_model(estimator=nb, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d6538c50>, kwargs={})
2021-07-16 05:30:35,668:INFO:Checking exceptions
2021-07-16 05:30:35,668:INFO:Importing libraries
2021-07-16 05:30:35,669:INFO:Copying training dataset
2021-07-16 05:30:35,670:INFO:Defining folds
2021-07-16 05:30:35,671:INFO:Declaring metric variables
2021-07-16 05:30:35,679:INFO:Importing untrained model
2021-07-16 05:30:35,690:INFO:Naive Bayes Imported succesfully
2021-07-16 05:30:35,710:INFO:Starting cross validation
2021-07-16 05:30:35,710:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:30:35,773:INFO:Calculating mean and std
2021-07-16 05:30:35,775:INFO:Creating metrics dataframe
2021-07-16 05:30:35,780:INFO:Uploading results into container
2021-07-16 05:30:35,780:INFO:Uploading model into container now
2021-07-16 05:30:35,780:INFO:create_model_container: 3
2021-07-16 05:30:35,781:INFO:master_model_container: 3
2021-07-16 05:30:35,781:INFO:display_container: 2
2021-07-16 05:30:35,781:INFO:GaussianNB(priors=None, var_smoothing=1e-09)
2021-07-16 05:30:35,781:INFO:create_model() succesfully completed......................................
2021-07-16 05:30:35,872:INFO:SubProcess create_model() end ==================================
2021-07-16 05:30:35,873:INFO:Creating metrics dataframe
2021-07-16 05:30:35,892:INFO:Initializing Decision Tree Classifier
2021-07-16 05:30:35,893:INFO:Total runtime is 0.07045319477717082 minutes
2021-07-16 05:30:35,903:INFO:SubProcess create_model() called ==================================
2021-07-16 05:30:35,904:INFO:Initializing create_model()
2021-07-16 05:30:35,904:INFO:create_model(estimator=dt, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d6538c50>, kwargs={})
2021-07-16 05:30:35,904:INFO:Checking exceptions
2021-07-16 05:30:35,905:INFO:Importing libraries
2021-07-16 05:30:35,905:INFO:Copying training dataset
2021-07-16 05:30:35,906:INFO:Defining folds
2021-07-16 05:30:35,906:INFO:Declaring metric variables
2021-07-16 05:30:35,916:INFO:Importing untrained model
2021-07-16 05:30:35,926:INFO:Decision Tree Classifier Imported succesfully
2021-07-16 05:30:35,944:INFO:Starting cross validation
2021-07-16 05:30:35,945:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:30:36,087:INFO:Calculating mean and std
2021-07-16 05:30:36,088:INFO:Creating metrics dataframe
2021-07-16 05:30:36,092:INFO:Uploading results into container
2021-07-16 05:30:36,092:INFO:Uploading model into container now
2021-07-16 05:30:36,093:INFO:create_model_container: 4
2021-07-16 05:30:36,093:INFO:master_model_container: 4
2021-07-16 05:30:36,093:INFO:display_container: 2
2021-07-16 05:30:36,093: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')
2021-07-16 05:30:36,094:INFO:create_model() succesfully completed......................................
2021-07-16 05:30:36,167:INFO:SubProcess create_model() end ==================================
2021-07-16 05:30:36,168:INFO:Creating metrics dataframe
2021-07-16 05:30:36,190:INFO:Initializing SVM - Linear Kernel
2021-07-16 05:30:36,190:INFO:Total runtime is 0.07541182041168214 minutes
2021-07-16 05:30:36,201:INFO:SubProcess create_model() called ==================================
2021-07-16 05:30:36,201:INFO:Initializing create_model()
2021-07-16 05:30:36,202:INFO:create_model(estimator=svm, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d6538c50>, kwargs={})
2021-07-16 05:30:36,202:INFO:Checking exceptions
2021-07-16 05:30:36,202:INFO:Importing libraries
2021-07-16 05:30:36,203:INFO:Copying training dataset
2021-07-16 05:30:36,204:INFO:Defining folds
2021-07-16 05:30:36,204:INFO:Declaring metric variables
2021-07-16 05:30:36,214:INFO:Importing untrained model
2021-07-16 05:30:36,225:INFO:SVM - Linear Kernel Imported succesfully
2021-07-16 05:30:36,245:INFO:Starting cross validation
2021-07-16 05:30:36,246:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:30:36,400:INFO:Calculating mean and std
2021-07-16 05:30:36,402:INFO:Creating metrics dataframe
2021-07-16 05:30:36,408:INFO:Uploading results into container
2021-07-16 05:30:36,409:INFO:Uploading model into container now
2021-07-16 05:30:36,409:INFO:create_model_container: 5
2021-07-16 05:30:36,409:INFO:master_model_container: 5
2021-07-16 05:30:36,409:INFO:display_container: 2
2021-07-16 05:30:36,410: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)
2021-07-16 05:30:36,411:INFO:create_model() succesfully completed......................................
2021-07-16 05:30:36,494:INFO:SubProcess create_model() end ==================================
2021-07-16 05:30:36,495:INFO:Creating metrics dataframe
2021-07-16 05:30:36,512:INFO:Initializing Ridge Classifier
2021-07-16 05:30:36,513:INFO:Total runtime is 0.08078355391820273 minutes
2021-07-16 05:30:36,522:INFO:SubProcess create_model() called ==================================
2021-07-16 05:30:36,523:INFO:Initializing create_model()
2021-07-16 05:30:36,523:INFO:create_model(estimator=ridge, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d6538c50>, kwargs={})
2021-07-16 05:30:36,523:INFO:Checking exceptions
2021-07-16 05:30:36,524:INFO:Importing libraries
2021-07-16 05:30:36,524:INFO:Copying training dataset
2021-07-16 05:30:36,525:INFO:Defining folds
2021-07-16 05:30:36,525:INFO:Declaring metric variables
2021-07-16 05:30:36,532:INFO:Importing untrained model
2021-07-16 05:30:36,541:INFO:Ridge Classifier Imported succesfully
2021-07-16 05:30:36,556:INFO:Starting cross validation
2021-07-16 05:30:36,557:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:30:36,620:INFO:Calculating mean and std
2021-07-16 05:30:36,621:INFO:Creating metrics dataframe
2021-07-16 05:30:36,626:INFO:Uploading results into container
2021-07-16 05:30:36,627:INFO:Uploading model into container now
2021-07-16 05:30:36,627:INFO:create_model_container: 6
2021-07-16 05:30:36,627:INFO:master_model_container: 6
2021-07-16 05:30:36,628:INFO:display_container: 2
2021-07-16 05:30:36,628: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)
2021-07-16 05:30:36,629:INFO:create_model() succesfully completed......................................
2021-07-16 05:30:36,707:INFO:SubProcess create_model() end ==================================
2021-07-16 05:30:36,707:INFO:Creating metrics dataframe
2021-07-16 05:30:36,727:INFO:Initializing Random Forest Classifier
2021-07-16 05:30:36,728:INFO:Total runtime is 0.08436866998672486 minutes
2021-07-16 05:30:36,737:INFO:SubProcess create_model() called ==================================
2021-07-16 05:30:36,737:INFO:Initializing create_model()
2021-07-16 05:30:36,738:INFO:create_model(estimator=rf, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d6538c50>, kwargs={})
2021-07-16 05:30:36,738:INFO:Checking exceptions
2021-07-16 05:30:36,738:INFO:Importing libraries
2021-07-16 05:30:36,738:INFO:Copying training dataset
2021-07-16 05:30:36,739:INFO:Defining folds
2021-07-16 05:30:36,739:INFO:Declaring metric variables
2021-07-16 05:30:36,747:INFO:Importing untrained model
2021-07-16 05:30:36,756:INFO:Random Forest Classifier Imported succesfully
2021-07-16 05:30:36,773:INFO:Starting cross validation
2021-07-16 05:30:36,774:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:30:39,632:INFO:Calculating mean and std
2021-07-16 05:30:39,634:INFO:Creating metrics dataframe
2021-07-16 05:30:39,639:INFO:Uploading results into container
2021-07-16 05:30:39,639:INFO:Uploading model into container now
2021-07-16 05:30:39,639:INFO:create_model_container: 7
2021-07-16 05:30:39,639:INFO:master_model_container: 7
2021-07-16 05:30:39,640:INFO:display_container: 2
2021-07-16 05:30:39,640: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)
2021-07-16 05:30:39,641:INFO:create_model() succesfully completed......................................
2021-07-16 05:30:39,724:INFO:SubProcess create_model() end ==================================
2021-07-16 05:30:39,725:INFO:Creating metrics dataframe
2021-07-16 05:30:39,747:INFO:Initializing Quadratic Discriminant Analysis
2021-07-16 05:30:39,748:INFO:Total runtime is 0.13470431566238406 minutes
2021-07-16 05:30:39,758:INFO:SubProcess create_model() called ==================================
2021-07-16 05:30:39,758:INFO:Initializing create_model()
2021-07-16 05:30:39,758:INFO:create_model(estimator=qda, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d6538c50>, kwargs={})
2021-07-16 05:30:39,758:INFO:Checking exceptions
2021-07-16 05:30:39,759:INFO:Importing libraries
2021-07-16 05:30:39,759:INFO:Copying training dataset
2021-07-16 05:30:39,760:INFO:Defining folds
2021-07-16 05:30:39,760:INFO:Declaring metric variables
2021-07-16 05:30:39,769:INFO:Importing untrained model
2021-07-16 05:30:39,778:INFO:Quadratic Discriminant Analysis Imported succesfully
2021-07-16 05:30:39,796:INFO:Starting cross validation
2021-07-16 05:30:39,797:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:30:39,868:INFO:Calculating mean and std
2021-07-16 05:30:39,870:INFO:Creating metrics dataframe
2021-07-16 05:30:39,876:INFO:Uploading results into container
2021-07-16 05:30:39,876:INFO:Uploading model into container now
2021-07-16 05:30:39,877:INFO:create_model_container: 8
2021-07-16 05:30:39,877:INFO:master_model_container: 8
2021-07-16 05:30:39,877:INFO:display_container: 2
2021-07-16 05:30:39,878:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,
                              store_covariance=False, tol=0.0001)
2021-07-16 05:30:39,878:INFO:create_model() succesfully completed......................................
2021-07-16 05:30:39,966:INFO:SubProcess create_model() end ==================================
2021-07-16 05:30:39,966:INFO:Creating metrics dataframe
2021-07-16 05:30:39,987:INFO:Initializing Ada Boost Classifier
2021-07-16 05:30:39,988:INFO:Total runtime is 0.13870400587717693 minutes
2021-07-16 05:30:39,997:INFO:SubProcess create_model() called ==================================
2021-07-16 05:30:39,997:INFO:Initializing create_model()
2021-07-16 05:30:39,997:INFO:create_model(estimator=ada, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d6538c50>, kwargs={})
2021-07-16 05:30:39,998:INFO:Checking exceptions
2021-07-16 05:30:39,998:INFO:Importing libraries
2021-07-16 05:30:39,998:INFO:Copying training dataset
2021-07-16 05:30:39,999:INFO:Defining folds
2021-07-16 05:30:39,999:INFO:Declaring metric variables
2021-07-16 05:30:40,007:INFO:Importing untrained model
2021-07-16 05:30:40,016:INFO:Ada Boost Classifier Imported succesfully
2021-07-16 05:30:40,035:INFO:Starting cross validation
2021-07-16 05:30:40,036:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:30:40,720:INFO:Calculating mean and std
2021-07-16 05:30:40,721:INFO:Creating metrics dataframe
2021-07-16 05:30:40,726:INFO:Uploading results into container
2021-07-16 05:30:40,727:INFO:Uploading model into container now
2021-07-16 05:30:40,727:INFO:create_model_container: 9
2021-07-16 05:30:40,727:INFO:master_model_container: 9
2021-07-16 05:30:40,727:INFO:display_container: 2
2021-07-16 05:30:40,728:INFO:AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,
                   n_estimators=50, random_state=123)
2021-07-16 05:30:40,728:INFO:create_model() succesfully completed......................................
2021-07-16 05:30:40,813:INFO:SubProcess create_model() end ==================================
2021-07-16 05:30:40,814:INFO:Creating metrics dataframe
2021-07-16 05:30:40,837:INFO:Initializing Gradient Boosting Classifier
2021-07-16 05:30:40,837:INFO:Total runtime is 0.1528607726097107 minutes
2021-07-16 05:30:40,848:INFO:SubProcess create_model() called ==================================
2021-07-16 05:30:40,848:INFO:Initializing create_model()
2021-07-16 05:30:40,849:INFO:create_model(estimator=gbc, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d6538c50>, kwargs={})
2021-07-16 05:30:40,849:INFO:Checking exceptions
2021-07-16 05:30:40,849:INFO:Importing libraries
2021-07-16 05:30:40,849:INFO:Copying training dataset
2021-07-16 05:30:40,850:INFO:Defining folds
2021-07-16 05:30:40,850:INFO:Declaring metric variables
2021-07-16 05:30:40,859:INFO:Importing untrained model
2021-07-16 05:30:40,868:INFO:Gradient Boosting Classifier Imported succesfully
2021-07-16 05:30:40,887:INFO:Starting cross validation
2021-07-16 05:30:40,888:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:30:43,611:INFO:Calculating mean and std
2021-07-16 05:30:43,612:INFO:Creating metrics dataframe
2021-07-16 05:30:43,616:INFO:Uploading results into container
2021-07-16 05:30:43,617:INFO:Uploading model into container now
2021-07-16 05:30:43,617:INFO:create_model_container: 10
2021-07-16 05:30:43,617:INFO:master_model_container: 10
2021-07-16 05:30:43,617:INFO:display_container: 2
2021-07-16 05:30:43,618: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)
2021-07-16 05:30:43,618:INFO:create_model() succesfully completed......................................
2021-07-16 05:30:43,690:INFO:SubProcess create_model() end ==================================
2021-07-16 05:30:43,690:INFO:Creating metrics dataframe
2021-07-16 05:30:43,711:INFO:Initializing Linear Discriminant Analysis
2021-07-16 05:30:43,712:INFO:Total runtime is 0.20076541900634767 minutes
2021-07-16 05:30:43,720:INFO:SubProcess create_model() called ==================================
2021-07-16 05:30:43,721:INFO:Initializing create_model()
2021-07-16 05:30:43,721:INFO:create_model(estimator=lda, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d6538c50>, kwargs={})
2021-07-16 05:30:43,721:INFO:Checking exceptions
2021-07-16 05:30:43,721:INFO:Importing libraries
2021-07-16 05:30:43,722:INFO:Copying training dataset
2021-07-16 05:30:43,722:INFO:Defining folds
2021-07-16 05:30:43,723:INFO:Declaring metric variables
2021-07-16 05:30:43,730:INFO:Importing untrained model
2021-07-16 05:30:43,738:INFO:Linear Discriminant Analysis Imported succesfully
2021-07-16 05:30:43,756:INFO:Starting cross validation
2021-07-16 05:30:43,757:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:30:43,838:INFO:Calculating mean and std
2021-07-16 05:30:43,840:INFO:Creating metrics dataframe
2021-07-16 05:30:43,844:INFO:Uploading results into container
2021-07-16 05:30:43,845:INFO:Uploading model into container now
2021-07-16 05:30:43,845:INFO:create_model_container: 11
2021-07-16 05:30:43,845:INFO:master_model_container: 11
2021-07-16 05:30:43,845:INFO:display_container: 2
2021-07-16 05:30:43,846:INFO:LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,
                           solver='svd', store_covariance=False, tol=0.0001)
2021-07-16 05:30:43,846:INFO:create_model() succesfully completed......................................
2021-07-16 05:30:43,934:INFO:SubProcess create_model() end ==================================
2021-07-16 05:30:43,935:INFO:Creating metrics dataframe
2021-07-16 05:30:43,958:INFO:Initializing Extra Trees Classifier
2021-07-16 05:30:43,959:INFO:Total runtime is 0.20488572120666507 minutes
2021-07-16 05:30:43,968:INFO:SubProcess create_model() called ==================================
2021-07-16 05:30:43,968:INFO:Initializing create_model()
2021-07-16 05:30:43,969:INFO:create_model(estimator=et, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d6538c50>, kwargs={})
2021-07-16 05:30:43,969:INFO:Checking exceptions
2021-07-16 05:30:43,969:INFO:Importing libraries
2021-07-16 05:30:43,970:INFO:Copying training dataset
2021-07-16 05:30:43,971:INFO:Defining folds
2021-07-16 05:30:43,971:INFO:Declaring metric variables
2021-07-16 05:30:43,979:INFO:Importing untrained model
2021-07-16 05:30:43,987:INFO:Extra Trees Classifier Imported succesfully
2021-07-16 05:30:44,005:INFO:Starting cross validation
2021-07-16 05:30:44,006:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:30:46,167:INFO:Calculating mean and std
2021-07-16 05:30:46,168:INFO:Creating metrics dataframe
2021-07-16 05:30:46,174:INFO:Uploading results into container
2021-07-16 05:30:46,174:INFO:Uploading model into container now
2021-07-16 05:30:46,174:INFO:create_model_container: 12
2021-07-16 05:30:46,174:INFO:master_model_container: 12
2021-07-16 05:30:46,175:INFO:display_container: 2
2021-07-16 05:30:46,175: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)
2021-07-16 05:30:46,175:INFO:create_model() succesfully completed......................................
2021-07-16 05:30:46,254:INFO:SubProcess create_model() end ==================================
2021-07-16 05:30:46,255:INFO:Creating metrics dataframe
2021-07-16 05:30:46,278:INFO:Initializing Light Gradient Boosting Machine
2021-07-16 05:30:46,278:INFO:Total runtime is 0.2435410022735596 minutes
2021-07-16 05:30:46,288:INFO:SubProcess create_model() called ==================================
2021-07-16 05:30:46,288:INFO:Initializing create_model()
2021-07-16 05:30:46,289:INFO:create_model(estimator=lightgbm, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d6538c50>, kwargs={})
2021-07-16 05:30:46,289:INFO:Checking exceptions
2021-07-16 05:30:46,289:INFO:Importing libraries
2021-07-16 05:30:46,289:INFO:Copying training dataset
2021-07-16 05:30:46,290:INFO:Defining folds
2021-07-16 05:30:46,291:INFO:Declaring metric variables
2021-07-16 05:30:46,299:INFO:Importing untrained model
2021-07-16 05:30:46,309:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 05:30:46,332:INFO:Starting cross validation
2021-07-16 05:30:46,333:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:30:47,532:INFO:Calculating mean and std
2021-07-16 05:30:47,533:INFO:Creating metrics dataframe
2021-07-16 05:30:47,539:INFO:Uploading results into container
2021-07-16 05:30:47,539:INFO:Uploading model into container now
2021-07-16 05:30:47,540:INFO:create_model_container: 13
2021-07-16 05:30:47,540:INFO:master_model_container: 13
2021-07-16 05:30:47,540:INFO:display_container: 2
2021-07-16 05:30:47,541: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 05:30:47,542:INFO:create_model() succesfully completed......................................
2021-07-16 05:30:47,631:INFO:SubProcess create_model() end ==================================
2021-07-16 05:30:47,632:INFO:Creating metrics dataframe
2021-07-16 05:30:47,679:INFO:Initializing create_model()
2021-07-16 05:30:47,679:INFO:create_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, display=None, kwargs={})
2021-07-16 05:30:47,679:INFO:Checking exceptions
2021-07-16 05:30:47,680:INFO:Importing libraries
2021-07-16 05:30:47,680:INFO:Copying training dataset
2021-07-16 05:30:47,680:INFO:Defining folds
2021-07-16 05:30:47,681:INFO:Declaring metric variables
2021-07-16 05:30:47,681:INFO:Importing untrained model
2021-07-16 05:30:47,681:INFO:Declaring custom model
2021-07-16 05:30:47,682:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 05:30:47,682:INFO:Cross validation set to False
2021-07-16 05:30:47,682:INFO:Fitting Model
2021-07-16 05:30:48,035: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 05:30:48,036:INFO:create_models() succesfully completed......................................
2021-07-16 05:30:48,106:INFO:Creating MLFlow logs
2021-07-16 05:30:48,151:INFO:Model: Light Gradient Boosting Machine
2021-07-16 05:30:48,385: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': True, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0}
2021-07-16 05:30:49,066:INFO:Initializing predict_model()
2021-07-16 05:30:49,066:INFO:predict_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-16 05:30:49,066:INFO:Checking exceptions
2021-07-16 05:30:49,066:INFO:Preloading libraries
2021-07-16 05:30:49,409:INFO:Creating MLFlow logs
2021-07-16 05:30:49,453:INFO:Model: Gradient Boosting Classifier
2021-07-16 05:30:49,679: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}
2021-07-16 05:30:50,578:INFO:Creating MLFlow logs
2021-07-16 05:30:50,615:INFO:Model: Extra Trees Classifier
2021-07-16 05:30:50,797: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}
2021-07-16 05:30:51,683:INFO:Creating MLFlow logs
2021-07-16 05:30:51,720:INFO:Model: Random Forest Classifier
2021-07-16 05:30:51,906: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}
2021-07-16 05:30:52,711:INFO:Creating MLFlow logs
2021-07-16 05:30:52,746:INFO:Model: Ada Boost Classifier
2021-07-16 05:30:52,909:INFO:logged params: {'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1.0, 'n_estimators': 50, 'random_state': 123}
2021-07-16 05:30:53,553:INFO:Creating MLFlow logs
2021-07-16 05:30:53,588:INFO:Model: Decision Tree Classifier
2021-07-16 05:30:53,751: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'}
2021-07-16 05:30:54,410:INFO:Creating MLFlow logs
2021-07-16 05:30:54,444:INFO:Model: Naive Bayes
2021-07-16 05:30:54,633:INFO:logged params: {'priors': None, 'var_smoothing': 1e-09}
2021-07-16 05:30:55,275:INFO:Creating MLFlow logs
2021-07-16 05:30:55,319:INFO:Model: Linear Discriminant Analysis
2021-07-16 05:30:55,545:INFO:logged params: {'n_components': None, 'priors': None, 'shrinkage': None, 'solver': 'svd', 'store_covariance': False, 'tol': 0.0001}
2021-07-16 05:30:56,110:INFO:Creating MLFlow logs
2021-07-16 05:30:56,142:INFO:Model: Ridge Classifier
2021-07-16 05:30:56,342: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}
2021-07-16 05:30:56,965:INFO:Creating MLFlow logs
2021-07-16 05:30:56,999:INFO:Model: Logistic Regression
2021-07-16 05:30:57,185: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}
2021-07-16 05:30:57,888:INFO:Creating MLFlow logs
2021-07-16 05:30:57,919:INFO:Model: Quadratic Discriminant Analysis
2021-07-16 05:30:58,081:INFO:logged params: {'priors': None, 'reg_param': 0.0, 'store_covariance': False, 'tol': 0.0001}
2021-07-16 05:30:58,678:INFO:Creating MLFlow logs
2021-07-16 05:30:58,710:INFO:Model: K Neighbors Classifier
2021-07-16 05:30:58,870:INFO:logged params: {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 5, 'p': 2, 'weights': 'uniform'}
2021-07-16 05:30:59,505:INFO:Creating MLFlow logs
2021-07-16 05:30:59,550:INFO:Model: SVM - Linear Kernel
2021-07-16 05:30:59,783: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}
2021-07-16 05:31:00,585:INFO:create_model_container: 13
2021-07-16 05:31:00,586:INFO:master_model_container: 13
2021-07-16 05:31:00,586:INFO:display_container: 2
2021-07-16 05:31:00,587: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 05:31:00,587:INFO:compare_models() succesfully completed......................................
2021-07-16 05:31:23,610:INFO:gpu_param set to False
2021-07-16 05:31:23,673:WARNING:Couldn't import xgboost.XGBClassifier
2021-07-16 05:31:23,674:WARNING:Couldn't import catboost.CatBoostClassifier
2021-07-16 05:38:41,476:INFO:Initializing create_model()
2021-07-16 05:38:41,477:INFO:create_model(estimator=<class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-07-16 05:38:41,477:INFO:Checking exceptions
2021-07-16 05:38:41,477:INFO:Preparing display monitor
2021-07-16 05:38:41,498:INFO:Importing libraries
2021-07-16 05:38:41,498:INFO:Copying training dataset
2021-07-16 05:38:41,500:INFO:Defining folds
2021-07-16 05:38:41,500:INFO:Declaring metric variables
2021-07-16 05:38:41,508:INFO:Importing untrained model
2021-07-16 05:38:41,508:INFO:Declaring custom model
2021-07-16 05:38:41,515:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 05:38:41,535:INFO:Starting cross validation
2021-07-16 05:38:41,549:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:38:44,746:INFO:Calculating mean and std
2021-07-16 05:38:44,749:INFO:Creating metrics dataframe
2021-07-16 05:38:44,780:INFO:Finalizing model
2021-07-16 05:43:52,042:INFO:Initializing create_model()
2021-07-16 05:43:52,042:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-07-16 05:43:52,043:INFO:Checking exceptions
2021-07-16 05:43:52,043:INFO:Preparing display monitor
2021-07-16 05:43:52,061:INFO:Importing libraries
2021-07-16 05:43:52,061:INFO:Copying training dataset
2021-07-16 05:43:52,063:INFO:Defining folds
2021-07-16 05:43:52,063:INFO:Declaring metric variables
2021-07-16 05:43:52,073:INFO:Importing untrained model
2021-07-16 05:43:52,075:INFO:Declaring custom model
2021-07-16 05:43:52,084:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 05:43:52,103:INFO:Starting cross validation
2021-07-16 05:43:52,104:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:43:56,876:INFO:Calculating mean and std
2021-07-16 05:43:56,879:INFO:Creating metrics dataframe
2021-07-16 05:43:56,908:INFO:Finalizing model
2021-07-16 05:43:58,248:INFO:Creating MLFlow logs
2021-07-16 05:43:58,296:INFO:Model: NeuralNetClassifier
2021-07-16 05:43:58,525:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 10, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc3fbec6b10>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc3fbf74fd0>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbe2e1d0>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbe2ee10>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc3fbe4b150>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc3fbe4b690>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 05:43:59,342:INFO:Initializing predict_model()
2021-07-16 05:43:59,343:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=10, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=10, out_features=10, bias=True)
    (output): Linear(in_features=10, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-16 05:43:59,343:INFO:Checking exceptions
2021-07-16 05:43:59,343:INFO:Preloading libraries
2021-07-16 05:43:59,355:WARNING:Couldn't create holdout prediction for model, exception below:
2021-07-16 05:43:59,356:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 10139, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 8559, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-07-16 05:43:59,623:INFO:Uploading results into container
2021-07-16 05:43:59,624:INFO:Uploading model into container now
2021-07-16 05:43:59,641:INFO:create_model_container: 14
2021-07-16 05:43:59,642:INFO:master_model_container: 14
2021-07-16 05:43:59,642:INFO:display_container: 3
2021-07-16 05:43:59,644:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=10, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=10, out_features=10, bias=True)
    (output): Linear(in_features=10, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-16 05:43:59,645:INFO:create_model() succesfully completed......................................
2021-07-16 05:44:15,475:INFO:Initializing create_model()
2021-07-16 05:44:15,476:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-07-16 05:44:15,476:INFO:Checking exceptions
2021-07-16 05:44:15,476:INFO:Preparing display monitor
2021-07-16 05:44:15,495:INFO:Importing libraries
2021-07-16 05:44:15,496:INFO:Copying training dataset
2021-07-16 05:44:15,497:INFO:Defining folds
2021-07-16 05:44:15,497:INFO:Declaring metric variables
2021-07-16 05:44:15,506:INFO:Importing untrained model
2021-07-16 05:44:15,506:INFO:Declaring custom model
2021-07-16 05:44:15,515:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 05:44:15,533:INFO:Starting cross validation
2021-07-16 05:44:15,534:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:44:18,909:INFO:Calculating mean and std
2021-07-16 05:44:18,910:INFO:Creating metrics dataframe
2021-07-16 05:44:18,924:INFO:Finalizing model
2021-07-16 05:44:20,233:INFO:Creating MLFlow logs
2021-07-16 05:44:20,294:INFO:Model: NeuralNetClassifier
2021-07-16 05:44:20,519:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 10, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc3fbe87b90>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc3fbf8a150>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbf84290>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbe2ebd0>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc3fbe2e7d0>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc3fbe2ed50>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 05:44:21,426:INFO:Initializing predict_model()
2021-07-16 05:44:21,427:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=10, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=10, out_features=10, bias=True)
    (output): Linear(in_features=10, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-16 05:44:21,427:INFO:Checking exceptions
2021-07-16 05:44:21,427:INFO:Preloading libraries
2021-07-16 05:44:21,439:WARNING:Couldn't create holdout prediction for model, exception below:
2021-07-16 05:44:21,439:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 10139, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 8559, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-07-16 05:44:21,693:INFO:Uploading results into container
2021-07-16 05:44:21,694:INFO:Uploading model into container now
2021-07-16 05:44:21,712:INFO:create_model_container: 15
2021-07-16 05:44:21,713:INFO:master_model_container: 15
2021-07-16 05:44:21,713:INFO:display_container: 4
2021-07-16 05:44:21,714:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=10, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=10, out_features=10, bias=True)
    (output): Linear(in_features=10, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-16 05:44:21,715:INFO:create_model() succesfully completed......................................
2021-07-16 05:44:35,401:INFO:Initializing compare_models()
2021-07-16 05:44:35,402:INFO:compare_models(include=[Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=10, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=10, out_features=10, bias=True)
    (output): Linear(in_features=10, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 'lr', 'lightgbm'], fold=None, round=4, cross_validation=True, sort=Accuracy, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-07-16 05:44:35,402:INFO:Checking exceptions
2021-07-16 05:44:35,403:INFO:Preparing display monitor
2021-07-16 05:44:35,404:INFO:Preparing display monitor
2021-07-16 05:44:35,431:INFO:Initializing custom model NeuralNetClassifier
2021-07-16 05:44:35,431:INFO:Total runtime is 1.1297067006429036e-05 minutes
2021-07-16 05:44:35,441:INFO:SubProcess create_model() called ==================================
2021-07-16 05:44:35,443:INFO:Initializing create_model()
2021-07-16 05:44:35,443:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=10, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=10, out_features=10, bias=True)
    (output): Linear(in_features=10, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d65101d0>, kwargs={})
2021-07-16 05:44:35,443:INFO:Checking exceptions
2021-07-16 05:44:35,443:INFO:Importing libraries
2021-07-16 05:44:35,444:INFO:Copying training dataset
2021-07-16 05:44:35,444:INFO:Defining folds
2021-07-16 05:44:35,445:INFO:Declaring metric variables
2021-07-16 05:44:35,454:INFO:Importing untrained model
2021-07-16 05:44:35,454:INFO:Declaring custom model
2021-07-16 05:44:35,464:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 05:44:35,481:INFO:Starting cross validation
2021-07-16 05:44:35,482:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:44:37,681:INFO:Calculating mean and std
2021-07-16 05:44:37,682:INFO:Creating metrics dataframe
2021-07-16 05:44:37,688:INFO:Uploading results into container
2021-07-16 05:44:37,688:INFO:Uploading model into container now
2021-07-16 05:44:37,688:INFO:create_model_container: 16
2021-07-16 05:44:37,688:INFO:master_model_container: 16
2021-07-16 05:44:37,689:INFO:display_container: 5
2021-07-16 05:44:37,690:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False)
2021-07-16 05:44:37,690:INFO:create_model() succesfully completed......................................
2021-07-16 05:44:37,787:INFO:SubProcess create_model() end ==================================
2021-07-16 05:44:37,788:INFO:Creating metrics dataframe
2021-07-16 05:44:37,807:INFO:Initializing Logistic Regression
2021-07-16 05:44:37,808:INFO:Total runtime is 0.039618881543477376 minutes
2021-07-16 05:44:37,818:INFO:SubProcess create_model() called ==================================
2021-07-16 05:44:37,819:INFO:Initializing create_model()
2021-07-16 05:44:37,819:INFO:create_model(estimator=lr, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d65101d0>, kwargs={})
2021-07-16 05:44:37,819:INFO:Checking exceptions
2021-07-16 05:44:37,819:INFO:Importing libraries
2021-07-16 05:44:37,820:INFO:Copying training dataset
2021-07-16 05:44:37,821:INFO:Defining folds
2021-07-16 05:44:37,821:INFO:Declaring metric variables
2021-07-16 05:44:37,829:INFO:Importing untrained model
2021-07-16 05:44:37,838:INFO:Logistic Regression Imported succesfully
2021-07-16 05:44:37,855:INFO:Starting cross validation
2021-07-16 05:44:37,856:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:44:37,982:INFO:Calculating mean and std
2021-07-16 05:44:37,983:INFO:Creating metrics dataframe
2021-07-16 05:44:37,989:INFO:Uploading results into container
2021-07-16 05:44:37,990:INFO:Uploading model into container now
2021-07-16 05:44:37,990:INFO:create_model_container: 17
2021-07-16 05:44:37,990:INFO:master_model_container: 17
2021-07-16 05:44:37,990:INFO:display_container: 5
2021-07-16 05:44:37,991: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)
2021-07-16 05:44:37,991:INFO:create_model() succesfully completed......................................
2021-07-16 05:44:38,086:INFO:SubProcess create_model() end ==================================
2021-07-16 05:44:38,087:INFO:Creating metrics dataframe
2021-07-16 05:44:38,105:INFO:Initializing Light Gradient Boosting Machine
2021-07-16 05:44:38,106:INFO:Total runtime is 0.04458718299865723 minutes
2021-07-16 05:44:38,115:INFO:SubProcess create_model() called ==================================
2021-07-16 05:44:38,115:INFO:Initializing create_model()
2021-07-16 05:44:38,115:INFO:create_model(estimator=lightgbm, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3d65101d0>, kwargs={})
2021-07-16 05:44:38,116:INFO:Checking exceptions
2021-07-16 05:44:38,116:INFO:Importing libraries
2021-07-16 05:44:38,116:INFO:Copying training dataset
2021-07-16 05:44:38,117:INFO:Defining folds
2021-07-16 05:44:38,117:INFO:Declaring metric variables
2021-07-16 05:44:38,126:INFO:Importing untrained model
2021-07-16 05:44:38,134:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 05:44:38,151:INFO:Starting cross validation
2021-07-16 05:44:38,153:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 05:44:39,098:INFO:Calculating mean and std
2021-07-16 05:44:39,099:INFO:Creating metrics dataframe
2021-07-16 05:44:39,104:INFO:Uploading results into container
2021-07-16 05:44:39,105:INFO:Uploading model into container now
2021-07-16 05:44:39,105:INFO:create_model_container: 18
2021-07-16 05:44:39,105:INFO:master_model_container: 18
2021-07-16 05:44:39,105:INFO:display_container: 5
2021-07-16 05:44:39,106: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 05:44:39,106:INFO:create_model() succesfully completed......................................
2021-07-16 05:44:39,197:INFO:SubProcess create_model() end ==================================
2021-07-16 05:44:39,198:INFO:Creating metrics dataframe
2021-07-16 05:44:39,237:INFO:Initializing create_model()
2021-07-16 05:44:39,237:INFO:create_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, display=None, kwargs={})
2021-07-16 05:44:39,238:INFO:Checking exceptions
2021-07-16 05:44:39,238:INFO:Importing libraries
2021-07-16 05:44:39,238:INFO:Copying training dataset
2021-07-16 05:44:39,239:INFO:Defining folds
2021-07-16 05:44:39,239:INFO:Declaring metric variables
2021-07-16 05:44:39,239:INFO:Importing untrained model
2021-07-16 05:44:39,240:INFO:Declaring custom model
2021-07-16 05:44:39,240:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 05:44:39,241:INFO:Cross validation set to False
2021-07-16 05:44:39,241:INFO:Fitting Model
2021-07-16 05:44:39,403: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 05:44:39,404:INFO:create_models() succesfully completed......................................
2021-07-16 05:44:39,496:INFO:Creating MLFlow logs
2021-07-16 05:44:39,544:INFO:Model: Light Gradient Boosting Machine
2021-07-16 05:44:39,767: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': True, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0}
2021-07-16 05:44:40,452:INFO:Initializing predict_model()
2021-07-16 05:44:40,452:INFO:predict_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-16 05:44:40,452:INFO:Checking exceptions
2021-07-16 05:44:40,452:INFO:Preloading libraries
2021-07-16 05:44:40,880:INFO:Creating MLFlow logs
2021-07-16 05:44:40,930:INFO:Model: Logistic Regression
2021-07-16 05:44:41,169: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}
2021-07-16 05:44:41,905:INFO:Creating MLFlow logs
2021-07-16 05:44:41,938:INFO:Model: NeuralNetClassifier
2021-07-16 05:44:42,095:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 10, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc3fbe2d650>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc3fbe4b310>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbfe9b90>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc47b6f2b50>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc3fbe38f10>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc3ce0f6550>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 05:44:43,107:INFO:create_model_container: 18
2021-07-16 05:44:43,107:INFO:master_model_container: 18
2021-07-16 05:44:43,107:INFO:display_container: 5
2021-07-16 05:44:43,108: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 05:44:43,108:INFO:compare_models() succesfully completed......................................
2021-07-16 06:06:03,572:INFO:Initializing create_model()
2021-07-16 06:06:03,572:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-07-16 06:06:03,573:INFO:Checking exceptions
2021-07-16 06:06:03,574:INFO:Preparing display monitor
2021-07-16 06:06:03,591:INFO:Importing libraries
2021-07-16 06:06:03,591:INFO:Copying training dataset
2021-07-16 06:06:03,592:INFO:Defining folds
2021-07-16 06:06:03,592:INFO:Declaring metric variables
2021-07-16 06:06:03,599:INFO:Importing untrained model
2021-07-16 06:06:03,600:INFO:Declaring custom model
2021-07-16 06:06:03,609:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 06:06:03,624:INFO:Starting cross validation
2021-07-16 06:06:03,625:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:06:08,539:INFO:Calculating mean and std
2021-07-16 06:06:08,540:INFO:Creating metrics dataframe
2021-07-16 06:06:08,557:INFO:Finalizing model
2021-07-16 06:06:09,910:INFO:Creating MLFlow logs
2021-07-16 06:06:09,957:INFO:Model: NeuralNetClassifier
2021-07-16 06:06:10,187:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 10, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc3cc0c2950>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc3fbb93290>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbcfbcd0>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbcfb0d0>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc3fbcfbf50>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc3fbcd8610>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 06:06:11,099:INFO:Initializing predict_model()
2021-07-16 06:06:11,100:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=100, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=100, out_features=50, bias=True)
    (output): Linear(in_features=50, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-16 06:06:11,100:INFO:Checking exceptions
2021-07-16 06:06:11,100:INFO:Preloading libraries
2021-07-16 06:06:11,110:WARNING:Couldn't create holdout prediction for model, exception below:
2021-07-16 06:06:11,111:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 10139, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 8559, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-07-16 06:06:11,375:INFO:Uploading results into container
2021-07-16 06:06:11,376:INFO:Uploading model into container now
2021-07-16 06:06:11,393:INFO:create_model_container: 19
2021-07-16 06:06:11,394:INFO:master_model_container: 19
2021-07-16 06:06:11,394:INFO:display_container: 6
2021-07-16 06:06:11,396:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=100, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=100, out_features=50, bias=True)
    (output): Linear(in_features=50, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-16 06:06:11,396:INFO:create_model() succesfully completed......................................
2021-07-16 06:06:11,574:INFO:Initializing compare_models()
2021-07-16 06:06:11,574:INFO:compare_models(include=[Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=100, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=100, out_features=50, bias=True)
    (output): Linear(in_features=50, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 'lr', 'lightgbm'], fold=None, round=4, cross_validation=True, sort=Accuracy, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-07-16 06:06:11,574:INFO:Checking exceptions
2021-07-16 06:06:11,575:INFO:Preparing display monitor
2021-07-16 06:06:11,576:INFO:Preparing display monitor
2021-07-16 06:06:11,603:INFO:Initializing custom model NeuralNetClassifier
2021-07-16 06:06:11,604:INFO:Total runtime is 2.3337205251057944e-05 minutes
2021-07-16 06:06:11,614:INFO:SubProcess create_model() called ==================================
2021-07-16 06:06:11,617:INFO:Initializing create_model()
2021-07-16 06:06:11,618:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=100, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=100, out_features=50, bias=True)
    (output): Linear(in_features=50, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3fbdde7d0>, kwargs={})
2021-07-16 06:06:11,618:INFO:Checking exceptions
2021-07-16 06:06:11,619:INFO:Importing libraries
2021-07-16 06:06:11,619:INFO:Copying training dataset
2021-07-16 06:06:11,620:INFO:Defining folds
2021-07-16 06:06:11,621:INFO:Declaring metric variables
2021-07-16 06:06:11,629:INFO:Importing untrained model
2021-07-16 06:06:11,629:INFO:Declaring custom model
2021-07-16 06:06:11,639:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 06:06:11,657:INFO:Starting cross validation
2021-07-16 06:06:11,657:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:06:15,573:INFO:Calculating mean and std
2021-07-16 06:06:15,575:INFO:Creating metrics dataframe
2021-07-16 06:06:15,580:INFO:Uploading results into container
2021-07-16 06:06:15,581:INFO:Uploading model into container now
2021-07-16 06:06:15,581:INFO:create_model_container: 20
2021-07-16 06:06:15,582:INFO:master_model_container: 20
2021-07-16 06:06:15,582:INFO:display_container: 7
2021-07-16 06:06:15,584:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False)
2021-07-16 06:06:15,585:INFO:create_model() succesfully completed......................................
2021-07-16 06:06:15,707:INFO:SubProcess create_model() end ==================================
2021-07-16 06:06:15,707:INFO:Creating metrics dataframe
2021-07-16 06:06:15,722:INFO:Initializing Logistic Regression
2021-07-16 06:06:15,723:INFO:Total runtime is 0.06867478688557943 minutes
2021-07-16 06:06:15,734:INFO:SubProcess create_model() called ==================================
2021-07-16 06:06:15,735:INFO:Initializing create_model()
2021-07-16 06:06:15,735:INFO:create_model(estimator=lr, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3fbdde7d0>, kwargs={})
2021-07-16 06:06:15,735:INFO:Checking exceptions
2021-07-16 06:06:15,735:INFO:Importing libraries
2021-07-16 06:06:15,736:INFO:Copying training dataset
2021-07-16 06:06:15,736:INFO:Defining folds
2021-07-16 06:06:15,737:INFO:Declaring metric variables
2021-07-16 06:06:15,747:INFO:Importing untrained model
2021-07-16 06:06:15,757:INFO:Logistic Regression Imported succesfully
2021-07-16 06:06:15,775:INFO:Starting cross validation
2021-07-16 06:06:15,776:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:06:15,921:INFO:Calculating mean and std
2021-07-16 06:06:15,922:INFO:Creating metrics dataframe
2021-07-16 06:06:15,926:INFO:Uploading results into container
2021-07-16 06:06:15,926:INFO:Uploading model into container now
2021-07-16 06:06:15,926:INFO:create_model_container: 21
2021-07-16 06:06:15,926:INFO:master_model_container: 21
2021-07-16 06:06:15,927:INFO:display_container: 7
2021-07-16 06:06:15,927: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)
2021-07-16 06:06:15,928:INFO:create_model() succesfully completed......................................
2021-07-16 06:06:16,046:INFO:SubProcess create_model() end ==================================
2021-07-16 06:06:16,046:INFO:Creating metrics dataframe
2021-07-16 06:06:16,067:INFO:Initializing Light Gradient Boosting Machine
2021-07-16 06:06:16,067:INFO:Total runtime is 0.07441486914952597 minutes
2021-07-16 06:06:16,077:INFO:SubProcess create_model() called ==================================
2021-07-16 06:06:16,077:INFO:Initializing create_model()
2021-07-16 06:06:16,078:INFO:create_model(estimator=lightgbm, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3fbdde7d0>, kwargs={})
2021-07-16 06:06:16,078:INFO:Checking exceptions
2021-07-16 06:06:16,078:INFO:Importing libraries
2021-07-16 06:06:16,078:INFO:Copying training dataset
2021-07-16 06:06:16,079:INFO:Defining folds
2021-07-16 06:06:16,079:INFO:Declaring metric variables
2021-07-16 06:06:16,090:INFO:Importing untrained model
2021-07-16 06:06:16,100:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 06:06:16,120:INFO:Starting cross validation
2021-07-16 06:06:16,121:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:06:17,030:INFO:Calculating mean and std
2021-07-16 06:06:17,031:INFO:Creating metrics dataframe
2021-07-16 06:06:17,035:INFO:Uploading results into container
2021-07-16 06:06:17,036:INFO:Uploading model into container now
2021-07-16 06:06:17,036:INFO:create_model_container: 22
2021-07-16 06:06:17,036:INFO:master_model_container: 22
2021-07-16 06:06:17,037:INFO:display_container: 7
2021-07-16 06:06:17,037: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:06:17,037:INFO:create_model() succesfully completed......................................
2021-07-16 06:06:17,144:INFO:SubProcess create_model() end ==================================
2021-07-16 06:06:17,145:INFO:Creating metrics dataframe
2021-07-16 06:06:17,184:INFO:Initializing create_model()
2021-07-16 06:06:17,185:INFO:create_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, display=None, kwargs={})
2021-07-16 06:06:17,185:INFO:Checking exceptions
2021-07-16 06:06:17,185:INFO:Importing libraries
2021-07-16 06:06:17,185:INFO:Copying training dataset
2021-07-16 06:06:17,186:INFO:Defining folds
2021-07-16 06:06:17,186:INFO:Declaring metric variables
2021-07-16 06:06:17,186:INFO:Importing untrained model
2021-07-16 06:06:17,187:INFO:Declaring custom model
2021-07-16 06:06:17,188:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 06:06:17,188:INFO:Cross validation set to False
2021-07-16 06:06:17,189:INFO:Fitting Model
2021-07-16 06:06:17,342: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:06:17,342:INFO:create_models() succesfully completed......................................
2021-07-16 06:06:17,449:INFO:Creating MLFlow logs
2021-07-16 06:06:17,497:INFO:Model: Light Gradient Boosting Machine
2021-07-16 06:06:17,720: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': True, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0}
2021-07-16 06:06:18,293:INFO:Initializing predict_model()
2021-07-16 06:06:18,293:INFO:predict_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-16 06:06:18,293:INFO:Checking exceptions
2021-07-16 06:06:18,294:INFO:Preloading libraries
2021-07-16 06:06:18,698:INFO:Creating MLFlow logs
2021-07-16 06:06:18,742:INFO:Model: Logistic Regression
2021-07-16 06:06:18,991: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}
2021-07-16 06:06:19,771:INFO:Creating MLFlow logs
2021-07-16 06:06:19,809:INFO:Model: NeuralNetClassifier
2021-07-16 06:06:19,983:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 10, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc3fbb77f50>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc3fbe546d0>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbe5ae50>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbe5aa90>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc3fbd41fd0>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc3fbd411d0>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 06:06:20,982:INFO:create_model_container: 22
2021-07-16 06:06:20,983:INFO:master_model_container: 22
2021-07-16 06:06:20,983:INFO:display_container: 7
2021-07-16 06:06:20,984: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:06:20,984:INFO:compare_models() succesfully completed......................................
2021-07-16 06:06:41,284:INFO:Initializing create_model()
2021-07-16 06:06:41,285:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-07-16 06:06:41,285:INFO:Checking exceptions
2021-07-16 06:06:41,286:INFO:Preparing display monitor
2021-07-16 06:06:41,304:INFO:Importing libraries
2021-07-16 06:06:41,305:INFO:Copying training dataset
2021-07-16 06:06:41,308:INFO:Defining folds
2021-07-16 06:06:41,308:INFO:Declaring metric variables
2021-07-16 06:06:41,319:INFO:Importing untrained model
2021-07-16 06:06:41,319:INFO:Declaring custom model
2021-07-16 06:06:41,328:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 06:06:41,347:INFO:Starting cross validation
2021-07-16 06:06:41,349:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:06:43,799:INFO:Calculating mean and std
2021-07-16 06:06:43,801:INFO:Creating metrics dataframe
2021-07-16 06:06:43,812:INFO:Finalizing model
2021-07-16 06:06:45,281:INFO:Creating MLFlow logs
2021-07-16 06:06:45,325:INFO:Model: NeuralNetClassifier
2021-07-16 06:06:45,544:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 10, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc3fbb6e910>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc34012d790>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbb77d90>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbcce690>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc3fbcce8d0>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc3fbccecd0>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 06:06:46,442:INFO:Initializing predict_model()
2021-07-16 06:06:46,442:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-16 06:06:46,443:INFO:Checking exceptions
2021-07-16 06:06:46,443:INFO:Preloading libraries
2021-07-16 06:06:46,452:WARNING:Couldn't create holdout prediction for model, exception below:
2021-07-16 06:06:46,453:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 10139, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 8559, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-07-16 06:06:46,707:INFO:Uploading results into container
2021-07-16 06:06:46,707:INFO:Uploading model into container now
2021-07-16 06:06:46,728:INFO:create_model_container: 23
2021-07-16 06:06:46,728:INFO:master_model_container: 23
2021-07-16 06:06:46,728:INFO:display_container: 8
2021-07-16 06:06:46,730:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-16 06:06:46,730:INFO:create_model() succesfully completed......................................
2021-07-16 06:06:49,899:INFO:Initializing compare_models()
2021-07-16 06:06:49,900:INFO:compare_models(include=[Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 'lr', 'lightgbm'], fold=None, round=4, cross_validation=True, sort=Accuracy, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-07-16 06:06:49,901:INFO:Checking exceptions
2021-07-16 06:06:49,902:INFO:Preparing display monitor
2021-07-16 06:06:49,902:INFO:Preparing display monitor
2021-07-16 06:06:49,930:INFO:Initializing custom model NeuralNetClassifier
2021-07-16 06:06:49,930:INFO:Total runtime is 1.3109048207600912e-05 minutes
2021-07-16 06:06:49,941:INFO:SubProcess create_model() called ==================================
2021-07-16 06:06:49,943:INFO:Initializing create_model()
2021-07-16 06:06:49,943:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3cc0b2990>, kwargs={})
2021-07-16 06:06:49,943:INFO:Checking exceptions
2021-07-16 06:06:49,943:INFO:Importing libraries
2021-07-16 06:06:49,944:INFO:Copying training dataset
2021-07-16 06:06:49,944:INFO:Defining folds
2021-07-16 06:06:49,945:INFO:Declaring metric variables
2021-07-16 06:06:49,953:INFO:Importing untrained model
2021-07-16 06:06:49,954:INFO:Declaring custom model
2021-07-16 06:06:49,964:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 06:06:49,988:INFO:Starting cross validation
2021-07-16 06:06:49,989:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:06:52,506:INFO:Calculating mean and std
2021-07-16 06:06:52,508:INFO:Creating metrics dataframe
2021-07-16 06:06:52,512:INFO:Uploading results into container
2021-07-16 06:06:52,512:INFO:Uploading model into container now
2021-07-16 06:06:52,513:INFO:create_model_container: 24
2021-07-16 06:06:52,513:INFO:master_model_container: 24
2021-07-16 06:06:52,513:INFO:display_container: 9
2021-07-16 06:06:52,514:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False)
2021-07-16 06:06:52,514:INFO:create_model() succesfully completed......................................
2021-07-16 06:06:52,624:INFO:SubProcess create_model() end ==================================
2021-07-16 06:06:52,624:INFO:Creating metrics dataframe
2021-07-16 06:06:52,641:INFO:Initializing Logistic Regression
2021-07-16 06:06:52,642:INFO:Total runtime is 0.04520461161931356 minutes
2021-07-16 06:06:52,652:INFO:SubProcess create_model() called ==================================
2021-07-16 06:06:52,652:INFO:Initializing create_model()
2021-07-16 06:06:52,653:INFO:create_model(estimator=lr, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3cc0b2990>, kwargs={})
2021-07-16 06:06:52,653:INFO:Checking exceptions
2021-07-16 06:06:52,653:INFO:Importing libraries
2021-07-16 06:06:52,653:INFO:Copying training dataset
2021-07-16 06:06:52,654:INFO:Defining folds
2021-07-16 06:06:52,654:INFO:Declaring metric variables
2021-07-16 06:06:52,662:INFO:Importing untrained model
2021-07-16 06:06:52,671:INFO:Logistic Regression Imported succesfully
2021-07-16 06:06:52,688:INFO:Starting cross validation
2021-07-16 06:06:52,689:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:06:52,809:INFO:Calculating mean and std
2021-07-16 06:06:52,810:INFO:Creating metrics dataframe
2021-07-16 06:06:52,814:INFO:Uploading results into container
2021-07-16 06:06:52,815:INFO:Uploading model into container now
2021-07-16 06:06:52,815:INFO:create_model_container: 25
2021-07-16 06:06:52,815:INFO:master_model_container: 25
2021-07-16 06:06:52,816:INFO:display_container: 9
2021-07-16 06:06:52,816: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)
2021-07-16 06:06:52,817:INFO:create_model() succesfully completed......................................
2021-07-16 06:06:52,929:INFO:SubProcess create_model() end ==================================
2021-07-16 06:06:52,930:INFO:Creating metrics dataframe
2021-07-16 06:06:52,949:INFO:Initializing Light Gradient Boosting Machine
2021-07-16 06:06:52,949:INFO:Total runtime is 0.050331521034240725 minutes
2021-07-16 06:06:52,961:INFO:SubProcess create_model() called ==================================
2021-07-16 06:06:52,961:INFO:Initializing create_model()
2021-07-16 06:06:52,961:INFO:create_model(estimator=lightgbm, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3cc0b2990>, kwargs={})
2021-07-16 06:06:52,962:INFO:Checking exceptions
2021-07-16 06:06:52,962:INFO:Importing libraries
2021-07-16 06:06:52,962:INFO:Copying training dataset
2021-07-16 06:06:52,963:INFO:Defining folds
2021-07-16 06:06:52,963:INFO:Declaring metric variables
2021-07-16 06:06:52,972:INFO:Importing untrained model
2021-07-16 06:06:52,980:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 06:06:52,998:INFO:Starting cross validation
2021-07-16 06:06:52,999:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:06:53,683:INFO:Calculating mean and std
2021-07-16 06:06:53,684:INFO:Creating metrics dataframe
2021-07-16 06:06:53,689:INFO:Uploading results into container
2021-07-16 06:06:53,690:INFO:Uploading model into container now
2021-07-16 06:06:53,690:INFO:create_model_container: 26
2021-07-16 06:06:53,690:INFO:master_model_container: 26
2021-07-16 06:06:53,691:INFO:display_container: 9
2021-07-16 06:06:53,691: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:06:53,692:INFO:create_model() succesfully completed......................................
2021-07-16 06:06:53,801:INFO:SubProcess create_model() end ==================================
2021-07-16 06:06:53,801:INFO:Creating metrics dataframe
2021-07-16 06:06:53,838:INFO:Initializing create_model()
2021-07-16 06:06:53,838:INFO:create_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, display=None, kwargs={})
2021-07-16 06:06:53,838:INFO:Checking exceptions
2021-07-16 06:06:53,839:INFO:Importing libraries
2021-07-16 06:06:53,839:INFO:Copying training dataset
2021-07-16 06:06:53,839:INFO:Defining folds
2021-07-16 06:06:53,840:INFO:Declaring metric variables
2021-07-16 06:06:53,840:INFO:Importing untrained model
2021-07-16 06:06:53,840:INFO:Declaring custom model
2021-07-16 06:06:53,841:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 06:06:53,842:INFO:Cross validation set to False
2021-07-16 06:06:53,842:INFO:Fitting Model
2021-07-16 06:06:54,304: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:06:54,304:INFO:create_models() succesfully completed......................................
2021-07-16 06:06:54,413:INFO:Creating MLFlow logs
2021-07-16 06:06:54,461:INFO:Model: Light Gradient Boosting Machine
2021-07-16 06:06:54,691: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': True, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0}
2021-07-16 06:06:55,356:INFO:Initializing predict_model()
2021-07-16 06:06:55,357:INFO:predict_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-16 06:06:55,357:INFO:Checking exceptions
2021-07-16 06:06:55,357:INFO:Preloading libraries
2021-07-16 06:06:55,769:INFO:Creating MLFlow logs
2021-07-16 06:06:55,815:INFO:Model: Logistic Regression
2021-07-16 06:06:56,030: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}
2021-07-16 06:06:56,706:INFO:Creating MLFlow logs
2021-07-16 06:06:56,738:INFO:Model: NeuralNetClassifier
2021-07-16 06:06:56,895:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 10, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc3fbcdc8d0>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc3fbf84fd0>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbe46090>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3cc0e4450>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc34014d8d0>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc34014d2d0>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 06:06:57,874:INFO:create_model_container: 26
2021-07-16 06:06:57,874:INFO:master_model_container: 26
2021-07-16 06:06:57,874:INFO:display_container: 9
2021-07-16 06:06:57,875: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:06:57,875:INFO:compare_models() succesfully completed......................................
2021-07-16 06:09:02,630:INFO:Initializing compare_models()
2021-07-16 06:09:02,631:INFO:compare_models(include=[Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 'lr', 'lightgbm'], fold=None, round=4, cross_validation=True, sort=Accuracy, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-07-16 06:09:02,631:INFO:Checking exceptions
2021-07-16 06:09:02,632:INFO:Preparing display monitor
2021-07-16 06:09:02,633:INFO:Preparing display monitor
2021-07-16 06:09:02,661:INFO:Initializing custom model NeuralNetClassifier
2021-07-16 06:09:02,661:INFO:Total runtime is 1.1678536732991537e-05 minutes
2021-07-16 06:09:02,670:INFO:SubProcess create_model() called ==================================
2021-07-16 06:09:02,672:INFO:Initializing create_model()
2021-07-16 06:09:02,672:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3fbe5ae90>, kwargs={})
2021-07-16 06:09:02,672:INFO:Checking exceptions
2021-07-16 06:09:02,672:INFO:Importing libraries
2021-07-16 06:09:02,673:INFO:Copying training dataset
2021-07-16 06:09:02,673:INFO:Defining folds
2021-07-16 06:09:02,674:INFO:Declaring metric variables
2021-07-16 06:09:02,680:INFO:Importing untrained model
2021-07-16 06:09:02,681:INFO:Declaring custom model
2021-07-16 06:09:02,690:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 06:09:02,707:INFO:Starting cross validation
2021-07-16 06:09:02,708:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:09:05,339:INFO:Calculating mean and std
2021-07-16 06:09:05,340:INFO:Creating metrics dataframe
2021-07-16 06:09:05,344:INFO:Uploading results into container
2021-07-16 06:09:05,344:INFO:Uploading model into container now
2021-07-16 06:09:05,345:INFO:create_model_container: 27
2021-07-16 06:09:05,345:INFO:master_model_container: 27
2021-07-16 06:09:05,345:INFO:display_container: 10
2021-07-16 06:09:05,346:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False)
2021-07-16 06:09:05,346:INFO:create_model() succesfully completed......................................
2021-07-16 06:09:05,495:INFO:SubProcess create_model() end ==================================
2021-07-16 06:09:05,496:INFO:Creating metrics dataframe
2021-07-16 06:09:05,514:INFO:Initializing Logistic Regression
2021-07-16 06:09:05,514:INFO:Total runtime is 0.04756104548772176 minutes
2021-07-16 06:09:05,525:INFO:SubProcess create_model() called ==================================
2021-07-16 06:09:05,525:INFO:Initializing create_model()
2021-07-16 06:09:05,525:INFO:create_model(estimator=lr, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3fbe5ae90>, kwargs={})
2021-07-16 06:09:05,526:INFO:Checking exceptions
2021-07-16 06:09:05,526:INFO:Importing libraries
2021-07-16 06:09:05,526:INFO:Copying training dataset
2021-07-16 06:09:05,527:INFO:Defining folds
2021-07-16 06:09:05,527:INFO:Declaring metric variables
2021-07-16 06:09:05,537:INFO:Importing untrained model
2021-07-16 06:09:05,546:INFO:Logistic Regression Imported succesfully
2021-07-16 06:09:05,566:INFO:Starting cross validation
2021-07-16 06:09:05,567:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:09:05,723:INFO:Calculating mean and std
2021-07-16 06:09:05,724:INFO:Creating metrics dataframe
2021-07-16 06:09:05,728:INFO:Uploading results into container
2021-07-16 06:09:05,729:INFO:Uploading model into container now
2021-07-16 06:09:05,729:INFO:create_model_container: 28
2021-07-16 06:09:05,729:INFO:master_model_container: 28
2021-07-16 06:09:05,729:INFO:display_container: 10
2021-07-16 06:09:05,730: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)
2021-07-16 06:09:05,730:INFO:create_model() succesfully completed......................................
2021-07-16 06:09:05,856:INFO:SubProcess create_model() end ==================================
2021-07-16 06:09:05,856:INFO:Creating metrics dataframe
2021-07-16 06:09:05,876:INFO:Initializing Light Gradient Boosting Machine
2021-07-16 06:09:05,876:INFO:Total runtime is 0.05358969370524089 minutes
2021-07-16 06:09:05,887:INFO:SubProcess create_model() called ==================================
2021-07-16 06:09:05,887:INFO:Initializing create_model()
2021-07-16 06:09:05,888:INFO:create_model(estimator=lightgbm, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3fbe5ae90>, kwargs={})
2021-07-16 06:09:05,888:INFO:Checking exceptions
2021-07-16 06:09:05,888:INFO:Importing libraries
2021-07-16 06:09:05,888:INFO:Copying training dataset
2021-07-16 06:09:05,889:INFO:Defining folds
2021-07-16 06:09:05,890:INFO:Declaring metric variables
2021-07-16 06:09:05,898:INFO:Importing untrained model
2021-07-16 06:09:05,907:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 06:09:05,926:INFO:Starting cross validation
2021-07-16 06:09:05,926:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:09:06,611:INFO:Calculating mean and std
2021-07-16 06:09:06,613:INFO:Creating metrics dataframe
2021-07-16 06:09:06,625:INFO:Uploading results into container
2021-07-16 06:09:06,625:INFO:Uploading model into container now
2021-07-16 06:09:06,626:INFO:create_model_container: 29
2021-07-16 06:09:06,626:INFO:master_model_container: 29
2021-07-16 06:09:06,626:INFO:display_container: 10
2021-07-16 06:09:06,627: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:09:06,627:INFO:create_model() succesfully completed......................................
2021-07-16 06:09:06,745:INFO:SubProcess create_model() end ==================================
2021-07-16 06:09:06,746:INFO:Creating metrics dataframe
2021-07-16 06:09:06,788:INFO:Initializing create_model()
2021-07-16 06:09:06,788:INFO:create_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, display=None, kwargs={})
2021-07-16 06:09:06,788:INFO:Checking exceptions
2021-07-16 06:09:06,789:INFO:Importing libraries
2021-07-16 06:09:06,789:INFO:Copying training dataset
2021-07-16 06:09:06,790:INFO:Defining folds
2021-07-16 06:09:06,790:INFO:Declaring metric variables
2021-07-16 06:09:06,790:INFO:Importing untrained model
2021-07-16 06:09:06,790:INFO:Declaring custom model
2021-07-16 06:09:06,791:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 06:09:06,791:INFO:Cross validation set to False
2021-07-16 06:09:06,792:INFO:Fitting Model
2021-07-16 06:09:06,947: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:09:06,947:INFO:create_models() succesfully completed......................................
2021-07-16 06:09:07,055:INFO:Creating MLFlow logs
2021-07-16 06:09:07,104:INFO:Model: Light Gradient Boosting Machine
2021-07-16 06:09:07,328: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': True, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0}
2021-07-16 06:09:07,977:INFO:Initializing predict_model()
2021-07-16 06:09:07,977:INFO:predict_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-16 06:09:07,977:INFO:Checking exceptions
2021-07-16 06:09:07,977:INFO:Preloading libraries
2021-07-16 06:09:08,384:INFO:Creating MLFlow logs
2021-07-16 06:09:08,432:INFO:Model: Logistic Regression
2021-07-16 06:09:08,653: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}
2021-07-16 06:09:09,339:INFO:Creating MLFlow logs
2021-07-16 06:09:09,369:INFO:Model: NeuralNetClassifier
2021-07-16 06:09:09,531:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 10, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc3cc0c9950>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc34014d8d0>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbca8450>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbca8250>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc3fbed0410>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc3fbe1e5d0>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 06:09:10,452:INFO:create_model_container: 29
2021-07-16 06:09:10,452:INFO:master_model_container: 29
2021-07-16 06:09:10,452:INFO:display_container: 10
2021-07-16 06:09:10,453: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:09:10,454:INFO:compare_models() succesfully completed......................................
2021-07-16 06:10:41,456:INFO:Initializing tune_model()
2021-07-16 06:10:41,456:INFO:tune_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold={'lr': [0.01, 0.02, 0.05, 0.1], 'max_epochs': [50, 100, 200], 'module__num_units_d1': [50, 30, 20, 10]}, round=4, n_iter=10, custom_grid=None, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=False, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, display=None, kwargs={})
2021-07-16 06:10:41,457:INFO:Checking exceptions
2021-07-16 06:11:07,109:INFO:Initializing tune_model()
2021-07-16 06:11:07,110:INFO:tune_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=None, round=4, n_iter=10, custom_grid={'lr': [0.01, 0.02, 0.05, 0.1], 'max_epochs': [50, 100, 200], 'module__num_units_d1': [50, 30, 20, 10]}, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=False, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, display=None, kwargs={})
2021-07-16 06:11:07,110:INFO:Checking exceptions
2021-07-16 06:11:07,113:INFO:Preparing display monitor
2021-07-16 06:11:07,130:INFO:Copying training dataset
2021-07-16 06:11:07,133:INFO:Checking base model
2021-07-16 06:11:07,134:INFO:A custom model has been passed
2021-07-16 06:11:07,135:INFO:Base model : NeuralNetClassifier
2021-07-16 06:11:07,143:INFO:Declaring metric variables
2021-07-16 06:11:07,151:INFO:Defining Hyperparameters
2021-07-16 06:11:07,279:INFO:custom_grid: {'actual_estimator__lr': [0.01, 0.02, 0.05, 0.1], 'actual_estimator__max_epochs': [50, 100, 200], 'actual_estimator__module__num_units_d1': [50, 30, 20, 10]}
2021-07-16 06:11:07,280:INFO:Tuning with n_jobs=-1
2021-07-16 06:11:07,280:INFO:Initializing RandomizedSearchCV
2021-07-16 06:12:14,616:INFO:Initializing tune_model()
2021-07-16 06:12:14,618:INFO:tune_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=None, round=4, n_iter=10, custom_grid={'net__lr': [0.01, 0.02, 0.05, 0.1], 'net__max_epochs': [50, 100, 200], 'net__num_units_d1': [50, 30, 20, 10]}, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=False, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, display=None, kwargs={})
2021-07-16 06:12:14,618:INFO:Checking exceptions
2021-07-16 06:12:14,620:INFO:Preparing display monitor
2021-07-16 06:12:14,638:INFO:Copying training dataset
2021-07-16 06:12:14,641:INFO:Checking base model
2021-07-16 06:12:14,642:INFO:A custom model has been passed
2021-07-16 06:12:14,642:INFO:Base model : NeuralNetClassifier
2021-07-16 06:12:14,652:INFO:Declaring metric variables
2021-07-16 06:12:14,660:INFO:Defining Hyperparameters
2021-07-16 06:12:14,801:INFO:custom_grid: {'actual_estimator__net__lr': [0.01, 0.02, 0.05, 0.1], 'actual_estimator__net__max_epochs': [50, 100, 200], 'actual_estimator__net__num_units_d1': [50, 30, 20, 10]}
2021-07-16 06:12:14,802:INFO:Tuning with n_jobs=-1
2021-07-16 06:12:14,802:INFO:Initializing RandomizedSearchCV
2021-07-16 06:18:33,540:INFO:Initializing tune_model()
2021-07-16 06:18:33,540:INFO:tune_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=None, round=4, n_iter=10, custom_grid={'net__lr': [0.01, 0.02, 0.05, 0.1], 'net__max_epochs': [50, 100, 200], 'net__module__num_units_d1': [50, 30, 20, 10]}, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=False, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, display=None, kwargs={})
2021-07-16 06:18:33,541:INFO:Checking exceptions
2021-07-16 06:18:33,542:INFO:Preparing display monitor
2021-07-16 06:18:33,559:INFO:Copying training dataset
2021-07-16 06:18:33,561:INFO:Checking base model
2021-07-16 06:18:33,562:INFO:A custom model has been passed
2021-07-16 06:18:33,562:INFO:Base model : NeuralNetClassifier
2021-07-16 06:18:33,570:INFO:Declaring metric variables
2021-07-16 06:18:33,576:INFO:Defining Hyperparameters
2021-07-16 06:18:33,706:INFO:custom_grid: {'actual_estimator__net__lr': [0.01, 0.02, 0.05, 0.1], 'actual_estimator__net__max_epochs': [50, 100, 200], 'actual_estimator__net__module__num_units_d1': [50, 30, 20, 10]}
2021-07-16 06:18:33,707:INFO:Tuning with n_jobs=-1
2021-07-16 06:18:33,707:INFO:Initializing RandomizedSearchCV
2021-07-16 06:20:08,191:INFO:Initializing create_model()
2021-07-16 06:20:08,194:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-07-16 06:20:08,194:INFO:Checking exceptions
2021-07-16 06:20:08,195:INFO:Preparing display monitor
2021-07-16 06:20:08,217:INFO:Importing libraries
2021-07-16 06:20:08,218:INFO:Copying training dataset
2021-07-16 06:20:08,220:INFO:Defining folds
2021-07-16 06:20:08,220:INFO:Declaring metric variables
2021-07-16 06:20:08,231:INFO:Importing untrained model
2021-07-16 06:20:08,232:INFO:Declaring custom model
2021-07-16 06:20:08,242:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 06:20:08,260:INFO:Starting cross validation
2021-07-16 06:20:08,261:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:20:34,911:INFO:Calculating mean and std
2021-07-16 06:20:34,913:INFO:Creating metrics dataframe
2021-07-16 06:20:34,927:INFO:Finalizing model
2021-07-16 06:20:49,334:INFO:Creating MLFlow logs
2021-07-16 06:20:49,379:INFO:Model: NeuralNetClassifier
2021-07-16 06:20:49,609:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 100, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc3cc67c890>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc3fbe3aa50>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3cc354ed0>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3cc709f90>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc3cc709790>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc3cc709190>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 06:20:50,517:INFO:Initializing predict_model()
2021-07-16 06:20:50,518:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-16 06:20:50,518:INFO:Checking exceptions
2021-07-16 06:20:50,518:INFO:Preloading libraries
2021-07-16 06:20:50,527:WARNING:Couldn't create holdout prediction for model, exception below:
2021-07-16 06:20:50,528:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 10139, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 8559, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-07-16 06:20:50,852:INFO:Uploading results into container
2021-07-16 06:20:50,853:INFO:Uploading model into container now
2021-07-16 06:20:50,870:INFO:create_model_container: 30
2021-07-16 06:20:50,871:INFO:master_model_container: 30
2021-07-16 06:20:50,871:INFO:display_container: 11
2021-07-16 06:20:50,873:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-16 06:20:50,873:INFO:create_model() succesfully completed......................................
2021-07-16 06:20:58,139:INFO:Initializing compare_models()
2021-07-16 06:20:58,139:INFO:compare_models(include=[Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 'lr', 'lightgbm'], fold=None, round=4, cross_validation=True, sort=Accuracy, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-07-16 06:20:58,139:INFO:Checking exceptions
2021-07-16 06:20:58,140:INFO:Preparing display monitor
2021-07-16 06:20:58,141:INFO:Preparing display monitor
2021-07-16 06:20:58,166:INFO:Initializing custom model NeuralNetClassifier
2021-07-16 06:20:58,167:INFO:Total runtime is 1.3856093088785807e-05 minutes
2021-07-16 06:20:58,175:INFO:SubProcess create_model() called ==================================
2021-07-16 06:20:58,176:INFO:Initializing create_model()
2021-07-16 06:20:58,176:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3fbcd8fd0>, kwargs={})
2021-07-16 06:20:58,176:INFO:Checking exceptions
2021-07-16 06:20:58,176:INFO:Importing libraries
2021-07-16 06:20:58,177:INFO:Copying training dataset
2021-07-16 06:20:58,177:INFO:Defining folds
2021-07-16 06:20:58,178:INFO:Declaring metric variables
2021-07-16 06:20:58,185:INFO:Importing untrained model
2021-07-16 06:20:58,185:INFO:Declaring custom model
2021-07-16 06:20:58,193:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 06:20:58,210:INFO:Starting cross validation
2021-07-16 06:20:58,210:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:21:23,993:INFO:Calculating mean and std
2021-07-16 06:21:23,996:INFO:Creating metrics dataframe
2021-07-16 06:21:24,005:INFO:Uploading results into container
2021-07-16 06:21:24,006:INFO:Uploading model into container now
2021-07-16 06:21:24,006:INFO:create_model_container: 31
2021-07-16 06:21:24,006:INFO:master_model_container: 31
2021-07-16 06:21:24,007:INFO:display_container: 12
2021-07-16 06:21:24,008:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False)
2021-07-16 06:21:24,008:INFO:create_model() succesfully completed......................................
2021-07-16 06:21:24,133:INFO:SubProcess create_model() end ==================================
2021-07-16 06:21:24,134:INFO:Creating metrics dataframe
2021-07-16 06:21:24,151:INFO:Initializing Logistic Regression
2021-07-16 06:21:24,151:INFO:Total runtime is 0.4330942074457804 minutes
2021-07-16 06:21:24,161:INFO:SubProcess create_model() called ==================================
2021-07-16 06:21:24,162:INFO:Initializing create_model()
2021-07-16 06:21:24,162:INFO:create_model(estimator=lr, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3fbcd8fd0>, kwargs={})
2021-07-16 06:21:24,162:INFO:Checking exceptions
2021-07-16 06:21:24,162:INFO:Importing libraries
2021-07-16 06:21:24,163:INFO:Copying training dataset
2021-07-16 06:21:24,164:INFO:Defining folds
2021-07-16 06:21:24,164:INFO:Declaring metric variables
2021-07-16 06:21:24,173:INFO:Importing untrained model
2021-07-16 06:21:24,181:INFO:Logistic Regression Imported succesfully
2021-07-16 06:21:24,196:INFO:Starting cross validation
2021-07-16 06:21:24,197:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:21:24,330:INFO:Calculating mean and std
2021-07-16 06:21:24,331:INFO:Creating metrics dataframe
2021-07-16 06:21:24,337:INFO:Uploading results into container
2021-07-16 06:21:24,338:INFO:Uploading model into container now
2021-07-16 06:21:24,338:INFO:create_model_container: 32
2021-07-16 06:21:24,338:INFO:master_model_container: 32
2021-07-16 06:21:24,338:INFO:display_container: 12
2021-07-16 06:21:24,339: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)
2021-07-16 06:21:24,340:INFO:create_model() succesfully completed......................................
2021-07-16 06:21:24,463:INFO:SubProcess create_model() end ==================================
2021-07-16 06:21:24,464:INFO:Creating metrics dataframe
2021-07-16 06:21:24,481:INFO:Initializing Light Gradient Boosting Machine
2021-07-16 06:21:24,481:INFO:Total runtime is 0.4385887066523234 minutes
2021-07-16 06:21:24,492:INFO:SubProcess create_model() called ==================================
2021-07-16 06:21:24,493:INFO:Initializing create_model()
2021-07-16 06:21:24,493:INFO:create_model(estimator=lightgbm, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3fbcd8fd0>, kwargs={})
2021-07-16 06:21:24,493:INFO:Checking exceptions
2021-07-16 06:21:24,494:INFO:Importing libraries
2021-07-16 06:21:24,494:INFO:Copying training dataset
2021-07-16 06:21:24,495:INFO:Defining folds
2021-07-16 06:21:24,495:INFO:Declaring metric variables
2021-07-16 06:21:24,504:INFO:Importing untrained model
2021-07-16 06:21:24,512:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 06:21:24,530:INFO:Starting cross validation
2021-07-16 06:21:24,531:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:21:25,584:INFO:Calculating mean and std
2021-07-16 06:21:25,586:INFO:Creating metrics dataframe
2021-07-16 06:21:25,591:INFO:Uploading results into container
2021-07-16 06:21:25,591:INFO:Uploading model into container now
2021-07-16 06:21:25,592:INFO:create_model_container: 33
2021-07-16 06:21:25,592:INFO:master_model_container: 33
2021-07-16 06:21:25,592:INFO:display_container: 12
2021-07-16 06:21:25,593: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:21:25,593:INFO:create_model() succesfully completed......................................
2021-07-16 06:21:25,706:INFO:SubProcess create_model() end ==================================
2021-07-16 06:21:25,707:INFO:Creating metrics dataframe
2021-07-16 06:21:25,749:INFO:Initializing create_model()
2021-07-16 06:21:25,749:INFO:create_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, display=None, kwargs={})
2021-07-16 06:21:25,749:INFO:Checking exceptions
2021-07-16 06:21:25,750:INFO:Importing libraries
2021-07-16 06:21:25,750:INFO:Copying training dataset
2021-07-16 06:21:25,751:INFO:Defining folds
2021-07-16 06:21:25,751:INFO:Declaring metric variables
2021-07-16 06:21:25,752:INFO:Importing untrained model
2021-07-16 06:21:25,752:INFO:Declaring custom model
2021-07-16 06:21:25,753:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 06:21:25,753:INFO:Cross validation set to False
2021-07-16 06:21:25,754:INFO:Fitting Model
2021-07-16 06:21:25,991: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:21:25,991:INFO:create_models() succesfully completed......................................
2021-07-16 06:21:26,104:INFO:Creating MLFlow logs
2021-07-16 06:21:26,152:INFO:Model: Light Gradient Boosting Machine
2021-07-16 06:21:26,378: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': True, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0}
2021-07-16 06:21:27,002:INFO:Initializing predict_model()
2021-07-16 06:21:27,003:INFO:predict_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-16 06:21:27,003:INFO:Checking exceptions
2021-07-16 06:21:27,003:INFO:Preloading libraries
2021-07-16 06:21:27,408:INFO:Creating MLFlow logs
2021-07-16 06:21:27,454:INFO:Model: NeuralNetClassifier
2021-07-16 06:21:27,676:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 100, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc3cc349e10>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc3fba88650>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3cc0c9e50>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbcd07d0>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc3fbcd0610>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc3fbcd05d0>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 06:21:28,738:INFO:Creating MLFlow logs
2021-07-16 06:21:28,769:INFO:Model: Logistic Regression
2021-07-16 06:21:28,942: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}
2021-07-16 06:21:29,647:INFO:create_model_container: 33
2021-07-16 06:21:29,647:INFO:master_model_container: 33
2021-07-16 06:21:29,648:INFO:display_container: 12
2021-07-16 06:21:29,649: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:21:29,649:INFO:compare_models() succesfully completed......................................
2021-07-16 06:24:57,625:INFO:Initializing tune_model()
2021-07-16 06:24:57,625:INFO:tune_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=None, round=4, n_iter=10, custom_grid={'net__lr': [0.01, 0.02, 0.05, 0.1], 'net__max_epochs': [50, 100, 200], 'net__module__num_units_d1': [50, 30, 20, 10]}, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=False, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, display=None, kwargs={})
2021-07-16 06:24:57,626:INFO:Checking exceptions
2021-07-16 06:24:57,627:INFO:Preparing display monitor
2021-07-16 06:24:57,645:INFO:Copying training dataset
2021-07-16 06:24:57,647:INFO:Checking base model
2021-07-16 06:24:57,648:INFO:A custom model has been passed
2021-07-16 06:24:57,649:INFO:Base model : NeuralNetClassifier
2021-07-16 06:24:57,659:INFO:Declaring metric variables
2021-07-16 06:24:57,669:INFO:Defining Hyperparameters
2021-07-16 06:24:57,826:INFO:custom_grid: {'actual_estimator__net__lr': [0.01, 0.02, 0.05, 0.1], 'actual_estimator__net__max_epochs': [50, 100, 200], 'actual_estimator__net__module__num_units_d1': [50, 30, 20, 10]}
2021-07-16 06:24:57,827:INFO:Tuning with n_jobs=-1
2021-07-16 06:24:57,827:INFO:Initializing RandomizedSearchCV
2021-07-16 06:27:24,146:INFO:Initializing tune_model()
2021-07-16 06:27:24,147:INFO:tune_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=None, round=4, n_iter=10, custom_grid={'net__lr': [0.01, 0.02, 0.05, 0.1], 'net__max_epochs': [50, 100, 200], 'net__module__num_units_d1': [50, 30, 20, 10]}, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=False, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, display=None, kwargs={})
2021-07-16 06:27:24,147:INFO:Checking exceptions
2021-07-16 06:27:24,150:INFO:Preparing display monitor
2021-07-16 06:27:24,170:INFO:Copying training dataset
2021-07-16 06:27:24,171:INFO:Checking base model
2021-07-16 06:27:24,171:INFO:A custom model has been passed
2021-07-16 06:27:24,172:INFO:Base model : NeuralNetClassifier
2021-07-16 06:27:24,180:INFO:Declaring metric variables
2021-07-16 06:27:24,190:INFO:Defining Hyperparameters
2021-07-16 06:27:24,368:INFO:custom_grid: {'actual_estimator__net__lr': [0.01, 0.02, 0.05, 0.1], 'actual_estimator__net__max_epochs': [50, 100, 200], 'actual_estimator__net__module__num_units_d1': [50, 30, 20, 10]}
2021-07-16 06:27:24,369:INFO:Tuning with n_jobs=-1
2021-07-16 06:27:24,369:INFO:Initializing RandomizedSearchCV
2021-07-16 06:32:45,754:INFO:best_params: {'actual_estimator__net__module__num_units_d1': 20, 'actual_estimator__net__max_epochs': 50, 'actual_estimator__net__lr': 0.01}
2021-07-16 06:32:45,755:INFO:Hyperparameter search completed
2021-07-16 06:32:45,756:INFO:SubProcess create_model() called ==================================
2021-07-16 06:32:45,758:INFO:Initializing create_model()
2021-07-16 06:32:45,758:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, verbose=True, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3cc51f5d0>, kwargs={'net__module__num_units_d1': 20, 'net__max_epochs': 50, 'net__lr': 0.01})
2021-07-16 06:32:45,759:INFO:Checking exceptions
2021-07-16 06:32:45,759:INFO:Importing libraries
2021-07-16 06:32:45,759:INFO:Copying training dataset
2021-07-16 06:32:45,761:INFO:Defining folds
2021-07-16 06:32:45,762:INFO:Declaring metric variables
2021-07-16 06:32:45,772:INFO:Importing untrained model
2021-07-16 06:32:45,773:INFO:Declaring custom model
2021-07-16 06:32:45,783:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 06:32:45,798:INFO:Starting cross validation
2021-07-16 06:32:45,799:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:32:57,725:INFO:Calculating mean and std
2021-07-16 06:32:57,726:INFO:Creating metrics dataframe
2021-07-16 06:32:57,739:INFO:Finalizing model
2021-07-16 06:33:04,725:INFO:Uploading results into container
2021-07-16 06:33:04,726:INFO:Uploading model into container now
2021-07-16 06:33:04,726:INFO:create_model_container: 34
2021-07-16 06:33:04,727:INFO:master_model_container: 34
2021-07-16 06:33:04,727:INFO:display_container: 13
2021-07-16 06:33:04,730:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=20, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=20, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-16 06:33:04,730:INFO:create_model() succesfully completed......................................
2021-07-16 06:33:04,863:INFO:SubProcess create_model() end ==================================
2021-07-16 06:33:04,864:INFO:Creating MLFlow logs
2021-07-16 06:33:04,912:INFO:Model: NeuralNetClassifier
2021-07-16 06:33:05,149:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.01, 'max_epochs': 50, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc3cc0c2090>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'module__num_units_d1': 20, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc3cc5a49d0>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbcacf90>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3cc6ac950>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc3cc6b1990>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc3fbcd8ed0>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 06:33:06,073:INFO:Initializing predict_model()
2021-07-16 06:33:06,074:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=20, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=20, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-16 06:33:06,074:INFO:Checking exceptions
2021-07-16 06:33:06,074:INFO:Preloading libraries
2021-07-16 06:33:06,084:WARNING:Couldn't create holdout prediction for model, exception below:
2021-07-16 06:33:06,085:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 10139, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 8559, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-07-16 06:33:06,412:INFO:create_model_container: 34
2021-07-16 06:33:06,412:INFO:master_model_container: 34
2021-07-16 06:33:06,413:INFO:display_container: 13
2021-07-16 06:33:06,415:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=20, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=20, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-16 06:33:06,415:INFO:tune_model() succesfully completed......................................
2021-07-16 06:33:51,893:INFO:Initializing compare_models()
2021-07-16 06:33:51,893:INFO:compare_models(include=[Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=20, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=20, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 'lr', 'lightgbm'], fold=None, round=4, cross_validation=True, sort=Accuracy, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-07-16 06:33:51,894:INFO:Checking exceptions
2021-07-16 06:33:51,898:INFO:Preparing display monitor
2021-07-16 06:33:51,899:INFO:Preparing display monitor
2021-07-16 06:33:51,925:INFO:Initializing custom model NeuralNetClassifier
2021-07-16 06:33:51,925:INFO:Total runtime is 1.2441476186116536e-05 minutes
2021-07-16 06:33:51,934:INFO:SubProcess create_model() called ==================================
2021-07-16 06:33:51,936:INFO:Initializing create_model()
2021-07-16 06:33:51,936:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=20, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=20, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3cc73e350>, kwargs={})
2021-07-16 06:33:51,936:INFO:Checking exceptions
2021-07-16 06:33:51,936:INFO:Importing libraries
2021-07-16 06:33:51,937:INFO:Copying training dataset
2021-07-16 06:33:51,938:INFO:Defining folds
2021-07-16 06:33:51,938:INFO:Declaring metric variables
2021-07-16 06:33:51,946:INFO:Importing untrained model
2021-07-16 06:33:51,947:INFO:Declaring custom model
2021-07-16 06:33:51,957:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 06:33:51,975:INFO:Starting cross validation
2021-07-16 06:33:51,976:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:34:03,601:INFO:Calculating mean and std
2021-07-16 06:34:03,603:INFO:Creating metrics dataframe
2021-07-16 06:34:03,608:INFO:Uploading results into container
2021-07-16 06:34:03,608:INFO:Uploading model into container now
2021-07-16 06:34:03,609:INFO:create_model_container: 35
2021-07-16 06:34:03,609:INFO:master_model_container: 35
2021-07-16 06:34:03,609:INFO:display_container: 14
2021-07-16 06:34:03,610:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
  module__num_units_d1=20,
))],
         verbose=False)
2021-07-16 06:34:03,610:INFO:create_model() succesfully completed......................................
2021-07-16 06:34:03,733:INFO:SubProcess create_model() end ==================================
2021-07-16 06:34:03,734:INFO:Creating metrics dataframe
2021-07-16 06:34:03,752:INFO:Initializing custom model NeuralNetClassifier
2021-07-16 06:34:03,752:INFO:Total runtime is 0.19713394244511923 minutes
2021-07-16 06:34:03,763:INFO:SubProcess create_model() called ==================================
2021-07-16 06:34:03,765:INFO:Initializing create_model()
2021-07-16 06:34:03,765:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3cc73e350>, kwargs={})
2021-07-16 06:34:03,765:INFO:Checking exceptions
2021-07-16 06:34:03,765:INFO:Importing libraries
2021-07-16 06:34:03,766:INFO:Copying training dataset
2021-07-16 06:34:03,767:INFO:Defining folds
2021-07-16 06:34:03,767:INFO:Declaring metric variables
2021-07-16 06:34:03,776:INFO:Importing untrained model
2021-07-16 06:34:03,776:INFO:Declaring custom model
2021-07-16 06:34:03,788:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 06:34:03,804:INFO:Starting cross validation
2021-07-16 06:34:03,805:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:34:32,264:INFO:Calculating mean and std
2021-07-16 06:34:32,265:INFO:Creating metrics dataframe
2021-07-16 06:34:32,270:INFO:Uploading results into container
2021-07-16 06:34:32,270:INFO:Uploading model into container now
2021-07-16 06:34:32,271:INFO:create_model_container: 36
2021-07-16 06:34:32,271:INFO:master_model_container: 36
2021-07-16 06:34:32,271:INFO:display_container: 14
2021-07-16 06:34:32,273:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False)
2021-07-16 06:34:32,273:INFO:create_model() succesfully completed......................................
2021-07-16 06:34:32,391:INFO:SubProcess create_model() end ==================================
2021-07-16 06:34:32,392:INFO:Creating metrics dataframe
2021-07-16 06:34:32,411:INFO:Initializing Logistic Regression
2021-07-16 06:34:32,412:INFO:Total runtime is 0.6747937560081482 minutes
2021-07-16 06:34:32,422:INFO:SubProcess create_model() called ==================================
2021-07-16 06:34:32,423:INFO:Initializing create_model()
2021-07-16 06:34:32,423:INFO:create_model(estimator=lr, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3cc73e350>, kwargs={})
2021-07-16 06:34:32,423:INFO:Checking exceptions
2021-07-16 06:34:32,424:INFO:Importing libraries
2021-07-16 06:34:32,424:INFO:Copying training dataset
2021-07-16 06:34:32,425:INFO:Defining folds
2021-07-16 06:34:32,425:INFO:Declaring metric variables
2021-07-16 06:34:32,434:INFO:Importing untrained model
2021-07-16 06:34:32,443:INFO:Logistic Regression Imported succesfully
2021-07-16 06:34:32,462:INFO:Starting cross validation
2021-07-16 06:34:32,463:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:34:32,597:INFO:Calculating mean and std
2021-07-16 06:34:32,598:INFO:Creating metrics dataframe
2021-07-16 06:34:32,602:INFO:Uploading results into container
2021-07-16 06:34:32,602:INFO:Uploading model into container now
2021-07-16 06:34:32,602:INFO:create_model_container: 37
2021-07-16 06:34:32,603:INFO:master_model_container: 37
2021-07-16 06:34:32,603:INFO:display_container: 14
2021-07-16 06:34:32,604: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)
2021-07-16 06:34:32,604:INFO:create_model() succesfully completed......................................
2021-07-16 06:34:32,729:INFO:SubProcess create_model() end ==================================
2021-07-16 06:34:32,730:INFO:Creating metrics dataframe
2021-07-16 06:34:32,749:INFO:Initializing Light Gradient Boosting Machine
2021-07-16 06:34:32,749:INFO:Total runtime is 0.6804198424021403 minutes
2021-07-16 06:34:32,758:INFO:SubProcess create_model() called ==================================
2021-07-16 06:34:32,759:INFO:Initializing create_model()
2021-07-16 06:34:32,759:INFO:create_model(estimator=lightgbm, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3cc73e350>, kwargs={})
2021-07-16 06:34:32,759:INFO:Checking exceptions
2021-07-16 06:34:32,759:INFO:Importing libraries
2021-07-16 06:34:32,760:INFO:Copying training dataset
2021-07-16 06:34:32,760:INFO:Defining folds
2021-07-16 06:34:32,761:INFO:Declaring metric variables
2021-07-16 06:34:32,769:INFO:Importing untrained model
2021-07-16 06:34:32,777:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 06:34:32,794:INFO:Starting cross validation
2021-07-16 06:34:32,795:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:34:33,954:INFO:Calculating mean and std
2021-07-16 06:34:33,955:INFO:Creating metrics dataframe
2021-07-16 06:34:33,959:INFO:Uploading results into container
2021-07-16 06:34:33,960:INFO:Uploading model into container now
2021-07-16 06:34:33,960:INFO:create_model_container: 38
2021-07-16 06:34:33,961:INFO:master_model_container: 38
2021-07-16 06:34:33,961:INFO:display_container: 14
2021-07-16 06:34:33,962: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:34:33,962:INFO:create_model() succesfully completed......................................
2021-07-16 06:34:34,082:INFO:SubProcess create_model() end ==================================
2021-07-16 06:34:34,082:INFO:Creating metrics dataframe
2021-07-16 06:34:34,122:INFO:Initializing create_model()
2021-07-16 06:34:34,123:INFO:create_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, display=None, kwargs={})
2021-07-16 06:34:34,123:INFO:Checking exceptions
2021-07-16 06:34:34,123:INFO:Importing libraries
2021-07-16 06:34:34,123:INFO:Copying training dataset
2021-07-16 06:34:34,124:INFO:Defining folds
2021-07-16 06:34:34,124:INFO:Declaring metric variables
2021-07-16 06:34:34,124:INFO:Importing untrained model
2021-07-16 06:34:34,125:INFO:Declaring custom model
2021-07-16 06:34:34,125:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 06:34:34,126:INFO:Cross validation set to False
2021-07-16 06:34:34,126:INFO:Fitting Model
2021-07-16 06:34:34,313: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:34:34,313:INFO:create_models() succesfully completed......................................
2021-07-16 06:34:34,426:INFO:Creating MLFlow logs
2021-07-16 06:34:34,476:INFO:Model: Light Gradient Boosting Machine
2021-07-16 06:34:34,699: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': True, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0}
2021-07-16 06:34:35,363:INFO:Initializing predict_model()
2021-07-16 06:34:35,363:INFO:predict_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-16 06:34:35,363:INFO:Checking exceptions
2021-07-16 06:34:35,364:INFO:Preloading libraries
2021-07-16 06:34:35,796:INFO:Creating MLFlow logs
2021-07-16 06:34:35,843:INFO:Model: NeuralNetClassifier
2021-07-16 06:34:36,073:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 100, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc3fbae36d0>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc3cc790910>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3cc685210>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3cc68af10>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc3cc68ab10>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc3cc68aa90>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 06:34:37,002:INFO:Creating MLFlow logs
2021-07-16 06:34:37,033:INFO:Model: Logistic Regression
2021-07-16 06:34:37,197: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}
2021-07-16 06:34:37,887:INFO:Creating MLFlow logs
2021-07-16 06:34:37,921:INFO:Model: NeuralNetClassifier
2021-07-16 06:34:38,081:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.01, 'max_epochs': 50, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc3cc50fc50>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'module__num_units_d1': 20, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc3cc78b190>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3cc349510>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3cc349810>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc3fbaf7910>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc3cc6ac050>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 06:34:39,046:INFO:create_model_container: 38
2021-07-16 06:34:39,047:INFO:master_model_container: 38
2021-07-16 06:34:39,047:INFO:display_container: 14
2021-07-16 06:34:39,048: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:34:39,048:INFO:compare_models() succesfully completed......................................
2021-07-16 06:35:43,238:INFO:Initializing tune_model()
2021-07-16 06:35:43,239:INFO:tune_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=None, round=4, n_iter=10, custom_grid={'net__lr': [0.01, 0.02, 0.05, 0.1], 'net__max_epochs': [50, 100, 200], 'net__module__num_units_d1': [10, 20, 50, 100, 200, 300], 'net__module__num_units_d2': [200, 100, 50, 30, 20, 10]}, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=False, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, display=None, kwargs={})
2021-07-16 06:35:43,240:INFO:Checking exceptions
2021-07-16 06:35:43,241:INFO:Preparing display monitor
2021-07-16 06:35:43,260:INFO:Copying training dataset
2021-07-16 06:35:43,262:INFO:Checking base model
2021-07-16 06:35:43,263:INFO:A custom model has been passed
2021-07-16 06:35:43,264:INFO:Base model : NeuralNetClassifier
2021-07-16 06:35:43,274:INFO:Declaring metric variables
2021-07-16 06:35:43,284:INFO:Defining Hyperparameters
2021-07-16 06:35:43,444:INFO:custom_grid: {'actual_estimator__net__lr': [0.01, 0.02, 0.05, 0.1], 'actual_estimator__net__max_epochs': [50, 100, 200], 'actual_estimator__net__module__num_units_d1': [10, 20, 50, 100, 200, 300], 'actual_estimator__net__module__num_units_d2': [200, 100, 50, 30, 20, 10]}
2021-07-16 06:35:43,444:INFO:Tuning with n_jobs=-1
2021-07-16 06:35:43,445:INFO:Initializing RandomizedSearchCV
2021-07-16 06:40:53,949:INFO:best_params: {'actual_estimator__net__module__num_units_d2': 20, 'actual_estimator__net__module__num_units_d1': 300, 'actual_estimator__net__max_epochs': 200, 'actual_estimator__net__lr': 0.05}
2021-07-16 06:40:53,950:INFO:Hyperparameter search completed
2021-07-16 06:40:53,951:INFO:SubProcess create_model() called ==================================
2021-07-16 06:40:53,952:INFO:Initializing create_model()
2021-07-16 06:40:53,952:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, verbose=True, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3cc354d50>, kwargs={'net__module__num_units_d2': 20, 'net__module__num_units_d1': 300, 'net__max_epochs': 200, 'net__lr': 0.05})
2021-07-16 06:40:53,953:INFO:Checking exceptions
2021-07-16 06:40:53,953:INFO:Importing libraries
2021-07-16 06:40:53,954:INFO:Copying training dataset
2021-07-16 06:40:53,955:INFO:Defining folds
2021-07-16 06:40:53,955:INFO:Declaring metric variables
2021-07-16 06:40:53,965:INFO:Importing untrained model
2021-07-16 06:40:53,966:INFO:Declaring custom model
2021-07-16 06:40:53,977:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 06:40:53,996:INFO:Starting cross validation
2021-07-16 06:40:53,996:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:41:49,297:INFO:Calculating mean and std
2021-07-16 06:41:49,298:INFO:Creating metrics dataframe
2021-07-16 06:41:49,309:INFO:Finalizing model
2021-07-16 06:42:18,824:INFO:Uploading results into container
2021-07-16 06:42:18,825:INFO:Uploading model into container now
2021-07-16 06:42:18,825:INFO:create_model_container: 39
2021-07-16 06:42:18,826:INFO:master_model_container: 39
2021-07-16 06:42:18,826:INFO:display_container: 15
2021-07-16 06:42:18,830:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=300, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=300, out_features=20, bias=True)
    (output): Linear(in_features=20, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-16 06:42:18,831:INFO:create_model() succesfully completed......................................
2021-07-16 06:42:18,943:INFO:SubProcess create_model() end ==================================
2021-07-16 06:42:18,944:INFO:Creating MLFlow logs
2021-07-16 06:42:18,994:INFO:Model: NeuralNetClassifier
2021-07-16 06:42:19,218:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.05, 'max_epochs': 200, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc3cc71b490>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'module__num_units_d2': 20, 'module__num_units_d1': 300, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc3cc79f250>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3cc526d10>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3cc6f5650>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc3cc51f650>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc3cc719d10>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 06:42:20,142:INFO:Initializing predict_model()
2021-07-16 06:42:20,142:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=300, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=300, out_features=20, bias=True)
    (output): Linear(in_features=20, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-16 06:42:20,142:INFO:Checking exceptions
2021-07-16 06:42:20,143:INFO:Preloading libraries
2021-07-16 06:42:20,153:WARNING:Couldn't create holdout prediction for model, exception below:
2021-07-16 06:42:20,154:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 10139, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 8559, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-07-16 06:42:20,544:INFO:create_model_container: 39
2021-07-16 06:42:20,545:INFO:master_model_container: 39
2021-07-16 06:42:20,545:INFO:display_container: 15
2021-07-16 06:42:20,547:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=300, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=300, out_features=20, bias=True)
    (output): Linear(in_features=20, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-16 06:42:20,547:INFO:tune_model() succesfully completed......................................
2021-07-16 06:42:20,975:INFO:Initializing compare_models()
2021-07-16 06:42:20,975:INFO:compare_models(include=[Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=300, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=300, out_features=20, bias=True)
    (output): Linear(in_features=20, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 'lr', 'lightgbm'], fold=None, round=4, cross_validation=True, sort=Accuracy, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-07-16 06:42:20,976:INFO:Checking exceptions
2021-07-16 06:42:20,977:INFO:Preparing display monitor
2021-07-16 06:42:20,977:INFO:Preparing display monitor
2021-07-16 06:42:21,006:INFO:Initializing custom model NeuralNetClassifier
2021-07-16 06:42:21,006:INFO:Total runtime is 1.0927518208821615e-05 minutes
2021-07-16 06:42:21,018:INFO:SubProcess create_model() called ==================================
2021-07-16 06:42:21,020:INFO:Initializing create_model()
2021-07-16 06:42:21,020:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=300, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=300, out_features=20, bias=True)
    (output): Linear(in_features=20, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3cc6f5850>, kwargs={})
2021-07-16 06:42:21,020:INFO:Checking exceptions
2021-07-16 06:42:21,020:INFO:Importing libraries
2021-07-16 06:42:21,021:INFO:Copying training dataset
2021-07-16 06:42:21,022:INFO:Defining folds
2021-07-16 06:42:21,022:INFO:Declaring metric variables
2021-07-16 06:42:21,033:INFO:Importing untrained model
2021-07-16 06:42:21,033:INFO:Declaring custom model
2021-07-16 06:42:21,043:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 06:42:21,066:INFO:Starting cross validation
2021-07-16 06:42:21,067:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:43:18,276:INFO:Calculating mean and std
2021-07-16 06:43:18,277:INFO:Creating metrics dataframe
2021-07-16 06:43:18,283:INFO:Uploading results into container
2021-07-16 06:43:18,283:INFO:Uploading model into container now
2021-07-16 06:43:18,283:INFO:create_model_container: 40
2021-07-16 06:43:18,284:INFO:master_model_container: 40
2021-07-16 06:43:18,284:INFO:display_container: 16
2021-07-16 06:43:18,285:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
  module__num_units_d1=300,
  module__num_units_d2=20,
))],
         verbose=False)
2021-07-16 06:43:18,285:INFO:create_model() succesfully completed......................................
2021-07-16 06:43:18,400:INFO:SubProcess create_model() end ==================================
2021-07-16 06:43:18,400:INFO:Creating metrics dataframe
2021-07-16 06:43:18,418:INFO:Initializing custom model NeuralNetClassifier
2021-07-16 06:43:18,419:INFO:Total runtime is 0.9568873008092245 minutes
2021-07-16 06:43:18,429:INFO:SubProcess create_model() called ==================================
2021-07-16 06:43:18,431:INFO:Initializing create_model()
2021-07-16 06:43:18,432:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3cc6f5850>, kwargs={})
2021-07-16 06:43:18,432:INFO:Checking exceptions
2021-07-16 06:43:18,432:INFO:Importing libraries
2021-07-16 06:43:18,432:INFO:Copying training dataset
2021-07-16 06:43:18,433:INFO:Defining folds
2021-07-16 06:43:18,434:INFO:Declaring metric variables
2021-07-16 06:43:18,442:INFO:Importing untrained model
2021-07-16 06:43:18,442:INFO:Declaring custom model
2021-07-16 06:43:18,452:INFO:NeuralNetClassifier Imported succesfully
2021-07-16 06:43:18,469:INFO:Starting cross validation
2021-07-16 06:43:18,470:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:43:46,547:INFO:Calculating mean and std
2021-07-16 06:43:46,549:INFO:Creating metrics dataframe
2021-07-16 06:43:46,553:INFO:Uploading results into container
2021-07-16 06:43:46,554:INFO:Uploading model into container now
2021-07-16 06:43:46,554:INFO:create_model_container: 41
2021-07-16 06:43:46,554:INFO:master_model_container: 41
2021-07-16 06:43:46,555:INFO:display_container: 16
2021-07-16 06:43:46,556:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False)
2021-07-16 06:43:46,556:INFO:create_model() succesfully completed......................................
2021-07-16 06:43:46,669:INFO:SubProcess create_model() end ==================================
2021-07-16 06:43:46,669:INFO:Creating metrics dataframe
2021-07-16 06:43:46,691:INFO:Initializing Logistic Regression
2021-07-16 06:43:46,691:INFO:Total runtime is 1.4280934651692707 minutes
2021-07-16 06:43:46,701:INFO:SubProcess create_model() called ==================================
2021-07-16 06:43:46,702:INFO:Initializing create_model()
2021-07-16 06:43:46,702:INFO:create_model(estimator=lr, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3cc6f5850>, kwargs={})
2021-07-16 06:43:46,702:INFO:Checking exceptions
2021-07-16 06:43:46,703:INFO:Importing libraries
2021-07-16 06:43:46,703:INFO:Copying training dataset
2021-07-16 06:43:46,704:INFO:Defining folds
2021-07-16 06:43:46,704:INFO:Declaring metric variables
2021-07-16 06:43:46,712:INFO:Importing untrained model
2021-07-16 06:43:46,720:INFO:Logistic Regression Imported succesfully
2021-07-16 06:43:46,737:INFO:Starting cross validation
2021-07-16 06:43:46,738:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:43:46,869:INFO:Calculating mean and std
2021-07-16 06:43:46,870:INFO:Creating metrics dataframe
2021-07-16 06:43:46,875:INFO:Uploading results into container
2021-07-16 06:43:46,875:INFO:Uploading model into container now
2021-07-16 06:43:46,875:INFO:create_model_container: 42
2021-07-16 06:43:46,875:INFO:master_model_container: 42
2021-07-16 06:43:46,876:INFO:display_container: 16
2021-07-16 06:43:46,876: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)
2021-07-16 06:43:46,876:INFO:create_model() succesfully completed......................................
2021-07-16 06:43:46,997:INFO:SubProcess create_model() end ==================================
2021-07-16 06:43:46,998:INFO:Creating metrics dataframe
2021-07-16 06:43:47,015:INFO:Initializing Light Gradient Boosting Machine
2021-07-16 06:43:47,015:INFO:Total runtime is 1.4334993322690326 minutes
2021-07-16 06:43:47,024:INFO:SubProcess create_model() called ==================================
2021-07-16 06:43:47,024:INFO:Initializing create_model()
2021-07-16 06:43:47,025:INFO:create_model(estimator=lightgbm, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7fc3cc6f5850>, kwargs={})
2021-07-16 06:43:47,025:INFO:Checking exceptions
2021-07-16 06:43:47,025:INFO:Importing libraries
2021-07-16 06:43:47,025:INFO:Copying training dataset
2021-07-16 06:43:47,026:INFO:Defining folds
2021-07-16 06:43:47,026:INFO:Declaring metric variables
2021-07-16 06:43:47,034:INFO:Importing untrained model
2021-07-16 06:43:47,043:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 06:43:47,061:INFO:Starting cross validation
2021-07-16 06:43:47,062:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-16 06:43:47,811:INFO:Calculating mean and std
2021-07-16 06:43:47,812:INFO:Creating metrics dataframe
2021-07-16 06:43:47,817:INFO:Uploading results into container
2021-07-16 06:43:47,817:INFO:Uploading model into container now
2021-07-16 06:43:47,817:INFO:create_model_container: 43
2021-07-16 06:43:47,818:INFO:master_model_container: 43
2021-07-16 06:43:47,818:INFO:display_container: 16
2021-07-16 06:43:47,819: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:43:47,819:INFO:create_model() succesfully completed......................................
2021-07-16 06:43:47,935:INFO:SubProcess create_model() end ==================================
2021-07-16 06:43:47,935:INFO:Creating metrics dataframe
2021-07-16 06:43:47,977:INFO:Initializing create_model()
2021-07-16 06:43:47,978:INFO:create_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, display=None, kwargs={})
2021-07-16 06:43:47,978:INFO:Checking exceptions
2021-07-16 06:43:47,979:INFO:Importing libraries
2021-07-16 06:43:47,979:INFO:Copying training dataset
2021-07-16 06:43:47,979:INFO:Defining folds
2021-07-16 06:43:47,980:INFO:Declaring metric variables
2021-07-16 06:43:47,980:INFO:Importing untrained model
2021-07-16 06:43:47,980:INFO:Declaring custom model
2021-07-16 06:43:47,981:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-16 06:43:47,981:INFO:Cross validation set to False
2021-07-16 06:43:47,982:INFO:Fitting Model
2021-07-16 06:43:48,176: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:43:48,176:INFO:create_models() succesfully completed......................................
2021-07-16 06:43:48,288:INFO:Creating MLFlow logs
2021-07-16 06:43:48,338:INFO:Model: Light Gradient Boosting Machine
2021-07-16 06:43:48,560: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': True, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0}
2021-07-16 06:43:49,209:INFO:Initializing predict_model()
2021-07-16 06:43:49,209:INFO:predict_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-16 06:43:49,210:INFO:Checking exceptions
2021-07-16 06:43:49,210:INFO:Preloading libraries
2021-07-16 06:43:49,613:INFO:Creating MLFlow logs
2021-07-16 06:43:49,655:INFO:Model: NeuralNetClassifier
2021-07-16 06:43:49,880:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 100, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc32633fa10>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc3cc7a5c90>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3cc19c890>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3cc19c5d0>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc3cc68ad10>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc3cc526d10>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 06:43:50,804:INFO:Creating MLFlow logs
2021-07-16 06:43:50,836:INFO:Model: NeuralNetClassifier
2021-07-16 06:43:51,002:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.05, 'max_epochs': 200, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7fc32616de10>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'module__num_units_d2': 20, 'module__num_units_d1': 300, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7fc3cc6c9610>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbca81d0>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7fc3fbae3ed0>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7fc3fbcdc510>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7fc3fbf993b0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7fc3cc719d10>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-16 06:43:51,953:INFO:Creating MLFlow logs
2021-07-16 06:43:51,985:INFO:Model: Logistic Regression
2021-07-16 06:43:52,147: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}
2021-07-16 06:43:52,850:INFO:create_model_container: 43
2021-07-16 06:43:52,851:INFO:master_model_container: 43
2021-07-16 06:43:52,851:INFO:display_container: 16
2021-07-16 06:43:52,852: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-16 06:43:52,852:INFO:compare_models() succesfully completed......................................
2021-07-18 14:38:07,847:INFO:PyCaret Supervised Module
2021-07-18 14:38:07,849:INFO:ML Usecase: classification
2021-07-18 14:38:07,849:INFO:version 2.3.2
2021-07-18 14:38:07,849:INFO:Initializing setup()
2021-07-18 14:38:07,850:INFO:setup(target=stabf, 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=0.7, test_data=None, preprocess=True, imputation_type=simple, iterative_imputation_iters=5, categorical_features=None, categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=10, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-07-18 14:38:07,850:INFO:Checking environment
2021-07-18 14:38:07,850:INFO:python_version: 3.7.6
2021-07-18 14:38:07,850:INFO:python_build: ('default', 'Jan  8 2020 19:59:22')
2021-07-18 14:38:07,851:INFO:machine: x86_64
2021-07-18 14:38:07,852:INFO:platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-debian-bullseye-sid
2021-07-18 14:38:07,852:INFO:Memory: svmem(total=13208035328, available=12226125824, percent=7.4, used=723128320, free=12082847744, active=738533376, inactive=224923648, buffers=48619520, cached=353439744, shared=69632, slab=66904064)
2021-07-18 14:38:07,853:INFO:Physical Core: 6
2021-07-18 14:38:07,853:INFO:Logical Core: 12
2021-07-18 14:38:07,854:INFO:Checking libraries
2021-07-18 14:38:07,854:INFO:pd==1.2.4
2021-07-18 14:38:07,854:INFO:numpy==1.19.5
2021-07-18 14:38:07,854:INFO:sklearn==0.23.2
2021-07-18 14:38:07,855:INFO:lightgbm==3.2.1
2021-07-18 14:38:07,855:WARNING:catboost not found
2021-07-18 14:38:07,856:WARNING:xgboost not found
2021-07-18 14:38:08,200:INFO:mlflow==1.19.0
2021-07-18 14:38:08,201:INFO:Checking Exceptions
2021-07-18 14:38:08,201:INFO:Declaring global variables
2021-07-18 14:38:08,201:INFO:USI: f954
2021-07-18 14:38:08,201:INFO:pycaret_globals: {'X', 'iterative_imputation_iters_param', 'seed', '_all_metrics', '_ml_usecase', '_internal_pipeline', 'fold_groups_param', 'fold_groups_param_full', 'fix_imbalance_param', 'experiment__', 'logging_param', 'gpu_param', 'create_model_container', 'log_plots_param', '_all_models_internal', 'X_test', 'fold_generator', 'n_jobs_param', 'prep_pipe', 'USI', 'fold_shuffle_param', 'master_model_container', 'transform_target_param', 'imputation_regressor', 'data_before_preprocess', 'y_test', '_gpu_n_jobs_param', '_all_models', 'transform_target_method_param', 'stratify_param', 'imputation_classifier', 'exp_name_log', 'display_container', 'pycaret_globals', 'fold_param', 'html_param', 'X_train', 'fix_imbalance_method_param', 'y', 'y_train', 'target_param', '_available_plots'}
2021-07-18 14:38:08,202:INFO:Preparing display monitor
2021-07-18 14:38:08,202:INFO:Preparing display monitor
2021-07-18 14:38:08,218:INFO:Importing libraries
2021-07-18 14:38:08,219:INFO:Copying data for preprocessing
2021-07-18 14:38:08,232:INFO:Declaring preprocessing parameters
2021-07-18 14:38:08,236:INFO:Creating preprocessing pipeline
2021-07-18 14:38:08,262:INFO:Preprocessing pipeline created successfully
2021-07-18 14:38:08,263:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-07-18 14:38:08,263:INFO:Creating global containers
2021-07-18 14:38:08,265:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-07-18 14:38:08,541:WARNING:Couldn't import xgboost.XGBClassifier
2021-07-18 14:38:08,542:WARNING:Couldn't import catboost.CatBoostClassifier
2021-07-18 14:38:08,605:WARNING:Couldn't import xgboost.XGBClassifier
2021-07-18 14:38:08,607:WARNING:Couldn't import catboost.CatBoostClassifier
2021-07-18 14:38:08,607:INFO:Creating grid variables
2021-07-18 14:38:08,623:INFO:Logging experiment in MLFlow
2021-07-18 14:38:08,676:WARNING:Couldn't create mlflow experiment. Exception:
2021-07-18 14:38:08,682:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 1702, in setup
    mlflow.create_experiment(exp_name_log)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/tracking/fluent.py", line 875, in create_experiment
    return MlflowClient().create_experiment(name, artifact_location)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/tracking/client.py", line 500, in create_experiment
    return self._tracking_client.create_experiment(name, artifact_location)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/tracking/_tracking_service/client.py", line 167, in create_experiment
    return self.store.create_experiment(name=name, artifact_location=artifact_location,)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/store/tracking/file_store.py", line 313, in create_experiment
    self._validate_experiment_name(name)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/store/tracking/file_store.py", line 308, in _validate_experiment_name
    databricks_pb2.RESOURCE_ALREADY_EXISTS,
mlflow.exceptions.MlflowException: Experiment 'electrical_grid_1' already exists.

2021-07-18 14:38:09,680:INFO:SubProcess save_model() called ==================================
2021-07-18 14:38:09,688:INFO:Initializing save_model()
2021-07-18 14:38:09,688:INFO:save_model(model=Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('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=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False), verbose=False, kwargs={})
2021-07-18 14:38:09,688:INFO:Adding model into prep_pipe
2021-07-18 14:38:09,689:WARNING:Only Model saved as it was a pipeline.
2021-07-18 14:38:09,700:INFO:Transformation Pipeline.pkl saved in current working directory
2021-07-18 14:38:09,704:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-07-18 14:38:09,704:INFO:save_model() successfully completed......................................
2021-07-18 14:38:09,767:INFO:SubProcess save_model() end ==================================
2021-07-18 14:38:10,004:INFO:create_model_container: 0
2021-07-18 14:38:10,004:INFO:master_model_container: 0
2021-07-18 14:38:10,004:INFO:display_container: 1
2021-07-18 14:38:10,008:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-07-18 14:38:10,008:INFO:setup() succesfully completed......................................
2021-07-18 14:38:21,131:INFO:Initializing compare_models()
2021-07-18 14:38:21,132:INFO:compare_models(include=None, fold=None, round=4, cross_validation=True, sort=Accuracy, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-07-18 14:38:21,132:INFO:Checking exceptions
2021-07-18 14:38:21,134:INFO:Preparing display monitor
2021-07-18 14:38:21,135:INFO:Preparing display monitor
2021-07-18 14:38:21,163:INFO:Initializing Logistic Regression
2021-07-18 14:38:21,164:INFO:Total runtime is 8.06252161661784e-06 minutes
2021-07-18 14:38:21,174:INFO:SubProcess create_model() called ==================================
2021-07-18 14:38:21,174:INFO:Initializing create_model()
2021-07-18 14:38:21,174:INFO:create_model(estimator=lr, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c115b4650>, kwargs={})
2021-07-18 14:38:21,175:INFO:Checking exceptions
2021-07-18 14:38:21,175:INFO:Importing libraries
2021-07-18 14:38:21,176:INFO:Copying training dataset
2021-07-18 14:38:21,177:INFO:Defining folds
2021-07-18 14:38:21,177:INFO:Declaring metric variables
2021-07-18 14:38:21,186:INFO:Importing untrained model
2021-07-18 14:38:21,194:INFO:Logistic Regression Imported succesfully
2021-07-18 14:38:21,212:INFO:Starting cross validation
2021-07-18 14:38:21,213:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 14:38:23,596:INFO:Calculating mean and std
2021-07-18 14:38:23,599:INFO:Creating metrics dataframe
2021-07-18 14:38:23,612:INFO:Uploading results into container
2021-07-18 14:38:23,613:INFO:Uploading model into container now
2021-07-18 14:38:23,613:INFO:create_model_container: 1
2021-07-18 14:38:23,613:INFO:master_model_container: 1
2021-07-18 14:38:23,614:INFO:display_container: 2
2021-07-18 14:38:23,615: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)
2021-07-18 14:38:23,615:INFO:create_model() succesfully completed......................................
2021-07-18 14:38:23,759:INFO:SubProcess create_model() end ==================================
2021-07-18 14:38:23,759:INFO:Creating metrics dataframe
2021-07-18 14:38:23,781:INFO:Initializing K Neighbors Classifier
2021-07-18 14:38:23,781:INFO:Total runtime is 0.04363665183385214 minutes
2021-07-18 14:38:23,794:INFO:SubProcess create_model() called ==================================
2021-07-18 14:38:23,794:INFO:Initializing create_model()
2021-07-18 14:38:23,795:INFO:create_model(estimator=knn, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c115b4650>, kwargs={})
2021-07-18 14:38:23,795:INFO:Checking exceptions
2021-07-18 14:38:23,795:INFO:Importing libraries
2021-07-18 14:38:23,796:INFO:Copying training dataset
2021-07-18 14:38:23,797:INFO:Defining folds
2021-07-18 14:38:23,797:INFO:Declaring metric variables
2021-07-18 14:38:23,808:INFO:Importing untrained model
2021-07-18 14:38:23,819:INFO:K Neighbors Classifier Imported succesfully
2021-07-18 14:38:23,844:INFO:Starting cross validation
2021-07-18 14:38:23,845:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 14:38:25,184:INFO:Calculating mean and std
2021-07-18 14:38:25,186:INFO:Creating metrics dataframe
2021-07-18 14:38:25,192:INFO:Uploading results into container
2021-07-18 14:38:25,192:INFO:Uploading model into container now
2021-07-18 14:38:25,193:INFO:create_model_container: 2
2021-07-18 14:38:25,193:INFO:master_model_container: 2
2021-07-18 14:38:25,193:INFO:display_container: 2
2021-07-18 14:38:25,194:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
                     metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
                     weights='uniform')
2021-07-18 14:38:25,194:INFO:create_model() succesfully completed......................................
2021-07-18 14:38:25,257:INFO:SubProcess create_model() end ==================================
2021-07-18 14:38:25,258:INFO:Creating metrics dataframe
2021-07-18 14:38:25,277:INFO:Initializing Naive Bayes
2021-07-18 14:38:25,278:INFO:Total runtime is 0.06857638756434123 minutes
2021-07-18 14:38:25,286:INFO:SubProcess create_model() called ==================================
2021-07-18 14:38:25,287:INFO:Initializing create_model()
2021-07-18 14:38:25,287:INFO:create_model(estimator=nb, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c115b4650>, kwargs={})
2021-07-18 14:38:25,287:INFO:Checking exceptions
2021-07-18 14:38:25,287:INFO:Importing libraries
2021-07-18 14:38:25,288:INFO:Copying training dataset
2021-07-18 14:38:25,289:INFO:Defining folds
2021-07-18 14:38:25,289:INFO:Declaring metric variables
2021-07-18 14:38:25,296:INFO:Importing untrained model
2021-07-18 14:38:25,305:INFO:Naive Bayes Imported succesfully
2021-07-18 14:38:25,327:INFO:Starting cross validation
2021-07-18 14:38:25,328:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 14:38:25,378:INFO:Calculating mean and std
2021-07-18 14:38:25,380:INFO:Creating metrics dataframe
2021-07-18 14:38:25,385:INFO:Uploading results into container
2021-07-18 14:38:25,385:INFO:Uploading model into container now
2021-07-18 14:38:25,386:INFO:create_model_container: 3
2021-07-18 14:38:25,386:INFO:master_model_container: 3
2021-07-18 14:38:25,386:INFO:display_container: 2
2021-07-18 14:38:25,387:INFO:GaussianNB(priors=None, var_smoothing=1e-09)
2021-07-18 14:38:25,387:INFO:create_model() succesfully completed......................................
2021-07-18 14:38:25,462:INFO:SubProcess create_model() end ==================================
2021-07-18 14:38:25,463:INFO:Creating metrics dataframe
2021-07-18 14:38:25,485:INFO:Initializing Decision Tree Classifier
2021-07-18 14:38:25,485:INFO:Total runtime is 0.0720334768295288 minutes
2021-07-18 14:38:25,498:INFO:SubProcess create_model() called ==================================
2021-07-18 14:38:25,498:INFO:Initializing create_model()
2021-07-18 14:38:25,499:INFO:create_model(estimator=dt, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c115b4650>, kwargs={})
2021-07-18 14:38:25,499:INFO:Checking exceptions
2021-07-18 14:38:25,499:INFO:Importing libraries
2021-07-18 14:38:25,499:INFO:Copying training dataset
2021-07-18 14:38:25,500:INFO:Defining folds
2021-07-18 14:38:25,501:INFO:Declaring metric variables
2021-07-18 14:38:25,511:INFO:Importing untrained model
2021-07-18 14:38:25,519:INFO:Decision Tree Classifier Imported succesfully
2021-07-18 14:38:25,541:INFO:Starting cross validation
2021-07-18 14:38:25,542:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 14:38:25,688:INFO:Calculating mean and std
2021-07-18 14:38:25,689:INFO:Creating metrics dataframe
2021-07-18 14:38:25,695:INFO:Uploading results into container
2021-07-18 14:38:25,695:INFO:Uploading model into container now
2021-07-18 14:38:25,695:INFO:create_model_container: 4
2021-07-18 14:38:25,696:INFO:master_model_container: 4
2021-07-18 14:38:25,696:INFO:display_container: 2
2021-07-18 14:38:25,697: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')
2021-07-18 14:38:25,697:INFO:create_model() succesfully completed......................................
2021-07-18 14:38:25,781:INFO:SubProcess create_model() end ==================================
2021-07-18 14:38:25,781:INFO:Creating metrics dataframe
2021-07-18 14:38:25,811:INFO:Initializing SVM - Linear Kernel
2021-07-18 14:38:25,812:INFO:Total runtime is 0.07747320334116617 minutes
2021-07-18 14:38:25,825:INFO:SubProcess create_model() called ==================================
2021-07-18 14:38:25,826:INFO:Initializing create_model()
2021-07-18 14:38:25,826:INFO:create_model(estimator=svm, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c115b4650>, kwargs={})
2021-07-18 14:38:25,827:INFO:Checking exceptions
2021-07-18 14:38:25,827:INFO:Importing libraries
2021-07-18 14:38:25,827:INFO:Copying training dataset
2021-07-18 14:38:25,828:INFO:Defining folds
2021-07-18 14:38:25,829:INFO:Declaring metric variables
2021-07-18 14:38:25,838:INFO:Importing untrained model
2021-07-18 14:38:25,847:INFO:SVM - Linear Kernel Imported succesfully
2021-07-18 14:38:25,864:INFO:Starting cross validation
2021-07-18 14:38:25,864:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 14:38:25,984:INFO:Calculating mean and std
2021-07-18 14:38:25,985:INFO:Creating metrics dataframe
2021-07-18 14:38:25,989:INFO:Uploading results into container
2021-07-18 14:38:25,989:INFO:Uploading model into container now
2021-07-18 14:38:25,990:INFO:create_model_container: 5
2021-07-18 14:38:25,990:INFO:master_model_container: 5
2021-07-18 14:38:25,990:INFO:display_container: 2
2021-07-18 14:38:25,990: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)
2021-07-18 14:38:25,991:INFO:create_model() succesfully completed......................................
2021-07-18 14:38:26,046:INFO:SubProcess create_model() end ==================================
2021-07-18 14:38:26,046:INFO:Creating metrics dataframe
2021-07-18 14:38:26,063:INFO:Initializing Ridge Classifier
2021-07-18 14:38:26,064:INFO:Total runtime is 0.08167803287506102 minutes
2021-07-18 14:38:26,072:INFO:SubProcess create_model() called ==================================
2021-07-18 14:38:26,072:INFO:Initializing create_model()
2021-07-18 14:38:26,073:INFO:create_model(estimator=ridge, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c115b4650>, kwargs={})
2021-07-18 14:38:26,073:INFO:Checking exceptions
2021-07-18 14:38:26,073:INFO:Importing libraries
2021-07-18 14:38:26,073:INFO:Copying training dataset
2021-07-18 14:38:26,074:INFO:Defining folds
2021-07-18 14:38:26,074:INFO:Declaring metric variables
2021-07-18 14:38:26,081:INFO:Importing untrained model
2021-07-18 14:38:26,089:INFO:Ridge Classifier Imported succesfully
2021-07-18 14:38:26,106:INFO:Starting cross validation
2021-07-18 14:38:26,107:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 14:38:26,157:INFO:Calculating mean and std
2021-07-18 14:38:26,159:INFO:Creating metrics dataframe
2021-07-18 14:38:26,163:INFO:Uploading results into container
2021-07-18 14:38:26,164:INFO:Uploading model into container now
2021-07-18 14:38:26,164:INFO:create_model_container: 6
2021-07-18 14:38:26,164:INFO:master_model_container: 6
2021-07-18 14:38:26,164:INFO:display_container: 2
2021-07-18 14:38:26,165: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)
2021-07-18 14:38:26,165:INFO:create_model() succesfully completed......................................
2021-07-18 14:38:26,219:INFO:SubProcess create_model() end ==================================
2021-07-18 14:38:26,219:INFO:Creating metrics dataframe
2021-07-18 14:38:26,238:INFO:Initializing Random Forest Classifier
2021-07-18 14:38:26,238:INFO:Total runtime is 0.08458433945973713 minutes
2021-07-18 14:38:26,247:INFO:SubProcess create_model() called ==================================
2021-07-18 14:38:26,247:INFO:Initializing create_model()
2021-07-18 14:38:26,248:INFO:create_model(estimator=rf, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c115b4650>, kwargs={})
2021-07-18 14:38:26,248:INFO:Checking exceptions
2021-07-18 14:38:26,248:INFO:Importing libraries
2021-07-18 14:38:26,248:INFO:Copying training dataset
2021-07-18 14:38:26,249:INFO:Defining folds
2021-07-18 14:38:26,249:INFO:Declaring metric variables
2021-07-18 14:38:26,256:INFO:Importing untrained model
2021-07-18 14:38:26,264:INFO:Random Forest Classifier Imported succesfully
2021-07-18 14:38:26,280:INFO:Starting cross validation
2021-07-18 14:38:26,281:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 14:38:28,650:INFO:Calculating mean and std
2021-07-18 14:38:28,652:INFO:Creating metrics dataframe
2021-07-18 14:38:28,657:INFO:Uploading results into container
2021-07-18 14:38:28,657:INFO:Uploading model into container now
2021-07-18 14:38:28,657:INFO:create_model_container: 7
2021-07-18 14:38:28,657:INFO:master_model_container: 7
2021-07-18 14:38:28,658:INFO:display_container: 2
2021-07-18 14:38:28,658: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)
2021-07-18 14:38:28,659:INFO:create_model() succesfully completed......................................
2021-07-18 14:38:28,713:INFO:SubProcess create_model() end ==================================
2021-07-18 14:38:28,713:INFO:Creating metrics dataframe
2021-07-18 14:38:28,732:INFO:Initializing Quadratic Discriminant Analysis
2021-07-18 14:38:28,733:INFO:Total runtime is 0.12615782419840493 minutes
2021-07-18 14:38:28,741:INFO:SubProcess create_model() called ==================================
2021-07-18 14:38:28,741:INFO:Initializing create_model()
2021-07-18 14:38:28,742:INFO:create_model(estimator=qda, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c115b4650>, kwargs={})
2021-07-18 14:38:28,742:INFO:Checking exceptions
2021-07-18 14:38:28,742:INFO:Importing libraries
2021-07-18 14:38:28,742:INFO:Copying training dataset
2021-07-18 14:38:28,743:INFO:Defining folds
2021-07-18 14:38:28,743:INFO:Declaring metric variables
2021-07-18 14:38:28,750:INFO:Importing untrained model
2021-07-18 14:38:28,758:INFO:Quadratic Discriminant Analysis Imported succesfully
2021-07-18 14:38:28,775:INFO:Starting cross validation
2021-07-18 14:38:28,776:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 14:38:28,835:INFO:Calculating mean and std
2021-07-18 14:38:28,836:INFO:Creating metrics dataframe
2021-07-18 14:38:28,841:INFO:Uploading results into container
2021-07-18 14:38:28,841:INFO:Uploading model into container now
2021-07-18 14:38:28,841:INFO:create_model_container: 8
2021-07-18 14:38:28,841:INFO:master_model_container: 8
2021-07-18 14:38:28,841:INFO:display_container: 2
2021-07-18 14:38:28,842:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,
                              store_covariance=False, tol=0.0001)
2021-07-18 14:38:28,842:INFO:create_model() succesfully completed......................................
2021-07-18 14:38:28,898:INFO:SubProcess create_model() end ==================================
2021-07-18 14:38:28,898:INFO:Creating metrics dataframe
2021-07-18 14:38:28,916:INFO:Initializing Ada Boost Classifier
2021-07-18 14:38:28,916:INFO:Total runtime is 0.129219122727712 minutes
2021-07-18 14:38:28,926:INFO:SubProcess create_model() called ==================================
2021-07-18 14:38:28,927:INFO:Initializing create_model()
2021-07-18 14:38:28,927:INFO:create_model(estimator=ada, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c115b4650>, kwargs={})
2021-07-18 14:38:28,927:INFO:Checking exceptions
2021-07-18 14:38:28,927:INFO:Importing libraries
2021-07-18 14:38:28,927:INFO:Copying training dataset
2021-07-18 14:38:28,928:INFO:Defining folds
2021-07-18 14:38:28,929:INFO:Declaring metric variables
2021-07-18 14:38:28,937:INFO:Importing untrained model
2021-07-18 14:38:28,945:INFO:Ada Boost Classifier Imported succesfully
2021-07-18 14:38:28,962:INFO:Starting cross validation
2021-07-18 14:38:28,963:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 14:38:29,732:INFO:Calculating mean and std
2021-07-18 14:38:29,733:INFO:Creating metrics dataframe
2021-07-18 14:38:29,737:INFO:Uploading results into container
2021-07-18 14:38:29,738:INFO:Uploading model into container now
2021-07-18 14:38:29,738:INFO:create_model_container: 9
2021-07-18 14:38:29,738:INFO:master_model_container: 9
2021-07-18 14:38:29,739:INFO:display_container: 2
2021-07-18 14:38:29,739:INFO:AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,
                   n_estimators=50, random_state=123)
2021-07-18 14:38:29,739:INFO:create_model() succesfully completed......................................
2021-07-18 14:38:29,794:INFO:SubProcess create_model() end ==================================
2021-07-18 14:38:29,794:INFO:Creating metrics dataframe
2021-07-18 14:38:29,814:INFO:Initializing Gradient Boosting Classifier
2021-07-18 14:38:29,815:INFO:Total runtime is 0.14419321616490682 minutes
2021-07-18 14:38:29,824:INFO:SubProcess create_model() called ==================================
2021-07-18 14:38:29,824:INFO:Initializing create_model()
2021-07-18 14:38:29,825:INFO:create_model(estimator=gbc, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c115b4650>, kwargs={})
2021-07-18 14:38:29,825:INFO:Checking exceptions
2021-07-18 14:38:29,825:INFO:Importing libraries
2021-07-18 14:38:29,825:INFO:Copying training dataset
2021-07-18 14:38:29,826:INFO:Defining folds
2021-07-18 14:38:29,827:INFO:Declaring metric variables
2021-07-18 14:38:29,835:INFO:Importing untrained model
2021-07-18 14:38:29,843:INFO:Gradient Boosting Classifier Imported succesfully
2021-07-18 14:38:29,862:INFO:Starting cross validation
2021-07-18 14:38:29,863:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 14:38:34,088:INFO:Calculating mean and std
2021-07-18 14:38:34,090:INFO:Creating metrics dataframe
2021-07-18 14:38:34,097:INFO:Uploading results into container
2021-07-18 14:38:34,097:INFO:Uploading model into container now
2021-07-18 14:38:34,097:INFO:create_model_container: 10
2021-07-18 14:38:34,098:INFO:master_model_container: 10
2021-07-18 14:38:34,098:INFO:display_container: 2
2021-07-18 14:38:34,099: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)
2021-07-18 14:38:34,099:INFO:create_model() succesfully completed......................................
2021-07-18 14:38:34,163:INFO:SubProcess create_model() end ==================================
2021-07-18 14:38:34,164:INFO:Creating metrics dataframe
2021-07-18 14:38:34,192:INFO:Initializing Linear Discriminant Analysis
2021-07-18 14:38:34,193:INFO:Total runtime is 0.21715860764185588 minutes
2021-07-18 14:38:34,205:INFO:SubProcess create_model() called ==================================
2021-07-18 14:38:34,206:INFO:Initializing create_model()
2021-07-18 14:38:34,206:INFO:create_model(estimator=lda, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c115b4650>, kwargs={})
2021-07-18 14:38:34,206:INFO:Checking exceptions
2021-07-18 14:38:34,207:INFO:Importing libraries
2021-07-18 14:38:34,207:INFO:Copying training dataset
2021-07-18 14:38:34,208:INFO:Defining folds
2021-07-18 14:38:34,209:INFO:Declaring metric variables
2021-07-18 14:38:34,219:INFO:Importing untrained model
2021-07-18 14:38:34,231:INFO:Linear Discriminant Analysis Imported succesfully
2021-07-18 14:38:34,255:INFO:Starting cross validation
2021-07-18 14:38:34,256:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 14:38:34,346:INFO:Calculating mean and std
2021-07-18 14:38:34,347:INFO:Creating metrics dataframe
2021-07-18 14:38:34,353:INFO:Uploading results into container
2021-07-18 14:38:34,353:INFO:Uploading model into container now
2021-07-18 14:38:34,354:INFO:create_model_container: 11
2021-07-18 14:38:34,354:INFO:master_model_container: 11
2021-07-18 14:38:34,354:INFO:display_container: 2
2021-07-18 14:38:34,355:INFO:LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,
                           solver='svd', store_covariance=False, tol=0.0001)
2021-07-18 14:38:34,355:INFO:create_model() succesfully completed......................................
2021-07-18 14:38:34,420:INFO:SubProcess create_model() end ==================================
2021-07-18 14:38:34,421:INFO:Creating metrics dataframe
2021-07-18 14:38:34,447:INFO:Initializing Extra Trees Classifier
2021-07-18 14:38:34,447:INFO:Total runtime is 0.22140290339787802 minutes
2021-07-18 14:38:34,458:INFO:SubProcess create_model() called ==================================
2021-07-18 14:38:34,459:INFO:Initializing create_model()
2021-07-18 14:38:34,459:INFO:create_model(estimator=et, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c115b4650>, kwargs={})
2021-07-18 14:38:34,460:INFO:Checking exceptions
2021-07-18 14:38:34,460:INFO:Importing libraries
2021-07-18 14:38:34,460:INFO:Copying training dataset
2021-07-18 14:38:34,461:INFO:Defining folds
2021-07-18 14:38:34,462:INFO:Declaring metric variables
2021-07-18 14:38:34,472:INFO:Importing untrained model
2021-07-18 14:38:34,483:INFO:Extra Trees Classifier Imported succesfully
2021-07-18 14:38:34,505:INFO:Starting cross validation
2021-07-18 14:38:34,506:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 14:38:36,724:INFO:Calculating mean and std
2021-07-18 14:38:36,726:INFO:Creating metrics dataframe
2021-07-18 14:38:36,735:INFO:Uploading results into container
2021-07-18 14:38:36,735:INFO:Uploading model into container now
2021-07-18 14:38:36,736:INFO:create_model_container: 12
2021-07-18 14:38:36,736:INFO:master_model_container: 12
2021-07-18 14:38:36,737:INFO:display_container: 2
2021-07-18 14:38:36,739: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)
2021-07-18 14:38:36,739:INFO:create_model() succesfully completed......................................
2021-07-18 14:38:36,825:INFO:SubProcess create_model() end ==================================
2021-07-18 14:38:36,826:INFO:Creating metrics dataframe
2021-07-18 14:38:36,859:INFO:Initializing Light Gradient Boosting Machine
2021-07-18 14:38:36,859:INFO:Total runtime is 0.2615968147913615 minutes
2021-07-18 14:38:36,872:INFO:SubProcess create_model() called ==================================
2021-07-18 14:38:36,873:INFO:Initializing create_model()
2021-07-18 14:38:36,873:INFO:create_model(estimator=lightgbm, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c115b4650>, kwargs={})
2021-07-18 14:38:36,874:INFO:Checking exceptions
2021-07-18 14:38:36,874:INFO:Importing libraries
2021-07-18 14:38:36,874:INFO:Copying training dataset
2021-07-18 14:38:36,876:INFO:Defining folds
2021-07-18 14:38:36,876:INFO:Declaring metric variables
2021-07-18 14:38:36,889:INFO:Importing untrained model
2021-07-18 14:38:36,903:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-18 14:38:36,935:INFO:Starting cross validation
2021-07-18 14:38:36,936:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 14:38:38,577:INFO:Calculating mean and std
2021-07-18 14:38:38,579:INFO:Creating metrics dataframe
2021-07-18 14:38:38,586:INFO:Uploading results into container
2021-07-18 14:38:38,586:INFO:Uploading model into container now
2021-07-18 14:38:38,587:INFO:create_model_container: 13
2021-07-18 14:38:38,587:INFO:master_model_container: 13
2021-07-18 14:38:38,587:INFO:display_container: 2
2021-07-18 14:38:38,588: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-18 14:38:38,589:INFO:create_model() succesfully completed......................................
2021-07-18 14:38:38,664:INFO:SubProcess create_model() end ==================================
2021-07-18 14:38:38,665:INFO:Creating metrics dataframe
2021-07-18 14:38:38,769:INFO:Initializing create_model()
2021-07-18 14:38:38,770:INFO:create_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, display=None, kwargs={})
2021-07-18 14:38:38,770:INFO:Checking exceptions
2021-07-18 14:38:38,771:INFO:Importing libraries
2021-07-18 14:38:38,771:INFO:Copying training dataset
2021-07-18 14:38:38,772:INFO:Defining folds
2021-07-18 14:38:38,772:INFO:Declaring metric variables
2021-07-18 14:38:38,773:INFO:Importing untrained model
2021-07-18 14:38:38,773:INFO:Declaring custom model
2021-07-18 14:38:38,775:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-18 14:38:38,777:INFO:Cross validation set to False
2021-07-18 14:38:38,777:INFO:Fitting Model
2021-07-18 14:38:40,374: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-18 14:38:40,375:INFO:create_models() succesfully completed......................................
2021-07-18 14:38:40,440:INFO:Creating MLFlow logs
2021-07-18 14:38:40,503:INFO:Model: Light Gradient Boosting Machine
2021-07-18 14:38:40,796: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': True, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0}
2021-07-18 14:38:41,533:INFO:Initializing predict_model()
2021-07-18 14:38:41,534:INFO:predict_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-18 14:38:41,534:INFO:Checking exceptions
2021-07-18 14:38:41,534:INFO:Preloading libraries
2021-07-18 14:38:41,865:INFO:Creating MLFlow logs
2021-07-18 14:38:41,911:INFO:Model: Gradient Boosting Classifier
2021-07-18 14:38:42,146: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}
2021-07-18 14:38:43,043:INFO:Creating MLFlow logs
2021-07-18 14:38:43,091:INFO:Model: Extra Trees Classifier
2021-07-18 14:38:43,329: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}
2021-07-18 14:38:44,270:INFO:Creating MLFlow logs
2021-07-18 14:38:44,313:INFO:Model: Random Forest Classifier
2021-07-18 14:38:44,567: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}
2021-07-18 14:38:45,788:INFO:Creating MLFlow logs
2021-07-18 14:38:45,849:INFO:Model: Ada Boost Classifier
2021-07-18 14:38:46,124:INFO:logged params: {'algorithm': 'SAMME.R', 'base_estimator': None, 'learning_rate': 1.0, 'n_estimators': 50, 'random_state': 123}
2021-07-18 14:38:46,980:INFO:Creating MLFlow logs
2021-07-18 14:38:47,027:INFO:Model: Decision Tree Classifier
2021-07-18 14:38:47,266: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'}
2021-07-18 14:38:48,175:INFO:Creating MLFlow logs
2021-07-18 14:38:48,218:INFO:Model: Naive Bayes
2021-07-18 14:38:48,439:INFO:logged params: {'priors': None, 'var_smoothing': 1e-09}
2021-07-18 14:38:49,105:INFO:Creating MLFlow logs
2021-07-18 14:38:49,150:INFO:Model: Linear Discriminant Analysis
2021-07-18 14:38:49,353:INFO:logged params: {'n_components': None, 'priors': None, 'shrinkage': None, 'solver': 'svd', 'store_covariance': False, 'tol': 0.0001}
2021-07-18 14:38:50,023:INFO:Creating MLFlow logs
2021-07-18 14:38:50,061:INFO:Model: Ridge Classifier
2021-07-18 14:38:50,255: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}
2021-07-18 14:38:50,968:INFO:Creating MLFlow logs
2021-07-18 14:38:51,022:INFO:Model: Logistic Regression
2021-07-18 14:38:51,308: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}
2021-07-18 14:38:52,245:INFO:Creating MLFlow logs
2021-07-18 14:38:52,292:INFO:Model: Quadratic Discriminant Analysis
2021-07-18 14:38:52,543:INFO:logged params: {'priors': None, 'reg_param': 0.0, 'store_covariance': False, 'tol': 0.0001}
2021-07-18 14:38:53,334:INFO:Creating MLFlow logs
2021-07-18 14:38:53,383:INFO:Model: K Neighbors Classifier
2021-07-18 14:38:53,619:INFO:logged params: {'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': -1, 'n_neighbors': 5, 'p': 2, 'weights': 'uniform'}
2021-07-18 14:38:54,487:INFO:Creating MLFlow logs
2021-07-18 14:38:54,533:INFO:Model: SVM - Linear Kernel
2021-07-18 14:38:54,762: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}
2021-07-18 14:38:55,665:INFO:create_model_container: 13
2021-07-18 14:38:55,665:INFO:master_model_container: 13
2021-07-18 14:38:55,666:INFO:display_container: 2
2021-07-18 14:38:55,667: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-18 14:38:55,667:INFO:compare_models() succesfully completed......................................
2021-07-18 15:47:41,591:INFO:gpu_param set to False
2021-07-18 15:47:41,667:WARNING:Couldn't import xgboost.XGBClassifier
2021-07-18 15:47:41,669:WARNING:Couldn't import catboost.CatBoostClassifier
2021-07-18 15:48:23,523:INFO:Initializing create_model()
2021-07-18 15:48:23,524:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-07-18 15:48:23,524:INFO:Checking exceptions
2021-07-18 15:48:23,524:INFO:Preparing display monitor
2021-07-18 15:48:23,545:INFO:Importing libraries
2021-07-18 15:48:23,546:INFO:Copying training dataset
2021-07-18 15:48:23,548:INFO:Defining folds
2021-07-18 15:48:23,549:INFO:Declaring metric variables
2021-07-18 15:48:23,557:INFO:Importing untrained model
2021-07-18 15:48:23,557:INFO:Declaring custom model
2021-07-18 15:48:23,568:INFO:NeuralNetClassifier Imported succesfully
2021-07-18 15:48:23,587:INFO:Starting cross validation
2021-07-18 15:48:23,589:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 15:48:33,812:INFO:Calculating mean and std
2021-07-18 15:48:33,815:INFO:Creating metrics dataframe
2021-07-18 15:48:33,841:INFO:Finalizing model
2021-07-18 15:48:36,111:INFO:Creating MLFlow logs
2021-07-18 15:48:36,177:INFO:Model: NeuralNetClassifier
2021-07-18 15:48:36,430:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 10, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7f3c3739e0d0>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7f3c438d3bd0>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c3740add0>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c3740aa50>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7f3c373a7090>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7f3c374734d0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7f3c373a7990>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-18 15:48:37,345:INFO:Initializing predict_model()
2021-07-18 15:48:37,346:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-18 15:48:37,346:INFO:Checking exceptions
2021-07-18 15:48:37,346:INFO:Preloading libraries
2021-07-18 15:48:37,366:WARNING:Couldn't create holdout prediction for model, exception below:
2021-07-18 15:48:37,371:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 10139, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 8559, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-07-18 15:48:37,733:INFO:Uploading results into container
2021-07-18 15:48:37,734:INFO:Uploading model into container now
2021-07-18 15:48:37,759:INFO:create_model_container: 14
2021-07-18 15:48:37,759:INFO:master_model_container: 14
2021-07-18 15:48:37,760:INFO:display_container: 3
2021-07-18 15:48:37,762:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-18 15:48:37,762:INFO:create_model() succesfully completed......................................
2021-07-18 15:48:39,096:INFO:Initializing compare_models()
2021-07-18 15:48:39,097:INFO:compare_models(include=[Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 'lr', 'lightgbm'], fold=None, round=4, cross_validation=True, sort=Accuracy, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-07-18 15:48:39,097:INFO:Checking exceptions
2021-07-18 15:48:39,099:INFO:Preparing display monitor
2021-07-18 15:48:39,100:INFO:Preparing display monitor
2021-07-18 15:48:39,145:INFO:Initializing custom model NeuralNetClassifier
2021-07-18 15:48:39,146:INFO:Total runtime is 1.457532246907552e-05 minutes
2021-07-18 15:48:39,162:INFO:SubProcess create_model() called ==================================
2021-07-18 15:48:39,165:INFO:Initializing create_model()
2021-07-18 15:48:39,165:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=MyModule(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c0902fcd0>, kwargs={})
2021-07-18 15:48:39,166:INFO:Checking exceptions
2021-07-18 15:48:39,166:INFO:Importing libraries
2021-07-18 15:48:39,167:INFO:Copying training dataset
2021-07-18 15:48:39,168:INFO:Defining folds
2021-07-18 15:48:39,169:INFO:Declaring metric variables
2021-07-18 15:48:39,183:INFO:Importing untrained model
2021-07-18 15:48:39,184:INFO:Declaring custom model
2021-07-18 15:48:39,198:INFO:NeuralNetClassifier Imported succesfully
2021-07-18 15:48:39,233:INFO:Starting cross validation
2021-07-18 15:48:39,234:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 15:48:47,016:INFO:Calculating mean and std
2021-07-18 15:48:47,018:INFO:Creating metrics dataframe
2021-07-18 15:48:47,028:INFO:Uploading results into container
2021-07-18 15:48:47,028:INFO:Uploading model into container now
2021-07-18 15:48:47,028:INFO:create_model_container: 15
2021-07-18 15:48:47,029:INFO:master_model_container: 15
2021-07-18 15:48:47,029:INFO:display_container: 4
2021-07-18 15:48:47,033:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.MyModule'>,
))],
         verbose=False)
2021-07-18 15:48:47,033:INFO:create_model() succesfully completed......................................
2021-07-18 15:48:47,178:INFO:SubProcess create_model() end ==================================
2021-07-18 15:48:47,178:INFO:Creating metrics dataframe
2021-07-18 15:48:47,215:INFO:Initializing Logistic Regression
2021-07-18 15:48:47,215:INFO:Total runtime is 0.13451302448908486 minutes
2021-07-18 15:48:47,234:INFO:SubProcess create_model() called ==================================
2021-07-18 15:48:47,235:INFO:Initializing create_model()
2021-07-18 15:48:47,236:INFO:create_model(estimator=lr, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c0902fcd0>, kwargs={})
2021-07-18 15:48:47,236:INFO:Checking exceptions
2021-07-18 15:48:47,236:INFO:Importing libraries
2021-07-18 15:48:47,237:INFO:Copying training dataset
2021-07-18 15:48:47,239:INFO:Defining folds
2021-07-18 15:48:47,239:INFO:Declaring metric variables
2021-07-18 15:48:47,262:INFO:Importing untrained model
2021-07-18 15:48:47,291:INFO:Logistic Regression Imported succesfully
2021-07-18 15:48:47,329:INFO:Starting cross validation
2021-07-18 15:48:47,330:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 15:48:47,694:INFO:Calculating mean and std
2021-07-18 15:48:47,695:INFO:Creating metrics dataframe
2021-07-18 15:48:47,704:INFO:Uploading results into container
2021-07-18 15:48:47,705:INFO:Uploading model into container now
2021-07-18 15:48:47,705:INFO:create_model_container: 16
2021-07-18 15:48:47,706:INFO:master_model_container: 16
2021-07-18 15:48:47,706:INFO:display_container: 4
2021-07-18 15:48:47,707: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)
2021-07-18 15:48:47,707:INFO:create_model() succesfully completed......................................
2021-07-18 15:48:47,825:INFO:SubProcess create_model() end ==================================
2021-07-18 15:48:47,825:INFO:Creating metrics dataframe
2021-07-18 15:48:47,859:INFO:Initializing Light Gradient Boosting Machine
2021-07-18 15:48:47,859:INFO:Total runtime is 0.1452417771021525 minutes
2021-07-18 15:48:47,876:INFO:SubProcess create_model() called ==================================
2021-07-18 15:48:47,877:INFO:Initializing create_model()
2021-07-18 15:48:47,877:INFO:create_model(estimator=lightgbm, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, verbose=False, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c0902fcd0>, kwargs={})
2021-07-18 15:48:47,878:INFO:Checking exceptions
2021-07-18 15:48:47,878:INFO:Importing libraries
2021-07-18 15:48:47,879:INFO:Copying training dataset
2021-07-18 15:48:47,881:INFO:Defining folds
2021-07-18 15:48:47,881:INFO:Declaring metric variables
2021-07-18 15:48:47,900:INFO:Importing untrained model
2021-07-18 15:48:47,918:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-18 15:48:47,954:INFO:Starting cross validation
2021-07-18 15:48:47,956:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 15:48:49,967:INFO:Calculating mean and std
2021-07-18 15:48:49,969:INFO:Creating metrics dataframe
2021-07-18 15:48:49,978:INFO:Uploading results into container
2021-07-18 15:48:49,979:INFO:Uploading model into container now
2021-07-18 15:48:49,979:INFO:create_model_container: 17
2021-07-18 15:48:49,980:INFO:master_model_container: 17
2021-07-18 15:48:49,981:INFO:display_container: 4
2021-07-18 15:48:49,982: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-18 15:48:49,983:INFO:create_model() succesfully completed......................................
2021-07-18 15:48:50,104:INFO:SubProcess create_model() end ==================================
2021-07-18 15:48:50,105:INFO:Creating metrics dataframe
2021-07-18 15:48:50,190:INFO:Initializing create_model()
2021-07-18 15:48:50,191:INFO:create_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, display=None, kwargs={})
2021-07-18 15:48:50,191:INFO:Checking exceptions
2021-07-18 15:48:50,192:INFO:Importing libraries
2021-07-18 15:48:50,193:INFO:Copying training dataset
2021-07-18 15:48:50,193:INFO:Defining folds
2021-07-18 15:48:50,194:INFO:Declaring metric variables
2021-07-18 15:48:50,194:INFO:Importing untrained model
2021-07-18 15:48:50,195:INFO:Declaring custom model
2021-07-18 15:48:50,196:INFO:Light Gradient Boosting Machine Imported succesfully
2021-07-18 15:48:50,197:INFO:Cross validation set to False
2021-07-18 15:48:50,198:INFO:Fitting Model
2021-07-18 15:48:54,648: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-18 15:48:54,649:INFO:create_models() succesfully completed......................................
2021-07-18 15:48:54,755:INFO:Creating MLFlow logs
2021-07-18 15:48:54,810:INFO:Model: Light Gradient Boosting Machine
2021-07-18 15:48:55,125: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': True, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0}
2021-07-18 15:48:55,974:INFO:Initializing predict_model()
2021-07-18 15:48:55,974:INFO:predict_model(estimator=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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-18 15:48:55,975:INFO:Checking exceptions
2021-07-18 15:48:55,975:INFO:Preloading libraries
2021-07-18 15:48:56,481:INFO:Creating MLFlow logs
2021-07-18 15:48:56,540:INFO:Model: Logistic Regression
2021-07-18 15:48:56,837: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}
2021-07-18 15:48:57,695:INFO:Creating MLFlow logs
2021-07-18 15:48:57,740:INFO:Model: NeuralNetClassifier
2021-07-18 15:48:57,953:INFO:logged params: {'module': <class '__main__.MyModule'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 10, 'batch_size': 128, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': <skorch.dataset.CVSplit object at 0x7f3c373b9cd0>, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'iterator_train__shuffle': True, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7f3c3ec9c2d0>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c43b3ded0>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c43b3dd10>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7f3c373f5a10>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7f3c374734d0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7f3c373f53d0>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-18 15:48:59,209:INFO:create_model_container: 17
2021-07-18 15:48:59,210:INFO:master_model_container: 17
2021-07-18 15:48:59,210:INFO:display_container: 4
2021-07-18 15:48:59,211: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=True,
               subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2021-07-18 15:48:59,211:INFO:compare_models() succesfully completed......................................
2021-07-18 19:13:38,188:INFO:Initializing create_model()
2021-07-18 19:13:38,190:INFO:create_model(estimator=<class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-07-18 19:13:38,191:INFO:Checking exceptions
2021-07-18 19:13:38,193:INFO:Preparing display monitor
2021-07-18 19:13:38,258:INFO:Importing libraries
2021-07-18 19:13:38,260:INFO:Copying training dataset
2021-07-18 19:13:38,263:INFO:Defining folds
2021-07-18 19:13:38,264:INFO:Declaring metric variables
2021-07-18 19:13:38,272:INFO:Importing untrained model
2021-07-18 19:13:38,273:INFO:Declaring custom model
2021-07-18 19:13:38,285:INFO:NeuralNetClassifier Imported succesfully
2021-07-18 19:13:38,302:INFO:Starting cross validation
2021-07-18 19:13:38,304:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 19:13:41,865:INFO:Calculating mean and std
2021-07-18 19:13:41,871:INFO:Creating metrics dataframe
2021-07-18 19:13:41,907:INFO:Finalizing model
2021-07-18 19:14:23,727:INFO:Initializing create_model()
2021-07-18 19:14:23,728:INFO:create_model(estimator=<class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
  module__num_inputs=12,
), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-07-18 19:14:23,729:INFO:Checking exceptions
2021-07-18 19:14:23,729:INFO:Preparing display monitor
2021-07-18 19:14:23,747:INFO:Importing libraries
2021-07-18 19:14:23,747:INFO:Copying training dataset
2021-07-18 19:14:23,750:INFO:Defining folds
2021-07-18 19:14:23,750:INFO:Declaring metric variables
2021-07-18 19:14:23,757:INFO:Importing untrained model
2021-07-18 19:14:23,757:INFO:Declaring custom model
2021-07-18 19:14:23,766:INFO:NeuralNetClassifier Imported succesfully
2021-07-18 19:14:23,782:INFO:Starting cross validation
2021-07-18 19:14:23,783:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 19:14:25,186:INFO:Calculating mean and std
2021-07-18 19:14:25,188:INFO:Creating metrics dataframe
2021-07-18 19:14:25,203:INFO:Finalizing model
2021-07-18 19:26:48,100:INFO:Initializing create_model()
2021-07-18 19:26:48,101:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-07-18 19:26:48,101:INFO:Checking exceptions
2021-07-18 19:26:48,102:INFO:Preparing display monitor
2021-07-18 19:26:48,121:INFO:Importing libraries
2021-07-18 19:26:48,122:INFO:Copying training dataset
2021-07-18 19:26:48,123:INFO:Defining folds
2021-07-18 19:26:48,123:INFO:Declaring metric variables
2021-07-18 19:26:48,132:INFO:Importing untrained model
2021-07-18 19:26:48,132:INFO:Declaring custom model
2021-07-18 19:26:48,142:INFO:NeuralNetClassifier Imported succesfully
2021-07-18 19:26:48,162:INFO:Starting cross validation
2021-07-18 19:26:48,163:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 19:27:35,763:INFO:Calculating mean and std
2021-07-18 19:27:35,766:INFO:Creating metrics dataframe
2021-07-18 19:27:35,794:INFO:Finalizing model
2021-07-18 19:27:52,773:INFO:Creating MLFlow logs
2021-07-18 19:27:52,872:INFO:Model: NeuralNetClassifier
2021-07-18 19:27:53,313:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 30, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7f3c08278e90>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c08299310>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c081ff250>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7f3c37140790>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7f3c374734d0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7f3c371405d0>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-18 19:27:54,818:INFO:Initializing predict_model()
2021-07-18 19:27:54,818:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-18 19:27:54,819:INFO:Checking exceptions
2021-07-18 19:27:54,819:INFO:Preloading libraries
2021-07-18 19:27:54,827:WARNING:Couldn't create holdout prediction for model, exception below:
2021-07-18 19:27:54,828:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 10139, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 8559, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-07-18 19:27:56,057:INFO:Uploading results into container
2021-07-18 19:27:56,057:INFO:Uploading model into container now
2021-07-18 19:27:56,090:INFO:create_model_container: 18
2021-07-18 19:27:56,091:INFO:master_model_container: 18
2021-07-18 19:27:56,091:INFO:display_container: 5
2021-07-18 19:27:56,094:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-18 19:27:56,095:INFO:create_model() succesfully completed......................................
2021-07-18 19:39:18,041:INFO:PyCaret Supervised Module
2021-07-18 19:39:18,042:INFO:ML Usecase: classification
2021-07-18 19:39:18,042:INFO:version 2.3.2
2021-07-18 19:39:18,043:INFO:Initializing setup()
2021-07-18 19:39:18,043:INFO:setup(target=stabf, 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=None, categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=10, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-07-18 19:39:18,043:INFO:Checking environment
2021-07-18 19:39:18,044:INFO:python_version: 3.7.6
2021-07-18 19:39:18,044:INFO:python_build: ('default', 'Jan  8 2020 19:59:22')
2021-07-18 19:39:18,044:INFO:machine: x86_64
2021-07-18 19:39:18,044:INFO:platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-debian-bullseye-sid
2021-07-18 19:39:18,045:INFO:Memory: svmem(total=13208035328, available=11995660288, percent=9.2, used=950837248, free=11628015616, active=1221390336, inactive=182181888, buffers=81154048, cached=548028416, shared=94208, slab=75296768)
2021-07-18 19:39:18,047:INFO:Physical Core: 6
2021-07-18 19:39:18,047:INFO:Logical Core: 12
2021-07-18 19:39:18,047:INFO:Checking libraries
2021-07-18 19:39:18,047:INFO:pd==1.2.4
2021-07-18 19:39:18,048:INFO:numpy==1.19.5
2021-07-18 19:39:18,048:INFO:sklearn==0.23.2
2021-07-18 19:39:18,048:INFO:lightgbm==3.2.1
2021-07-18 19:39:18,049:WARNING:catboost not found
2021-07-18 19:39:18,049:WARNING:xgboost not found
2021-07-18 19:39:18,050:INFO:mlflow==1.19.0
2021-07-18 19:39:18,050:INFO:Checking Exceptions
2021-07-18 19:39:18,050:INFO:Declaring global variables
2021-07-18 19:39:18,050:INFO:USI: 07b3
2021-07-18 19:39:18,051:INFO:pycaret_globals: {'X', 'iterative_imputation_iters_param', 'seed', '_all_metrics', '_ml_usecase', '_internal_pipeline', 'fold_groups_param', 'fold_groups_param_full', 'fix_imbalance_param', 'experiment__', 'logging_param', 'gpu_param', 'create_model_container', 'log_plots_param', '_all_models_internal', 'X_test', 'fold_generator', 'n_jobs_param', 'prep_pipe', 'USI', 'fold_shuffle_param', 'master_model_container', 'transform_target_param', 'imputation_regressor', 'data_before_preprocess', 'y_test', '_gpu_n_jobs_param', '_all_models', 'transform_target_method_param', 'stratify_param', 'imputation_classifier', 'exp_name_log', 'display_container', 'pycaret_globals', 'fold_param', 'html_param', 'X_train', 'fix_imbalance_method_param', 'y', 'y_train', 'target_param', '_available_plots'}
2021-07-18 19:39:18,051:INFO:Preparing display monitor
2021-07-18 19:39:18,051:INFO:Preparing display monitor
2021-07-18 19:39:18,063:INFO:Importing libraries
2021-07-18 19:39:18,064:INFO:Copying data for preprocessing
2021-07-18 19:39:18,077:INFO:Declaring preprocessing parameters
2021-07-18 19:39:18,081:INFO:Creating preprocessing pipeline
2021-07-18 19:39:18,100:INFO:Preprocessing pipeline created successfully
2021-07-18 19:39:18,100:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-07-18 19:39:18,100:INFO:Creating global containers
2021-07-18 19:39:18,101:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-07-18 19:39:18,326:WARNING:Couldn't import xgboost.XGBClassifier
2021-07-18 19:39:18,327:WARNING:Couldn't import catboost.CatBoostClassifier
2021-07-18 19:39:18,383:WARNING:Couldn't import xgboost.XGBClassifier
2021-07-18 19:39:18,384:WARNING:Couldn't import catboost.CatBoostClassifier
2021-07-18 19:39:18,385:INFO:Creating grid variables
2021-07-18 19:39:18,400:INFO:Logging experiment in MLFlow
2021-07-18 19:39:18,435:WARNING:Couldn't create mlflow experiment. Exception:
2021-07-18 19:39:18,436:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 1702, in setup
    mlflow.create_experiment(exp_name_log)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/tracking/fluent.py", line 875, in create_experiment
    return MlflowClient().create_experiment(name, artifact_location)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/tracking/client.py", line 500, in create_experiment
    return self._tracking_client.create_experiment(name, artifact_location)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/tracking/_tracking_service/client.py", line 167, in create_experiment
    return self.store.create_experiment(name=name, artifact_location=artifact_location,)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/store/tracking/file_store.py", line 313, in create_experiment
    self._validate_experiment_name(name)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/store/tracking/file_store.py", line 308, in _validate_experiment_name
    databricks_pb2.RESOURCE_ALREADY_EXISTS,
mlflow.exceptions.MlflowException: Experiment 'electrical_grid_1' already exists.

2021-07-18 19:39:19,461:INFO:SubProcess save_model() called ==================================
2021-07-18 19:39:19,468:INFO:Initializing save_model()
2021-07-18 19:39:19,468:INFO:save_model(model=Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('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=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False), verbose=False, kwargs={})
2021-07-18 19:39:19,469:INFO:Adding model into prep_pipe
2021-07-18 19:39:19,470:WARNING:Only Model saved as it was a pipeline.
2021-07-18 19:39:19,480:INFO:Transformation Pipeline.pkl saved in current working directory
2021-07-18 19:39:19,485:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-07-18 19:39:19,485:INFO:save_model() successfully completed......................................
2021-07-18 19:39:19,570:INFO:SubProcess save_model() end ==================================
2021-07-18 19:39:19,880:INFO:create_model_container: 0
2021-07-18 19:39:19,880:INFO:master_model_container: 0
2021-07-18 19:39:19,880:INFO:display_container: 1
2021-07-18 19:39:19,885:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-07-18 19:39:19,885:INFO:setup() succesfully completed......................................
2021-07-18 19:46:08,708:INFO:Initializing create_model()
2021-07-18 19:46:08,708:INFO:create_model(estimator=rf, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-07-18 19:46:08,709:INFO:Checking exceptions
2021-07-18 19:46:08,709:INFO:Preparing display monitor
2021-07-18 19:46:08,726:INFO:Importing libraries
2021-07-18 19:46:08,727:INFO:Copying training dataset
2021-07-18 19:46:08,728:INFO:Defining folds
2021-07-18 19:46:08,728:INFO:Declaring metric variables
2021-07-18 19:46:08,736:INFO:Importing untrained model
2021-07-18 19:46:08,744:INFO:Random Forest Classifier Imported succesfully
2021-07-18 19:46:08,758:INFO:Starting cross validation
2021-07-18 19:46:08,759:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 19:46:13,578:INFO:Calculating mean and std
2021-07-18 19:46:13,580:INFO:Creating metrics dataframe
2021-07-18 19:46:13,599:INFO:Finalizing model
2021-07-18 19:46:14,551:INFO:Creating MLFlow logs
2021-07-18 19:46:14,594:INFO:Model: Random Forest Classifier
2021-07-18 19:46:14,810: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}
2021-07-18 19:46:15,475:INFO:Initializing predict_model()
2021-07-18 19:46:15,475:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-18 19:46:15,475:INFO:Checking exceptions
2021-07-18 19:46:15,476:INFO:Preloading libraries
2021-07-18 19:46:16,185:INFO:Uploading results into container
2021-07-18 19:46:16,186:INFO:Uploading model into container now
2021-07-18 19:46:16,202:INFO:create_model_container: 1
2021-07-18 19:46:16,202:INFO:master_model_container: 1
2021-07-18 19:46:16,202:INFO:display_container: 2
2021-07-18 19:46:16,203: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)
2021-07-18 19:46:16,203:INFO:create_model() succesfully completed......................................
2021-07-18 19:46:43,956:INFO:PyCaret Supervised Module
2021-07-18 19:46:43,957:INFO:ML Usecase: classification
2021-07-18 19:46:43,957:INFO:version 2.3.2
2021-07-18 19:46:43,958:INFO:Initializing setup()
2021-07-18 19:46:43,958:INFO:setup(target=stabf, 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=None, categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-07-18 19:46:43,958:INFO:Checking environment
2021-07-18 19:46:43,959:INFO:python_version: 3.7.6
2021-07-18 19:46:43,959:INFO:python_build: ('default', 'Jan  8 2020 19:59:22')
2021-07-18 19:46:43,960:INFO:machine: x86_64
2021-07-18 19:46:43,960:INFO:platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-debian-bullseye-sid
2021-07-18 19:46:43,960:INFO:Memory: svmem(total=13208035328, available=10499166208, percent=20.5, used=2446991360, free=10129494016, active=2700050432, inactive=183238656, buffers=81903616, cached=549646336, shared=143360, slab=79925248)
2021-07-18 19:46:43,961:INFO:Physical Core: 6
2021-07-18 19:46:43,961:INFO:Logical Core: 12
2021-07-18 19:46:43,962:INFO:Checking libraries
2021-07-18 19:46:43,962:INFO:pd==1.2.4
2021-07-18 19:46:43,962:INFO:numpy==1.19.5
2021-07-18 19:46:43,962:INFO:sklearn==0.23.2
2021-07-18 19:46:43,963:INFO:lightgbm==3.2.1
2021-07-18 19:46:43,963:WARNING:catboost not found
2021-07-18 19:46:43,964:WARNING:xgboost not found
2021-07-18 19:46:43,964:INFO:mlflow==1.19.0
2021-07-18 19:46:43,965:INFO:Checking Exceptions
2021-07-18 19:46:43,965:INFO:Declaring global variables
2021-07-18 19:46:43,965:INFO:USI: 7c1e
2021-07-18 19:46:43,965:INFO:pycaret_globals: {'X', 'iterative_imputation_iters_param', 'seed', '_all_metrics', '_ml_usecase', '_internal_pipeline', 'fold_groups_param', 'fold_groups_param_full', 'fix_imbalance_param', 'experiment__', 'logging_param', 'gpu_param', 'create_model_container', 'log_plots_param', '_all_models_internal', 'X_test', 'fold_generator', 'n_jobs_param', 'prep_pipe', 'USI', 'fold_shuffle_param', 'master_model_container', 'transform_target_param', 'imputation_regressor', 'data_before_preprocess', 'y_test', '_gpu_n_jobs_param', '_all_models', 'transform_target_method_param', 'stratify_param', 'imputation_classifier', 'exp_name_log', 'display_container', 'pycaret_globals', 'fold_param', 'html_param', 'X_train', 'fix_imbalance_method_param', 'y', 'y_train', 'target_param', '_available_plots'}
2021-07-18 19:46:43,966:INFO:Preparing display monitor
2021-07-18 19:46:43,966:INFO:Preparing display monitor
2021-07-18 19:46:43,978:INFO:Importing libraries
2021-07-18 19:46:43,978:INFO:Copying data for preprocessing
2021-07-18 19:46:43,987:INFO:Declaring preprocessing parameters
2021-07-18 19:46:43,989:INFO:Creating preprocessing pipeline
2021-07-18 19:46:44,007:INFO:Preprocessing pipeline created successfully
2021-07-18 19:46:44,008:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-07-18 19:46:44,008:INFO:Creating global containers
2021-07-18 19:46:44,009:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-07-18 19:46:44,217:WARNING:Couldn't import xgboost.XGBClassifier
2021-07-18 19:46:44,218:WARNING:Couldn't import catboost.CatBoostClassifier
2021-07-18 19:46:44,274:WARNING:Couldn't import xgboost.XGBClassifier
2021-07-18 19:46:44,275:WARNING:Couldn't import catboost.CatBoostClassifier
2021-07-18 19:46:44,276:INFO:Creating grid variables
2021-07-18 19:46:44,291:INFO:Logging experiment in MLFlow
2021-07-18 19:46:44,328:WARNING:Couldn't create mlflow experiment. Exception:
2021-07-18 19:46:44,328:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 1702, in setup
    mlflow.create_experiment(exp_name_log)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/tracking/fluent.py", line 875, in create_experiment
    return MlflowClient().create_experiment(name, artifact_location)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/tracking/client.py", line 500, in create_experiment
    return self._tracking_client.create_experiment(name, artifact_location)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/tracking/_tracking_service/client.py", line 167, in create_experiment
    return self.store.create_experiment(name=name, artifact_location=artifact_location,)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/store/tracking/file_store.py", line 313, in create_experiment
    self._validate_experiment_name(name)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/mlflow/store/tracking/file_store.py", line 308, in _validate_experiment_name
    databricks_pb2.RESOURCE_ALREADY_EXISTS,
mlflow.exceptions.MlflowException: Experiment 'electrical_grid_1' already exists.

2021-07-18 19:46:45,372:INFO:SubProcess save_model() called ==================================
2021-07-18 19:46:45,381:INFO:Initializing save_model()
2021-07-18 19:46:45,381:INFO:save_model(model=Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('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=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False), verbose=False, kwargs={})
2021-07-18 19:46:45,381:INFO:Adding model into prep_pipe
2021-07-18 19:46:45,383:WARNING:Only Model saved as it was a pipeline.
2021-07-18 19:46:45,398:INFO:Transformation Pipeline.pkl saved in current working directory
2021-07-18 19:46:45,403:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-07-18 19:46:45,403:INFO:save_model() successfully completed......................................
2021-07-18 19:46:45,507:INFO:SubProcess save_model() end ==================================
2021-07-18 19:46:45,813:INFO:create_model_container: 0
2021-07-18 19:46:45,813:INFO:master_model_container: 0
2021-07-18 19:46:45,814:INFO:display_container: 1
2021-07-18 19:46:45,818:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-07-18 19:46:45,819:INFO:setup() succesfully completed......................................
2021-07-18 19:46:49,694:INFO:Initializing create_model()
2021-07-18 19:46:49,695:INFO:create_model(estimator=rf, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-07-18 19:46:49,695:INFO:Checking exceptions
2021-07-18 19:46:49,695:INFO:Preparing display monitor
2021-07-18 19:46:49,715:INFO:Importing libraries
2021-07-18 19:46:49,715:INFO:Copying training dataset
2021-07-18 19:46:49,716:INFO:Defining folds
2021-07-18 19:46:49,717:INFO:Declaring metric variables
2021-07-18 19:46:49,726:INFO:Importing untrained model
2021-07-18 19:46:49,736:INFO:Random Forest Classifier Imported succesfully
2021-07-18 19:46:49,760:INFO:Starting cross validation
2021-07-18 19:46:49,761:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 19:46:53,687:INFO:Calculating mean and std
2021-07-18 19:46:53,689:INFO:Creating metrics dataframe
2021-07-18 19:46:53,707:INFO:Finalizing model
2021-07-18 19:46:54,887:INFO:Creating MLFlow logs
2021-07-18 19:46:54,933:INFO:Model: Random Forest Classifier
2021-07-18 19:46:55,180: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}
2021-07-18 19:46:55,949:INFO:Initializing predict_model()
2021-07-18 19:46:55,949:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-18 19:46:55,950:INFO:Checking exceptions
2021-07-18 19:46:55,950:INFO:Preloading libraries
2021-07-18 19:46:56,727:INFO:Uploading results into container
2021-07-18 19:46:56,728:INFO:Uploading model into container now
2021-07-18 19:46:56,745:INFO:create_model_container: 1
2021-07-18 19:46:56,745:INFO:master_model_container: 1
2021-07-18 19:46:56,746:INFO:display_container: 2
2021-07-18 19:46:56,746: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)
2021-07-18 19:46:56,747:INFO:create_model() succesfully completed......................................
2021-07-18 19:51:37,799:INFO:Initializing create_model()
2021-07-18 19:51:37,799:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-07-18 19:51:37,800:INFO:Checking exceptions
2021-07-18 19:51:37,800:INFO:Preparing display monitor
2021-07-18 19:51:37,819:INFO:Importing libraries
2021-07-18 19:51:37,819:INFO:Copying training dataset
2021-07-18 19:51:37,820:INFO:Defining folds
2021-07-18 19:51:37,820:INFO:Declaring metric variables
2021-07-18 19:51:37,829:INFO:Importing untrained model
2021-07-18 19:51:37,829:INFO:Declaring custom model
2021-07-18 19:51:37,841:INFO:NeuralNetClassifier Imported succesfully
2021-07-18 19:51:37,860:INFO:Starting cross validation
2021-07-18 19:51:37,861:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 19:51:47,200:INFO:Calculating mean and std
2021-07-18 19:51:47,201:INFO:Creating metrics dataframe
2021-07-18 19:51:47,212:INFO:Finalizing model
2021-07-18 19:51:56,421:INFO:Creating MLFlow logs
2021-07-18 19:51:56,463:INFO:Model: NeuralNetClassifier
2021-07-18 19:51:56,678:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 30, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7f3c08225c10>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c08256150>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3cb8fb83d0>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7f3c371ab110>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7f3c374734d0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7f3b79c648d0>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-18 19:51:57,554:INFO:Initializing predict_model()
2021-07-18 19:51:57,554:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-18 19:51:57,554:INFO:Checking exceptions
2021-07-18 19:51:57,554:INFO:Preloading libraries
2021-07-18 19:51:57,558:WARNING:Couldn't create holdout prediction for model, exception below:
2021-07-18 19:51:57,558:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 10139, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 8559, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-07-18 19:51:57,868:INFO:Uploading results into container
2021-07-18 19:51:57,869:INFO:Uploading model into container now
2021-07-18 19:51:57,886:INFO:create_model_container: 2
2021-07-18 19:51:57,886:INFO:master_model_container: 2
2021-07-18 19:51:57,886:INFO:display_container: 3
2021-07-18 19:51:57,888:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-18 19:51:57,888:INFO:create_model() succesfully completed......................................
2021-07-18 19:52:08,201:INFO:Initializing create_model()
2021-07-18 19:52:08,202:INFO:create_model(estimator=rf, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-07-18 19:52:08,203:INFO:Checking exceptions
2021-07-18 19:52:08,204:INFO:Preparing display monitor
2021-07-18 19:52:08,224:INFO:Importing libraries
2021-07-18 19:52:08,224:INFO:Copying training dataset
2021-07-18 19:52:08,225:INFO:Defining folds
2021-07-18 19:52:08,226:INFO:Declaring metric variables
2021-07-18 19:52:08,234:INFO:Importing untrained model
2021-07-18 19:52:08,242:INFO:Random Forest Classifier Imported succesfully
2021-07-18 19:52:08,257:INFO:Starting cross validation
2021-07-18 19:52:08,258:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 19:52:11,361:INFO:Calculating mean and std
2021-07-18 19:52:11,363:INFO:Creating metrics dataframe
2021-07-18 19:52:11,375:INFO:Finalizing model
2021-07-18 19:52:12,229:INFO:Creating MLFlow logs
2021-07-18 19:52:12,292:INFO:Model: Random Forest Classifier
2021-07-18 19:52:12,507: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}
2021-07-18 19:52:13,146:INFO:Initializing predict_model()
2021-07-18 19:52:13,147:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-18 19:52:13,147:INFO:Checking exceptions
2021-07-18 19:52:13,148:INFO:Preloading libraries
2021-07-18 19:52:13,805:INFO:Uploading results into container
2021-07-18 19:52:13,805:INFO:Uploading model into container now
2021-07-18 19:52:13,819:INFO:create_model_container: 3
2021-07-18 19:52:13,819:INFO:master_model_container: 3
2021-07-18 19:52:13,820:INFO:display_container: 4
2021-07-18 19:52:13,820: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)
2021-07-18 19:52:13,820:INFO:create_model() succesfully completed......................................
2021-07-18 19:56:32,332:INFO:Initializing compare_models()
2021-07-18 19:56:32,332:INFO:compare_models(include=[Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 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)], fold=None, round=4, cross_validation=True, sort=AUC, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-07-18 19:56:32,333:INFO:Checking exceptions
2021-07-18 19:56:32,334:INFO:Preparing display monitor
2021-07-18 19:56:32,334:INFO:Preparing display monitor
2021-07-18 19:56:32,359:INFO:Initializing custom model NeuralNetClassifier
2021-07-18 19:56:32,359:INFO:Total runtime is 1.18255615234375e-05 minutes
2021-07-18 19:56:32,368:INFO:SubProcess create_model() called ==================================
2021-07-18 19:56:32,369:INFO:Initializing create_model()
2021-07-18 19:56:32,370:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 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, display=<pycaret.internal.Display.Display object at 0x7f3c3712ce10>, kwargs={})
2021-07-18 19:56:32,370:INFO:Checking exceptions
2021-07-18 19:56:32,370:INFO:Importing libraries
2021-07-18 19:56:32,370:INFO:Copying training dataset
2021-07-18 19:56:32,371:INFO:Defining folds
2021-07-18 19:56:32,371:INFO:Declaring metric variables
2021-07-18 19:56:32,378:INFO:Importing untrained model
2021-07-18 19:56:32,378:INFO:Declaring custom model
2021-07-18 19:56:32,387:INFO:NeuralNetClassifier Imported succesfully
2021-07-18 19:56:32,403:INFO:Starting cross validation
2021-07-18 19:56:32,404:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 19:56:42,325:INFO:Calculating mean and std
2021-07-18 19:56:42,327:INFO:Creating metrics dataframe
2021-07-18 19:56:42,334:INFO:Uploading results into container
2021-07-18 19:56:42,334:INFO:Uploading model into container now
2021-07-18 19:56:42,335:INFO:create_model_container: 4
2021-07-18 19:56:42,335:INFO:master_model_container: 4
2021-07-18 19:56:42,336:INFO:display_container: 5
2021-07-18 19:56:42,338:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False)
2021-07-18 19:56:42,338:INFO:create_model() succesfully completed......................................
2021-07-18 19:56:42,447:INFO:SubProcess create_model() end ==================================
2021-07-18 19:56:42,448:INFO:Creating metrics dataframe
2021-07-18 19:56:42,465:INFO:Initializing custom model Random Forest Classifier
2021-07-18 19:56:42,466:INFO:Total runtime is 0.1684606432914734 minutes
2021-07-18 19:56:42,476:INFO:SubProcess create_model() called ==================================
2021-07-18 19:56:42,476:INFO:Initializing create_model()
2021-07-18 19:56:42,477:INFO:create_model(estimator=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), 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, display=<pycaret.internal.Display.Display object at 0x7f3c3712ce10>, kwargs={})
2021-07-18 19:56:42,477:INFO:Checking exceptions
2021-07-18 19:56:42,477:INFO:Importing libraries
2021-07-18 19:56:42,478:INFO:Copying training dataset
2021-07-18 19:56:42,479:INFO:Defining folds
2021-07-18 19:56:42,479:INFO:Declaring metric variables
2021-07-18 19:56:42,487:INFO:Importing untrained model
2021-07-18 19:56:42,487:INFO:Declaring custom model
2021-07-18 19:56:42,496:INFO:Random Forest Classifier Imported succesfully
2021-07-18 19:56:42,512:INFO:Starting cross validation
2021-07-18 19:56:42,513:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 19:56:44,344:INFO:Calculating mean and std
2021-07-18 19:56:44,345:INFO:Creating metrics dataframe
2021-07-18 19:56:44,351:INFO:Uploading results into container
2021-07-18 19:56:44,351:INFO:Uploading model into container now
2021-07-18 19:56:44,351:INFO:create_model_container: 5
2021-07-18 19:56:44,351:INFO:master_model_container: 5
2021-07-18 19:56:44,352:INFO:display_container: 5
2021-07-18 19:56:44,353: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)
2021-07-18 19:56:44,353:INFO:create_model() succesfully completed......................................
2021-07-18 19:56:44,442:INFO:SubProcess create_model() end ==================================
2021-07-18 19:56:44,442:INFO:Creating metrics dataframe
2021-07-18 19:56:44,481:INFO:Initializing create_model()
2021-07-18 19:56:44,482:INFO:create_model(estimator=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), 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, display=None, kwargs={})
2021-07-18 19:56:44,482:INFO:Checking exceptions
2021-07-18 19:56:44,482:INFO:Importing libraries
2021-07-18 19:56:44,483:INFO:Copying training dataset
2021-07-18 19:56:44,483:INFO:Defining folds
2021-07-18 19:56:44,484:INFO:Declaring metric variables
2021-07-18 19:56:44,484:INFO:Importing untrained model
2021-07-18 19:56:44,484:INFO:Declaring custom model
2021-07-18 19:56:44,485:INFO:Random Forest Classifier Imported succesfully
2021-07-18 19:56:44,485:INFO:Cross validation set to False
2021-07-18 19:56:44,486:INFO:Fitting Model
2021-07-18 19:56:45,354: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)
2021-07-18 19:56:45,356:INFO:create_models() succesfully completed......................................
2021-07-18 19:56:45,447:INFO:Creating MLFlow logs
2021-07-18 19:56:45,494:INFO:Model: Random Forest Classifier
2021-07-18 19:56:45,711: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}
2021-07-18 19:56:46,335:INFO:Initializing predict_model()
2021-07-18 19:56:46,335:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-18 19:56:46,335:INFO:Checking exceptions
2021-07-18 19:56:46,336:INFO:Preloading libraries
2021-07-18 19:56:47,009:INFO:Creating MLFlow logs
2021-07-18 19:56:47,053:INFO:Model: NeuralNetClassifier
2021-07-18 19:56:47,276:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 30, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7f3c37409490>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c3739c4d0>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c081665d0>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7f3c08256150>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7f3c374734d0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7f3cb9024890>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-18 19:56:48,236:INFO:create_model_container: 5
2021-07-18 19:56:48,236:INFO:master_model_container: 5
2021-07-18 19:56:48,237:INFO:display_container: 5
2021-07-18 19:56:48,238: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)
2021-07-18 19:56:48,238:INFO:compare_models() succesfully completed......................................
2021-07-18 20:09:46,892:INFO:Initializing tune_model()
2021-07-18 20:09:46,893:INFO:tune_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=None, round=4, n_iter=10, custom_grid={'nn__max_epochs': [10, 20], 'nn__lr': [0.1, 0.01], 'nn__module__num_units': [5, 10], 'nn__module__dropout': [0.1, 0.5], 'nn__optimizer': [<class 'torch.optim.adam.Adam'>, <class 'torch.optim.sgd.SGD'>, <class 'torch.optim.rmsprop.RMSprop'>]}, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=False, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, display=None, kwargs={})
2021-07-18 20:09:46,893:INFO:Checking exceptions
2021-07-18 20:09:46,894:INFO:Preparing display monitor
2021-07-18 20:09:46,910:INFO:Copying training dataset
2021-07-18 20:09:46,911:INFO:Checking base model
2021-07-18 20:09:46,913:INFO:A custom model has been passed
2021-07-18 20:09:46,913:INFO:Base model : NeuralNetClassifier
2021-07-18 20:09:46,921:INFO:Declaring metric variables
2021-07-18 20:09:46,927:INFO:Defining Hyperparameters
2021-07-18 20:09:47,028:INFO:custom_grid: {'actual_estimator__nn__max_epochs': [10, 20], 'actual_estimator__nn__lr': [0.1, 0.01], 'actual_estimator__nn__module__num_units': [5, 10], 'actual_estimator__nn__module__dropout': [0.1, 0.5], 'actual_estimator__nn__optimizer': [<class 'torch.optim.adam.Adam'>, <class 'torch.optim.sgd.SGD'>, <class 'torch.optim.rmsprop.RMSprop'>]}
2021-07-18 20:09:47,028:INFO:Tuning with n_jobs=-1
2021-07-18 20:09:47,028:INFO:Initializing RandomizedSearchCV
2021-07-18 20:11:20,048:INFO:Initializing tune_model()
2021-07-18 20:11:20,049:INFO:tune_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=None, round=4, n_iter=10, custom_grid={'net__max_epochs': [10, 20], 'net__lr': [0.01, 0.05, 0.1], 'net__module__num_units_d1': [50, 100, 150], 'net__module__num_units_d2': [50, 100, 150], 'net__optimizer': [<class 'torch.optim.adam.Adam'>, <class 'torch.optim.sgd.SGD'>, <class 'torch.optim.rmsprop.RMSprop'>]}, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=False, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, display=None, kwargs={})
2021-07-18 20:11:20,050:INFO:Checking exceptions
2021-07-18 20:11:20,052:INFO:Preparing display monitor
2021-07-18 20:11:20,073:INFO:Copying training dataset
2021-07-18 20:11:20,074:INFO:Checking base model
2021-07-18 20:11:20,075:INFO:A custom model has been passed
2021-07-18 20:11:20,076:INFO:Base model : NeuralNetClassifier
2021-07-18 20:11:20,086:INFO:Declaring metric variables
2021-07-18 20:11:20,096:INFO:Defining Hyperparameters
2021-07-18 20:11:20,216:INFO:custom_grid: {'actual_estimator__net__max_epochs': [10, 20], 'actual_estimator__net__lr': [0.01, 0.05, 0.1], 'actual_estimator__net__module__num_units_d1': [50, 100, 150], 'actual_estimator__net__module__num_units_d2': [50, 100, 150], 'actual_estimator__net__optimizer': [<class 'torch.optim.adam.Adam'>, <class 'torch.optim.sgd.SGD'>, <class 'torch.optim.rmsprop.RMSprop'>]}
2021-07-18 20:11:20,217:INFO:Tuning with n_jobs=-1
2021-07-18 20:11:20,218:INFO:Initializing RandomizedSearchCV
2021-07-18 20:13:20,520:INFO:best_params: {'actual_estimator__net__optimizer': <class 'torch.optim.sgd.SGD'>, 'actual_estimator__net__module__num_units_d2': 150, 'actual_estimator__net__module__num_units_d1': 150, 'actual_estimator__net__max_epochs': 20, 'actual_estimator__net__lr': 0.05}
2021-07-18 20:13:20,521:INFO:Hyperparameter search completed
2021-07-18 20:13:20,522:INFO:SubProcess create_model() called ==================================
2021-07-18 20:13:20,524:INFO:Initializing create_model()
2021-07-18 20:13:20,525:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, verbose=True, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c08156d90>, kwargs={'net__optimizer': <class 'torch.optim.sgd.SGD'>, 'net__module__num_units_d2': 150, 'net__module__num_units_d1': 150, 'net__max_epochs': 20, 'net__lr': 0.05})
2021-07-18 20:13:20,526:INFO:Checking exceptions
2021-07-18 20:13:20,527:INFO:Importing libraries
2021-07-18 20:13:20,527:INFO:Copying training dataset
2021-07-18 20:13:20,530:INFO:Defining folds
2021-07-18 20:13:20,531:INFO:Declaring metric variables
2021-07-18 20:13:20,549:INFO:Importing untrained model
2021-07-18 20:13:20,550:INFO:Declaring custom model
2021-07-18 20:13:20,568:INFO:NeuralNetClassifier Imported succesfully
2021-07-18 20:13:20,600:INFO:Starting cross validation
2021-07-18 20:13:20,601:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 20:13:33,782:INFO:Calculating mean and std
2021-07-18 20:13:33,785:INFO:Creating metrics dataframe
2021-07-18 20:13:33,810:INFO:Finalizing model
2021-07-18 20:13:46,589:INFO:Uploading results into container
2021-07-18 20:13:46,589:INFO:Uploading model into container now
2021-07-18 20:13:46,590:INFO:create_model_container: 6
2021-07-18 20:13:46,590:INFO:master_model_container: 6
2021-07-18 20:13:46,591:INFO:display_container: 6
2021-07-18 20:13:46,594:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=150, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=150, out_features=150, bias=True)
    (output): Linear(in_features=150, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-18 20:13:46,594:INFO:create_model() succesfully completed......................................
2021-07-18 20:13:46,742:INFO:SubProcess create_model() end ==================================
2021-07-18 20:13:46,743:INFO:Creating MLFlow logs
2021-07-18 20:13:46,828:INFO:Model: NeuralNetClassifier
2021-07-18 20:13:47,231:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.05, 'max_epochs': 20, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'module__num_units_d2': 150, 'module__num_units_d1': 150, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7f3b79c64050>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c0815f390>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c0815f310>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7f3c0815f790>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7f3c374734d0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7f3c0813a5d0>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-18 20:13:48,719:INFO:Initializing predict_model()
2021-07-18 20:13:48,720:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=150, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=150, out_features=150, bias=True)
    (output): Linear(in_features=150, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-18 20:13:48,720:INFO:Checking exceptions
2021-07-18 20:13:48,721:INFO:Preloading libraries
2021-07-18 20:13:48,727:WARNING:Couldn't create holdout prediction for model, exception below:
2021-07-18 20:13:48,727:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 10139, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 8559, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-07-18 20:13:49,176:INFO:create_model_container: 6
2021-07-18 20:13:49,177:INFO:master_model_container: 6
2021-07-18 20:13:49,177:INFO:display_container: 6
2021-07-18 20:13:49,179:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=150, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=150, out_features=150, bias=True)
    (output): Linear(in_features=150, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-18 20:13:49,180:INFO:tune_model() succesfully completed......................................
2021-07-18 20:17:34,578:INFO:Initializing tune_model()
2021-07-18 20:17:34,579:INFO:tune_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=None, round=4, n_iter=10, custom_grid={'net__max_epochs': [10, 20], 'net__lr': [0.01, 0.05, 0.1], 'net__module__num_units_d1': [50, 100, 150], 'net__module__num_units_d2': [50, 100, 150], 'net__optimizer': [<class 'torch.optim.adam.Adam'>, <class 'torch.optim.sgd.SGD'>, <class 'torch.optim.rmsprop.RMSprop'>]}, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=False, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, display=None, kwargs={})
2021-07-18 20:17:34,579:INFO:Checking exceptions
2021-07-18 20:17:34,581:INFO:Preparing display monitor
2021-07-18 20:17:34,607:INFO:Copying training dataset
2021-07-18 20:17:34,608:INFO:Checking base model
2021-07-18 20:17:34,609:INFO:A custom model has been passed
2021-07-18 20:17:34,610:INFO:Base model : NeuralNetClassifier
2021-07-18 20:17:34,618:INFO:Declaring metric variables
2021-07-18 20:17:34,628:INFO:Defining Hyperparameters
2021-07-18 20:17:34,737:INFO:custom_grid: {'actual_estimator__net__max_epochs': [10, 20], 'actual_estimator__net__lr': [0.01, 0.05, 0.1], 'actual_estimator__net__module__num_units_d1': [50, 100, 150], 'actual_estimator__net__module__num_units_d2': [50, 100, 150], 'actual_estimator__net__optimizer': [<class 'torch.optim.adam.Adam'>, <class 'torch.optim.sgd.SGD'>, <class 'torch.optim.rmsprop.RMSprop'>]}
2021-07-18 20:17:34,738:INFO:Tuning with n_jobs=-1
2021-07-18 20:17:34,738:INFO:Initializing RandomizedSearchCV
2021-07-18 20:19:03,372:INFO:best_params: {'actual_estimator__net__optimizer': <class 'torch.optim.sgd.SGD'>, 'actual_estimator__net__module__num_units_d2': 150, 'actual_estimator__net__module__num_units_d1': 150, 'actual_estimator__net__max_epochs': 20, 'actual_estimator__net__lr': 0.05}
2021-07-18 20:19:03,374:INFO:Hyperparameter search completed
2021-07-18 20:19:03,374:INFO:SubProcess create_model() called ==================================
2021-07-18 20:19:03,376:INFO:Initializing create_model()
2021-07-18 20:19:03,376:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, verbose=True, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c08feb410>, kwargs={'net__optimizer': <class 'torch.optim.sgd.SGD'>, 'net__module__num_units_d2': 150, 'net__module__num_units_d1': 150, 'net__max_epochs': 20, 'net__lr': 0.05})
2021-07-18 20:19:03,377:INFO:Checking exceptions
2021-07-18 20:19:03,377:INFO:Importing libraries
2021-07-18 20:19:03,378:INFO:Copying training dataset
2021-07-18 20:19:03,379:INFO:Defining folds
2021-07-18 20:19:03,380:INFO:Declaring metric variables
2021-07-18 20:19:03,393:INFO:Importing untrained model
2021-07-18 20:19:03,393:INFO:Declaring custom model
2021-07-18 20:19:03,409:INFO:NeuralNetClassifier Imported succesfully
2021-07-18 20:19:03,433:INFO:Starting cross validation
2021-07-18 20:19:03,434:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 20:19:14,648:INFO:Calculating mean and std
2021-07-18 20:19:14,650:INFO:Creating metrics dataframe
2021-07-18 20:19:14,668:INFO:Finalizing model
2021-07-18 20:19:25,547:INFO:Uploading results into container
2021-07-18 20:19:25,547:INFO:Uploading model into container now
2021-07-18 20:19:25,548:INFO:create_model_container: 7
2021-07-18 20:19:25,548:INFO:master_model_container: 7
2021-07-18 20:19:25,548:INFO:display_container: 7
2021-07-18 20:19:25,552:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=150, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=150, out_features=150, bias=True)
    (output): Linear(in_features=150, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-18 20:19:25,552:INFO:create_model() succesfully completed......................................
2021-07-18 20:19:25,679:INFO:SubProcess create_model() end ==================================
2021-07-18 20:19:25,680:INFO:Creating MLFlow logs
2021-07-18 20:19:25,754:INFO:Model: NeuralNetClassifier
2021-07-18 20:19:26,123:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.05, 'max_epochs': 20, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'module__num_units_d2': 150, 'module__num_units_d1': 150, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7f3c08131510>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3b79ce6cd0>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c082fba90>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7f3c11560290>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7f3c374734d0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7f3b79c76a90>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-18 20:19:27,407:INFO:Initializing predict_model()
2021-07-18 20:19:27,408:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=150, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=150, out_features=150, bias=True)
    (output): Linear(in_features=150, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-18 20:19:27,408:INFO:Checking exceptions
2021-07-18 20:19:27,409:INFO:Preloading libraries
2021-07-18 20:19:27,413:WARNING:Couldn't create holdout prediction for model, exception below:
2021-07-18 20:19:27,414:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 10139, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 8559, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-07-18 20:19:27,843:INFO:create_model_container: 7
2021-07-18 20:19:27,844:INFO:master_model_container: 7
2021-07-18 20:19:27,844:INFO:display_container: 7
2021-07-18 20:19:27,846:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=150, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=150, out_features=150, bias=True)
    (output): Linear(in_features=150, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-18 20:19:27,846:INFO:tune_model() succesfully completed......................................
2021-07-18 20:21:12,896:INFO:Initializing compare_models()
2021-07-18 20:21:12,896:INFO:compare_models(include=[Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=150, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=150, out_features=150, bias=True)
    (output): Linear(in_features=150, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 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)], fold=None, round=4, cross_validation=True, sort=AUC, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-07-18 20:21:12,897:INFO:Checking exceptions
2021-07-18 20:21:12,898:INFO:Preparing display monitor
2021-07-18 20:21:12,898:INFO:Preparing display monitor
2021-07-18 20:21:12,923:INFO:Initializing custom model NeuralNetClassifier
2021-07-18 20:21:12,924:INFO:Total runtime is 1.1940797170003256e-05 minutes
2021-07-18 20:21:12,933:INFO:SubProcess create_model() called ==================================
2021-07-18 20:21:12,935:INFO:Initializing create_model()
2021-07-18 20:21:12,935:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=150, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=150, out_features=150, bias=True)
    (output): Linear(in_features=150, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 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, display=<pycaret.internal.Display.Display object at 0x7f3b79918390>, kwargs={})
2021-07-18 20:21:12,935:INFO:Checking exceptions
2021-07-18 20:21:12,935:INFO:Importing libraries
2021-07-18 20:21:12,936:INFO:Copying training dataset
2021-07-18 20:21:12,937:INFO:Defining folds
2021-07-18 20:21:12,937:INFO:Declaring metric variables
2021-07-18 20:21:12,945:INFO:Importing untrained model
2021-07-18 20:21:12,945:INFO:Declaring custom model
2021-07-18 20:21:12,954:INFO:NeuralNetClassifier Imported succesfully
2021-07-18 20:21:12,970:INFO:Starting cross validation
2021-07-18 20:21:12,971:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 20:21:23,992:INFO:Calculating mean and std
2021-07-18 20:21:23,994:INFO:Creating metrics dataframe
2021-07-18 20:21:24,001:INFO:Uploading results into container
2021-07-18 20:21:24,002:INFO:Uploading model into container now
2021-07-18 20:21:24,003:INFO:create_model_container: 8
2021-07-18 20:21:24,003:INFO:master_model_container: 8
2021-07-18 20:21:24,004:INFO:display_container: 8
2021-07-18 20:21:24,005:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
  module__num_units_d1=150,
  module__num_units_d2=150,
))],
         verbose=False)
2021-07-18 20:21:24,005:INFO:create_model() succesfully completed......................................
2021-07-18 20:21:24,122:INFO:SubProcess create_model() end ==================================
2021-07-18 20:21:24,123:INFO:Creating metrics dataframe
2021-07-18 20:21:24,146:INFO:Initializing custom model NeuralNetClassifier
2021-07-18 20:21:24,147:INFO:Total runtime is 0.18706013361612955 minutes
2021-07-18 20:21:24,161:INFO:SubProcess create_model() called ==================================
2021-07-18 20:21:24,164:INFO:Initializing create_model()
2021-07-18 20:21:24,164:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 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, display=<pycaret.internal.Display.Display object at 0x7f3b79918390>, kwargs={})
2021-07-18 20:21:24,164:INFO:Checking exceptions
2021-07-18 20:21:24,165:INFO:Importing libraries
2021-07-18 20:21:24,165:INFO:Copying training dataset
2021-07-18 20:21:24,167:INFO:Defining folds
2021-07-18 20:21:24,167:INFO:Declaring metric variables
2021-07-18 20:21:24,181:INFO:Importing untrained model
2021-07-18 20:21:24,182:INFO:Declaring custom model
2021-07-18 20:21:24,196:INFO:NeuralNetClassifier Imported succesfully
2021-07-18 20:21:24,221:INFO:Starting cross validation
2021-07-18 20:21:24,222:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 20:21:40,156:INFO:Calculating mean and std
2021-07-18 20:21:40,158:INFO:Creating metrics dataframe
2021-07-18 20:21:40,165:INFO:Uploading results into container
2021-07-18 20:21:40,166:INFO:Uploading model into container now
2021-07-18 20:21:40,167:INFO:create_model_container: 9
2021-07-18 20:21:40,167:INFO:master_model_container: 9
2021-07-18 20:21:40,168:INFO:display_container: 8
2021-07-18 20:21:40,170:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False)
2021-07-18 20:21:40,170:INFO:create_model() succesfully completed......................................
2021-07-18 20:21:40,293:INFO:SubProcess create_model() end ==================================
2021-07-18 20:21:40,294:INFO:Creating metrics dataframe
2021-07-18 20:21:40,322:INFO:Initializing custom model Random Forest Classifier
2021-07-18 20:21:40,322:INFO:Total runtime is 0.4566534360249837 minutes
2021-07-18 20:21:40,337:INFO:SubProcess create_model() called ==================================
2021-07-18 20:21:40,338:INFO:Initializing create_model()
2021-07-18 20:21:40,338:INFO:create_model(estimator=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), 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, display=<pycaret.internal.Display.Display object at 0x7f3b79918390>, kwargs={})
2021-07-18 20:21:40,339:INFO:Checking exceptions
2021-07-18 20:21:40,339:INFO:Importing libraries
2021-07-18 20:21:40,340:INFO:Copying training dataset
2021-07-18 20:21:40,341:INFO:Defining folds
2021-07-18 20:21:40,342:INFO:Declaring metric variables
2021-07-18 20:21:40,356:INFO:Importing untrained model
2021-07-18 20:21:40,356:INFO:Declaring custom model
2021-07-18 20:21:40,370:INFO:Random Forest Classifier Imported succesfully
2021-07-18 20:21:40,396:INFO:Starting cross validation
2021-07-18 20:21:40,397:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 20:21:43,287:INFO:Calculating mean and std
2021-07-18 20:21:43,289:INFO:Creating metrics dataframe
2021-07-18 20:21:43,296:INFO:Uploading results into container
2021-07-18 20:21:43,297:INFO:Uploading model into container now
2021-07-18 20:21:43,297:INFO:create_model_container: 10
2021-07-18 20:21:43,298:INFO:master_model_container: 10
2021-07-18 20:21:43,298:INFO:display_container: 8
2021-07-18 20:21:43,299: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)
2021-07-18 20:21:43,300:INFO:create_model() succesfully completed......................................
2021-07-18 20:21:43,417:INFO:SubProcess create_model() end ==================================
2021-07-18 20:21:43,418:INFO:Creating metrics dataframe
2021-07-18 20:21:43,475:INFO:Initializing create_model()
2021-07-18 20:21:43,475:INFO:create_model(estimator=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), 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, display=None, kwargs={})
2021-07-18 20:21:43,476:INFO:Checking exceptions
2021-07-18 20:21:43,476:INFO:Importing libraries
2021-07-18 20:21:43,477:INFO:Copying training dataset
2021-07-18 20:21:43,478:INFO:Defining folds
2021-07-18 20:21:43,478:INFO:Declaring metric variables
2021-07-18 20:21:43,479:INFO:Importing untrained model
2021-07-18 20:21:43,479:INFO:Declaring custom model
2021-07-18 20:21:43,480:INFO:Random Forest Classifier Imported succesfully
2021-07-18 20:21:43,481:INFO:Cross validation set to False
2021-07-18 20:21:43,482:INFO:Fitting Model
2021-07-18 20:21:44,688: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)
2021-07-18 20:21:44,688:INFO:create_models() succesfully completed......................................
2021-07-18 20:21:44,794:INFO:Creating MLFlow logs
2021-07-18 20:21:44,861:INFO:Model: Random Forest Classifier
2021-07-18 20:21:45,171: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}
2021-07-18 20:21:46,042:INFO:Initializing predict_model()
2021-07-18 20:21:46,043:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-18 20:21:46,043:INFO:Checking exceptions
2021-07-18 20:21:46,043:INFO:Preloading libraries
2021-07-18 20:21:46,815:INFO:Creating MLFlow logs
2021-07-18 20:21:46,860:INFO:Model: NeuralNetClassifier
2021-07-18 20:21:47,081:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.05, 'max_epochs': 20, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'module__num_units_d2': 150, 'module__num_units_d1': 150, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7f3b797f5d50>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c08feb690>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c3712ca10>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7f3c371f0a90>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7f3c374734d0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7f3b73b1ee50>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-18 20:21:48,359:INFO:Creating MLFlow logs
2021-07-18 20:21:48,407:INFO:Model: NeuralNetClassifier
2021-07-18 20:21:48,646:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 30, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7f3b797ff390>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3b79918790>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3b79918490>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7f3c43845d50>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7f3c374734d0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7f3b797f5d50>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-18 20:21:50,035:INFO:create_model_container: 10
2021-07-18 20:21:50,035:INFO:master_model_container: 10
2021-07-18 20:21:50,036:INFO:display_container: 8
2021-07-18 20:21:50,037: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)
2021-07-18 20:21:50,037:INFO:compare_models() succesfully completed......................................
2021-07-18 20:28:52,190:INFO:Initializing tune_model()
2021-07-18 20:28:52,191:INFO:tune_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=None, round=4, n_iter=10, custom_grid={'net__max_epochs': [20, 30], 'net__lr': [0.01, 0.05, 0.1], 'net__module__num_units_d1': [50, 100, 150, 200], 'net__module__num_units_d2': [50, 100, 150, 200], 'net__optimizer': [<class 'torch.optim.adam.Adam'>, <class 'torch.optim.sgd.SGD'>, <class 'torch.optim.rmsprop.RMSprop'>]}, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=False, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, display=None, kwargs={})
2021-07-18 20:28:52,191:INFO:Checking exceptions
2021-07-18 20:28:52,193:INFO:Preparing display monitor
2021-07-18 20:28:52,210:INFO:Copying training dataset
2021-07-18 20:28:52,211:INFO:Checking base model
2021-07-18 20:28:52,212:INFO:A custom model has been passed
2021-07-18 20:28:52,212:INFO:Base model : NeuralNetClassifier
2021-07-18 20:28:52,221:INFO:Declaring metric variables
2021-07-18 20:28:52,228:INFO:Defining Hyperparameters
2021-07-18 20:28:52,336:INFO:custom_grid: {'actual_estimator__net__max_epochs': [20, 30], 'actual_estimator__net__lr': [0.01, 0.05, 0.1], 'actual_estimator__net__module__num_units_d1': [50, 100, 150, 200], 'actual_estimator__net__module__num_units_d2': [50, 100, 150, 200], 'actual_estimator__net__optimizer': [<class 'torch.optim.adam.Adam'>, <class 'torch.optim.sgd.SGD'>, <class 'torch.optim.rmsprop.RMSprop'>]}
2021-07-18 20:28:52,337:INFO:Tuning with n_jobs=-1
2021-07-18 20:28:52,337:INFO:Initializing RandomizedSearchCV
2021-07-18 20:29:47,364:INFO:best_params: {'actual_estimator__net__optimizer': <class 'torch.optim.sgd.SGD'>, 'actual_estimator__net__module__num_units_d2': 200, 'actual_estimator__net__module__num_units_d1': 100, 'actual_estimator__net__max_epochs': 20, 'actual_estimator__net__lr': 0.1}
2021-07-18 20:29:47,365:INFO:Hyperparameter search completed
2021-07-18 20:29:47,366:INFO:SubProcess create_model() called ==================================
2021-07-18 20:29:47,368:INFO:Initializing create_model()
2021-07-18 20:29:47,368:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, verbose=True, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x7f3c371cad90>, kwargs={'net__optimizer': <class 'torch.optim.sgd.SGD'>, 'net__module__num_units_d2': 200, 'net__module__num_units_d1': 100, 'net__max_epochs': 20, 'net__lr': 0.1})
2021-07-18 20:29:47,368:INFO:Checking exceptions
2021-07-18 20:29:47,369:INFO:Importing libraries
2021-07-18 20:29:47,369:INFO:Copying training dataset
2021-07-18 20:29:47,370:INFO:Defining folds
2021-07-18 20:29:47,370:INFO:Declaring metric variables
2021-07-18 20:29:47,379:INFO:Importing untrained model
2021-07-18 20:29:47,379:INFO:Declaring custom model
2021-07-18 20:29:47,390:INFO:NeuralNetClassifier Imported succesfully
2021-07-18 20:29:47,410:INFO:Starting cross validation
2021-07-18 20:29:47,411:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 20:29:53,093:INFO:Calculating mean and std
2021-07-18 20:29:53,095:INFO:Creating metrics dataframe
2021-07-18 20:29:53,108:INFO:Finalizing model
2021-07-18 20:29:58,994:INFO:Uploading results into container
2021-07-18 20:29:58,994:INFO:Uploading model into container now
2021-07-18 20:29:58,994:INFO:create_model_container: 11
2021-07-18 20:29:58,994:INFO:master_model_container: 11
2021-07-18 20:29:58,995:INFO:display_container: 9
2021-07-18 20:29:58,997:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=100, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=100, out_features=200, bias=True)
    (output): Linear(in_features=200, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-18 20:29:58,997:INFO:create_model() succesfully completed......................................
2021-07-18 20:29:59,099:INFO:SubProcess create_model() end ==================================
2021-07-18 20:29:59,100:INFO:Creating MLFlow logs
2021-07-18 20:29:59,146:INFO:Model: NeuralNetClassifier
2021-07-18 20:29:59,366:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 20, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'module__num_units_d2': 200, 'module__num_units_d1': 100, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7f3c373f71d0>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3b7392c310>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c373aa090>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7f3c082fb990>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7f3c374734d0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7f3c373c9a10>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-18 20:30:00,239:INFO:Initializing predict_model()
2021-07-18 20:30:00,239:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=100, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=100, out_features=200, bias=True)
    (output): Linear(in_features=200, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-18 20:30:00,240:INFO:Checking exceptions
2021-07-18 20:30:00,240:INFO:Preloading libraries
2021-07-18 20:30:00,243:WARNING:Couldn't create holdout prediction for model, exception below:
2021-07-18 20:30:00,244:WARNING:Traceback (most recent call last):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 10139, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/pycaret/internal/tabular.py", line 8559, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/skorch/net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "/home/pranay/anaconda3/envs/ml/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-07-18 20:30:00,577:INFO:create_model_container: 11
2021-07-18 20:30:00,577:INFO:master_model_container: 11
2021-07-18 20:30:00,578:INFO:display_container: 9
2021-07-18 20:30:00,579:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=100, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=100, out_features=200, bias=True)
    (output): Linear(in_features=200, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-07-18 20:30:00,580:INFO:tune_model() succesfully completed......................................
2021-07-18 20:30:00,756:INFO:Initializing compare_models()
2021-07-18 20:30:00,757:INFO:compare_models(include=[Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=100, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=100, out_features=200, bias=True)
    (output): Linear(in_features=200, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 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)], fold=None, round=4, cross_validation=True, sort=AUC, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-07-18 20:30:00,757:INFO:Checking exceptions
2021-07-18 20:30:00,758:INFO:Preparing display monitor
2021-07-18 20:30:00,758:INFO:Preparing display monitor
2021-07-18 20:30:00,786:INFO:Initializing custom model NeuralNetClassifier
2021-07-18 20:30:00,786:INFO:Total runtime is 1.4146169026692709e-05 minutes
2021-07-18 20:30:00,796:INFO:SubProcess create_model() called ==================================
2021-07-18 20:30:00,798:INFO:Initializing create_model()
2021-07-18 20:30:00,798:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=100, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=100, out_features=200, bias=True)
    (output): Linear(in_features=200, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 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, display=<pycaret.internal.Display.Display object at 0x7f3c373f8b90>, kwargs={})
2021-07-18 20:30:00,798:INFO:Checking exceptions
2021-07-18 20:30:00,798:INFO:Importing libraries
2021-07-18 20:30:00,798:INFO:Copying training dataset
2021-07-18 20:30:00,799:INFO:Defining folds
2021-07-18 20:30:00,800:INFO:Declaring metric variables
2021-07-18 20:30:00,811:INFO:Importing untrained model
2021-07-18 20:30:00,811:INFO:Declaring custom model
2021-07-18 20:30:00,822:INFO:NeuralNetClassifier Imported succesfully
2021-07-18 20:30:00,841:INFO:Starting cross validation
2021-07-18 20:30:00,841:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 20:30:07,261:INFO:Calculating mean and std
2021-07-18 20:30:07,262:INFO:Creating metrics dataframe
2021-07-18 20:30:07,267:INFO:Uploading results into container
2021-07-18 20:30:07,267:INFO:Uploading model into container now
2021-07-18 20:30:07,267:INFO:create_model_container: 12
2021-07-18 20:30:07,268:INFO:master_model_container: 12
2021-07-18 20:30:07,268:INFO:display_container: 10
2021-07-18 20:30:07,269:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
  module__num_units_d1=100,
  module__num_units_d2=200,
))],
         verbose=False)
2021-07-18 20:30:07,270:INFO:create_model() succesfully completed......................................
2021-07-18 20:30:07,358:INFO:SubProcess create_model() end ==================================
2021-07-18 20:30:07,359:INFO:Creating metrics dataframe
2021-07-18 20:30:07,373:INFO:Initializing custom model NeuralNetClassifier
2021-07-18 20:30:07,373:INFO:Total runtime is 0.10979113578796386 minutes
2021-07-18 20:30:07,383:INFO:SubProcess create_model() called ==================================
2021-07-18 20:30:07,384:INFO:Initializing create_model()
2021-07-18 20:30:07,385:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 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, display=<pycaret.internal.Display.Display object at 0x7f3c373f8b90>, kwargs={})
2021-07-18 20:30:07,385:INFO:Checking exceptions
2021-07-18 20:30:07,385:INFO:Importing libraries
2021-07-18 20:30:07,385:INFO:Copying training dataset
2021-07-18 20:30:07,386:INFO:Defining folds
2021-07-18 20:30:07,386:INFO:Declaring metric variables
2021-07-18 20:30:07,396:INFO:Importing untrained model
2021-07-18 20:30:07,396:INFO:Declaring custom model
2021-07-18 20:30:07,407:INFO:NeuralNetClassifier Imported succesfully
2021-07-18 20:30:07,427:INFO:Starting cross validation
2021-07-18 20:30:07,427:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 20:30:16,135:INFO:Calculating mean and std
2021-07-18 20:30:16,136:INFO:Creating metrics dataframe
2021-07-18 20:30:16,141:INFO:Uploading results into container
2021-07-18 20:30:16,142:INFO:Uploading model into container now
2021-07-18 20:30:16,142:INFO:create_model_container: 13
2021-07-18 20:30:16,142:INFO:master_model_container: 13
2021-07-18 20:30:16,143:INFO:display_container: 10
2021-07-18 20:30:16,144:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False)
2021-07-18 20:30:16,144:INFO:create_model() succesfully completed......................................
2021-07-18 20:30:16,234:INFO:SubProcess create_model() end ==================================
2021-07-18 20:30:16,234:INFO:Creating metrics dataframe
2021-07-18 20:30:16,253:INFO:Initializing custom model Random Forest Classifier
2021-07-18 20:30:16,253:INFO:Total runtime is 0.2577998558680216 minutes
2021-07-18 20:30:16,263:INFO:SubProcess create_model() called ==================================
2021-07-18 20:30:16,264:INFO:Initializing create_model()
2021-07-18 20:30:16,264:INFO:create_model(estimator=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), 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, display=<pycaret.internal.Display.Display object at 0x7f3c373f8b90>, kwargs={})
2021-07-18 20:30:16,264:INFO:Checking exceptions
2021-07-18 20:30:16,265:INFO:Importing libraries
2021-07-18 20:30:16,265:INFO:Copying training dataset
2021-07-18 20:30:16,266:INFO:Defining folds
2021-07-18 20:30:16,266:INFO:Declaring metric variables
2021-07-18 20:30:16,273:INFO:Importing untrained model
2021-07-18 20:30:16,274:INFO:Declaring custom model
2021-07-18 20:30:16,283:INFO:Random Forest Classifier Imported succesfully
2021-07-18 20:30:16,300:INFO:Starting cross validation
2021-07-18 20:30:16,301:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-07-18 20:30:18,167:INFO:Calculating mean and std
2021-07-18 20:30:18,168:INFO:Creating metrics dataframe
2021-07-18 20:30:18,172:INFO:Uploading results into container
2021-07-18 20:30:18,173:INFO:Uploading model into container now
2021-07-18 20:30:18,173:INFO:create_model_container: 14
2021-07-18 20:30:18,174:INFO:master_model_container: 14
2021-07-18 20:30:18,174:INFO:display_container: 10
2021-07-18 20:30:18,175: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)
2021-07-18 20:30:18,175:INFO:create_model() succesfully completed......................................
2021-07-18 20:30:18,267:INFO:SubProcess create_model() end ==================================
2021-07-18 20:30:18,268:INFO:Creating metrics dataframe
2021-07-18 20:30:18,305:INFO:Initializing create_model()
2021-07-18 20:30:18,305:INFO:create_model(estimator=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), 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, display=None, kwargs={})
2021-07-18 20:30:18,306:INFO:Checking exceptions
2021-07-18 20:30:18,306:INFO:Importing libraries
2021-07-18 20:30:18,306:INFO:Copying training dataset
2021-07-18 20:30:18,307:INFO:Defining folds
2021-07-18 20:30:18,307:INFO:Declaring metric variables
2021-07-18 20:30:18,307:INFO:Importing untrained model
2021-07-18 20:30:18,307:INFO:Declaring custom model
2021-07-18 20:30:18,308:INFO:Random Forest Classifier Imported succesfully
2021-07-18 20:30:18,308:INFO:Cross validation set to False
2021-07-18 20:30:18,309:INFO:Fitting Model
2021-07-18 20:30:19,161: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)
2021-07-18 20:30:19,161:INFO:create_models() succesfully completed......................................
2021-07-18 20:30:19,259:INFO:Creating MLFlow logs
2021-07-18 20:30:19,308:INFO:Model: Random Forest Classifier
2021-07-18 20:30:19,526: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}
2021-07-18 20:30:20,163:INFO:Initializing predict_model()
2021-07-18 20:30:20,163:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-07-18 20:30:20,163:INFO:Checking exceptions
2021-07-18 20:30:20,164:INFO:Preloading libraries
2021-07-18 20:30:20,852:INFO:Creating MLFlow logs
2021-07-18 20:30:20,899:INFO:Model: NeuralNetClassifier
2021-07-18 20:30:21,117:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 30, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7f3c438d39d0>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3b79b91290>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3b79b91b90>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7f3b78136990>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7f3c374734d0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7f3b798a3410>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-18 20:30:22,028:INFO:Creating MLFlow logs
2021-07-18 20:30:22,062:INFO:Model: NeuralNetClassifier
2021-07-18 20:30:22,228:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class 'torch.nn.modules.loss.NLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 20, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'module__num_units_d2': 200, 'module__num_units_d1': 100, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x7f3c373f7610>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c373b6110>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x7f3c373aefd0>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x7f3b73906bd0>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x7f3c374734d0>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x7f3b73906690>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-07-18 20:30:23,161:INFO:create_model_container: 14
2021-07-18 20:30:23,161:INFO:master_model_container: 14
2021-07-18 20:30:23,161:INFO:display_container: 10
2021-07-18 20:30:23,162: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)
2021-07-18 20:30:23,162:INFO:compare_models() succesfully completed......................................
2021-07-18 20:32:18,787:INFO:Initializing evaluate_model()
2021-07-18 20:32:18,787:INFO:evaluate_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=100, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=100, out_features=200, bias=True)
    (output): Linear(in_features=200, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=None, fit_kwargs=None, feature_name=None, groups=None, use_train_data=False)
2021-07-18 20:32:18,805:INFO:Initializing plot_model()
2021-07-18 20:32:18,805:INFO:plot_model(plot=parameter, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), use_train_data=False, verbose=True, is_in_evaluate=True, display=None, display_format=None, estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=100, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=100, out_features=200, bias=True)
    (output): Linear(in_features=200, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), feature_name=None, fit_kwargs={}, groups=None, label=False, save=False, scale=1, system=True)
2021-07-18 20:32:18,805:INFO:Checking exceptions
2021-07-18 20:32:18,806:INFO:Preparing display monitor
2021-07-18 20:32:18,812:INFO:Preloading libraries
2021-07-18 20:32:18,833:INFO:Copying training dataset
2021-07-18 20:32:18,834:INFO:Copying test dataset
2021-07-18 20:32:18,834:INFO:Plot type: parameter
2021-07-18 20:32:18,912:INFO:Visual Rendered Successfully
2021-07-18 20:32:19,008:INFO:plot_model() succesfully completed......................................
2021-07-18 20:32:27,325:INFO:Initializing plot_model()
2021-07-18 20:32:27,325:INFO:plot_model(plot=confusion_matrix, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), use_train_data=False, verbose=True, is_in_evaluate=True, display=None, display_format=None, estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=100, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=100, out_features=200, bias=True)
    (output): Linear(in_features=200, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), feature_name=None, fit_kwargs={}, groups=None, label=False, save=False, scale=1, system=True)
2021-07-18 20:32:27,325:INFO:Checking exceptions
2021-07-18 20:32:27,326:INFO:Preparing display monitor
2021-07-18 20:32:27,331:INFO:Preloading libraries
2021-07-18 20:32:27,346:INFO:Copying training dataset
2021-07-18 20:32:27,347:INFO:Copying test dataset
2021-07-18 20:32:27,348:INFO:Plot type: confusion_matrix
2021-07-18 20:32:27,352:INFO:Fitting Model
2021-07-18 20:32:33,387:INFO:Scoring test/hold-out set
2021-07-18 20:32:33,633:INFO:Visual Rendered Successfully
2021-07-18 20:32:33,727:INFO:plot_model() succesfully completed......................................
2021-07-18 20:32:47,627:INFO:Initializing evaluate_model()
2021-07-18 20:32:47,628:INFO:evaluate_model(estimator=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), fold=None, fit_kwargs=None, feature_name=None, groups=None, use_train_data=False)
2021-07-18 20:32:47,644:INFO:Initializing plot_model()
2021-07-18 20:32:47,644:INFO:plot_model(plot=parameter, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), use_train_data=False, verbose=True, is_in_evaluate=True, display=None, display_format=None, estimator=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), feature_name=None, fit_kwargs={}, groups=None, label=False, save=False, scale=1, system=True)
2021-07-18 20:32:47,644:INFO:Checking exceptions
2021-07-18 20:32:47,750:INFO:Preparing display monitor
2021-07-18 20:32:47,769:INFO:Preloading libraries
2021-07-18 20:32:47,784:INFO:Copying training dataset
2021-07-18 20:32:47,786:INFO:Copying test dataset
2021-07-18 20:32:47,786:INFO:Plot type: parameter
2021-07-18 20:32:47,999:INFO:Visual Rendered Successfully
2021-07-18 20:32:48,098:INFO:plot_model() succesfully completed......................................
2021-07-18 20:32:49,951:INFO:Initializing plot_model()
2021-07-18 20:32:49,951:INFO:plot_model(plot=confusion_matrix, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), use_train_data=False, verbose=True, is_in_evaluate=True, display=None, display_format=None, estimator=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), feature_name=None, fit_kwargs={}, groups=None, label=False, save=False, scale=1, system=True)
2021-07-18 20:32:49,951:INFO:Checking exceptions
2021-07-18 20:32:50,055:INFO:Preparing display monitor
2021-07-18 20:32:50,062:INFO:Preloading libraries
2021-07-18 20:32:50,073:INFO:Copying training dataset
2021-07-18 20:32:50,074:INFO:Copying test dataset
2021-07-18 20:32:50,075:INFO:Plot type: confusion_matrix
2021-07-18 20:32:50,283:INFO:Fitting Model
2021-07-18 20:32:50,284:INFO:Scoring test/hold-out set
2021-07-18 20:32:50,584:INFO:Visual Rendered Successfully
2021-07-18 20:32:50,676:INFO:plot_model() succesfully completed......................................
2021-09-07 14:41:33,029:INFO:PyCaret Supervised Module
2021-09-07 14:41:33,029:INFO:ML Usecase: classification
2021-09-07 14:41:33,029:INFO:version 2.3.3
2021-09-07 14:41:33,029:INFO:Initializing setup()
2021-09-07 14:41:33,029:INFO:setup(target=stabf, 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=None, categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-09-07 14:41:33,029:INFO:Checking environment
2021-09-07 14:41:33,029:INFO:python_version: 3.7.11
2021-09-07 14:41:33,029:INFO:python_build: ('default', 'Jul 27 2021 09:42:29')
2021-09-07 14:41:33,029:INFO:machine: AMD64
2021-09-07 14:41:33,029:INFO:platform: Windows-10-10.0.19041-SP0
2021-09-07 14:41:33,029:INFO:Memory: svmem(total=16929984512, available=5043929088, percent=70.2, used=11886055424, free=5043929088)
2021-09-07 14:41:33,029:INFO:Physical Core: 6
2021-09-07 14:41:33,029:INFO:Logical Core: 12
2021-09-07 14:41:33,029:INFO:Checking libraries
2021-09-07 14:41:33,029:INFO:pd==1.3.1
2021-09-07 14:41:33,029:INFO:numpy==1.19.5
2021-09-07 14:41:33,029:INFO:sklearn==0.23.2
2021-09-07 14:41:33,029:INFO:lightgbm==3.2.1
2021-09-07 14:41:33,029:WARNING:catboost not found
2021-09-07 14:41:33,029:WARNING:xgboost not found
2021-09-07 14:41:33,629:INFO:mlflow==1.19.0
2021-09-07 14:41:33,629:INFO:Checking Exceptions
2021-09-07 14:41:33,629:INFO:Declaring global variables
2021-09-07 14:41:33,629:INFO:USI: 3a7e
2021-09-07 14:41:33,629:INFO:pycaret_globals: {'fold_groups_param', '_all_metrics', 'transform_target_method_param', 'fold_generator', 'log_plots_param', '_available_plots', '_all_models_internal', 'fold_groups_param_full', '_ml_usecase', 'experiment__', 'X_test', 'data_before_preprocess', 'transform_target_param', '_all_models', 'html_param', 'target_param', 'seed', 'stratify_param', 'X_train', 'prep_pipe', 'fix_imbalance_param', 'y', 'imputation_regressor', 'gpu_param', 'create_model_container', 'X', 'master_model_container', 'y_test', 'imputation_classifier', 'display_container', 'iterative_imputation_iters_param', 'pycaret_globals', 'n_jobs_param', 'exp_name_log', '_internal_pipeline', 'USI', 'y_train', 'fold_param', 'fold_shuffle_param', 'fix_imbalance_method_param', '_gpu_n_jobs_param', 'logging_param'}
2021-09-07 14:41:33,629:INFO:Preparing display monitor
2021-09-07 14:41:33,629:INFO:Preparing display monitor
2021-09-07 14:41:33,637:INFO:Importing libraries
2021-09-07 14:41:33,637:INFO:Copying data for preprocessing
2021-09-07 14:41:33,645:INFO:Declaring preprocessing parameters
2021-09-07 14:41:33,645:INFO:Creating preprocessing pipeline
2021-09-07 14:41:33,669:INFO:Preprocessing pipeline created successfully
2021-09-07 14:41:33,669:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-09-07 14:41:33,669:INFO:Creating global containers
2021-09-07 14:41:33,669:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-09-07 14:41:33,933:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 14:41:33,933:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 14:41:34,005:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 14:41:34,005:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 14:41:34,005:INFO:Creating grid variables
2021-09-07 14:41:34,021:INFO:Logging experiment in MLFlow
2021-09-07 14:41:34,069:WARNING:Couldn't create mlflow experiment. Exception:
2021-09-07 14:41:34,085:WARNING:Traceback (most recent call last):
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\pycaret\internal\tabular.py", line 1702, in setup
    mlflow.create_experiment(exp_name_log)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\fluent.py", line 875, in create_experiment
    return MlflowClient().create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\client.py", line 500, in create_experiment
    return self._tracking_client.create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\_tracking_service\client.py", line 167, in create_experiment
    return self.store.create_experiment(name=name, artifact_location=artifact_location,)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 313, in create_experiment
    self._validate_experiment_name(name)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 308, in _validate_experiment_name
    databricks_pb2.RESOURCE_ALREADY_EXISTS,
mlflow.exceptions.MlflowException: Experiment 'electrical_grid_1' already exists.

2021-09-07 14:41:34,421:INFO:SubProcess save_model() called ==================================
2021-09-07 14:41:34,429:INFO:Initializing save_model()
2021-09-07 14:41:34,437:INFO:save_model(model=Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('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=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False), verbose=False, kwargs={})
2021-09-07 14:41:34,437:INFO:Adding model into prep_pipe
2021-09-07 14:41:34,437:WARNING:Only Model saved as it was a pipeline.
2021-09-07 14:41:34,437:INFO:Transformation Pipeline.pkl saved in current working directory
2021-09-07 14:41:34,445:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 14:41:34,445:INFO:save_model() successfully completed......................................
2021-09-07 14:41:34,501:INFO:SubProcess save_model() end ==================================
2021-09-07 14:41:34,525:INFO:create_model_container: 0
2021-09-07 14:41:34,525:INFO:master_model_container: 0
2021-09-07 14:41:34,525:INFO:display_container: 1
2021-09-07 14:41:34,533:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 14:41:34,533:INFO:setup() succesfully completed......................................
2021-09-07 14:41:34,613:INFO:Initializing create_model()
2021-09-07 14:41:34,613:INFO:create_model(estimator=rf, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 14:41:34,613:INFO:Checking exceptions
2021-09-07 14:41:34,617:INFO:Preparing display monitor
2021-09-07 14:41:34,642:INFO:Importing libraries
2021-09-07 14:41:34,642:INFO:Copying training dataset
2021-09-07 14:41:34,650:INFO:Defining folds
2021-09-07 14:41:34,650:INFO:Declaring metric variables
2021-09-07 14:41:34,658:INFO:Importing untrained model
2021-09-07 14:41:34,674:INFO:Random Forest Classifier Imported succesfully
2021-09-07 14:41:34,706:INFO:Starting cross validation
2021-09-07 14:41:34,706:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 14:41:39,567:INFO:Calculating mean and std
2021-09-07 14:41:39,567:INFO:Creating metrics dataframe
2021-09-07 14:41:39,583:INFO:Finalizing model
2021-09-07 14:41:40,447:INFO:Creating MLFlow logs
2021-09-07 14:41:40,455:INFO:Model: Random Forest Classifier
2021-09-07 14:41:40,479: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}
2021-09-07 14:41:40,527:INFO:Initializing predict_model()
2021-09-07 14:41:40,527:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 14:41:40,527:INFO:Checking exceptions
2021-09-07 14:41:40,527:INFO:Preloading libraries
2021-09-07 14:41:41,044:INFO:Uploading results into container
2021-09-07 14:41:41,044:INFO:Uploading model into container now
2021-09-07 14:41:41,060:INFO:create_model_container: 1
2021-09-07 14:41:41,060:INFO:master_model_container: 1
2021-09-07 14:41:41,060:INFO:display_container: 2
2021-09-07 14:41:41,060: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)
2021-09-07 14:41:41,060:INFO:create_model() succesfully completed......................................
2021-09-07 14:43:02,509:INFO:PyCaret Supervised Module
2021-09-07 14:43:02,509:INFO:ML Usecase: classification
2021-09-07 14:43:02,509:INFO:version 2.3.3
2021-09-07 14:43:02,509:INFO:Initializing setup()
2021-09-07 14:43:02,509:INFO:setup(target=stabf, 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=None, categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-09-07 14:43:02,509:INFO:Checking environment
2021-09-07 14:43:02,509:INFO:python_version: 3.7.11
2021-09-07 14:43:02,509:INFO:python_build: ('default', 'Jul 27 2021 09:42:29')
2021-09-07 14:43:02,509:INFO:machine: AMD64
2021-09-07 14:43:02,509:INFO:platform: Windows-10-10.0.19041-SP0
2021-09-07 14:43:02,509:INFO:Memory: svmem(total=16929984512, available=5333217280, percent=68.5, used=11596767232, free=5333217280)
2021-09-07 14:43:02,509:INFO:Physical Core: 6
2021-09-07 14:43:02,509:INFO:Logical Core: 12
2021-09-07 14:43:02,509:INFO:Checking libraries
2021-09-07 14:43:02,509:INFO:pd==1.3.1
2021-09-07 14:43:02,509:INFO:numpy==1.19.5
2021-09-07 14:43:02,509:INFO:sklearn==0.23.2
2021-09-07 14:43:02,509:INFO:lightgbm==3.2.1
2021-09-07 14:43:02,509:WARNING:catboost not found
2021-09-07 14:43:02,517:WARNING:xgboost not found
2021-09-07 14:43:02,873:INFO:mlflow==1.19.0
2021-09-07 14:43:02,873:INFO:Checking Exceptions
2021-09-07 14:43:02,873:INFO:Declaring global variables
2021-09-07 14:43:02,873:INFO:USI: 5a87
2021-09-07 14:43:02,873:INFO:pycaret_globals: {'_all_models_internal', 'master_model_container', 'create_model_container', 'X', 'fix_imbalance_method_param', 'experiment__', 'log_plots_param', 'fold_generator', '_all_models', 'exp_name_log', 'imputation_classifier', 'seed', 'transform_target_param', 'data_before_preprocess', 'fix_imbalance_param', 'target_param', '_internal_pipeline', '_available_plots', '_all_metrics', 'imputation_regressor', 'fold_shuffle_param', '_ml_usecase', 'pycaret_globals', 'logging_param', 'X_test', 'gpu_param', 'fold_groups_param', 'X_train', 'fold_param', '_gpu_n_jobs_param', 'transform_target_method_param', 'y_train', 'prep_pipe', 'n_jobs_param', 'stratify_param', 'fold_groups_param_full', 'iterative_imputation_iters_param', 'y_test', 'html_param', 'y', 'display_container', 'USI'}
2021-09-07 14:43:02,873:INFO:Preparing display monitor
2021-09-07 14:43:02,873:INFO:Preparing display monitor
2021-09-07 14:43:02,889:INFO:Importing libraries
2021-09-07 14:43:02,889:INFO:Copying data for preprocessing
2021-09-07 14:43:02,897:INFO:Declaring preprocessing parameters
2021-09-07 14:43:02,897:INFO:Creating preprocessing pipeline
2021-09-07 14:43:02,913:INFO:Preprocessing pipeline created successfully
2021-09-07 14:43:02,913:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-09-07 14:43:02,913:INFO:Creating global containers
2021-09-07 14:43:02,921:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-09-07 14:43:03,153:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 14:43:03,153:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 14:43:03,233:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 14:43:03,233:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 14:43:03,233:INFO:Creating grid variables
2021-09-07 14:43:03,241:INFO:Logging experiment in MLFlow
2021-09-07 14:43:03,265:WARNING:Couldn't create mlflow experiment. Exception:
2021-09-07 14:43:03,273:WARNING:Traceback (most recent call last):
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\pycaret\internal\tabular.py", line 1702, in setup
    mlflow.create_experiment(exp_name_log)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\fluent.py", line 875, in create_experiment
    return MlflowClient().create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\client.py", line 500, in create_experiment
    return self._tracking_client.create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\_tracking_service\client.py", line 167, in create_experiment
    return self.store.create_experiment(name=name, artifact_location=artifact_location,)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 313, in create_experiment
    self._validate_experiment_name(name)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 308, in _validate_experiment_name
    databricks_pb2.RESOURCE_ALREADY_EXISTS,
mlflow.exceptions.MlflowException: Experiment 'electrical_grid_1' already exists.

2021-09-07 14:43:03,537:INFO:SubProcess save_model() called ==================================
2021-09-07 14:43:03,545:INFO:Initializing save_model()
2021-09-07 14:43:03,545:INFO:save_model(model=Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('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=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False), verbose=False, kwargs={})
2021-09-07 14:43:03,553:INFO:Adding model into prep_pipe
2021-09-07 14:43:03,553:WARNING:Only Model saved as it was a pipeline.
2021-09-07 14:43:03,553:INFO:Transformation Pipeline.pkl saved in current working directory
2021-09-07 14:43:03,561:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 14:43:03,561:INFO:save_model() successfully completed......................................
2021-09-07 14:43:03,625:INFO:SubProcess save_model() end ==================================
2021-09-07 14:43:03,657:INFO:create_model_container: 0
2021-09-07 14:43:03,657:INFO:master_model_container: 0
2021-09-07 14:43:03,657:INFO:display_container: 1
2021-09-07 14:43:03,665:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 14:43:03,665:INFO:setup() succesfully completed......................................
2021-09-07 14:43:03,737:INFO:Initializing create_model()
2021-09-07 14:43:03,737:INFO:create_model(estimator=rf, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 14:43:03,737:INFO:Checking exceptions
2021-09-07 14:43:03,737:INFO:Preparing display monitor
2021-09-07 14:43:03,761:INFO:Importing libraries
2021-09-07 14:43:03,761:INFO:Copying training dataset
2021-09-07 14:43:03,761:INFO:Defining folds
2021-09-07 14:43:03,761:INFO:Declaring metric variables
2021-09-07 14:43:03,769:INFO:Importing untrained model
2021-09-07 14:43:03,785:INFO:Random Forest Classifier Imported succesfully
2021-09-07 14:43:03,801:INFO:Starting cross validation
2021-09-07 14:43:03,801:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 14:43:08,511:INFO:Calculating mean and std
2021-09-07 14:43:08,511:INFO:Creating metrics dataframe
2021-09-07 14:43:08,527:INFO:Finalizing model
2021-09-07 14:43:09,388:INFO:Creating MLFlow logs
2021-09-07 14:43:09,419:INFO:Model: Random Forest Classifier
2021-09-07 14:43:09,451: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}
2021-09-07 14:43:09,507:INFO:Initializing predict_model()
2021-09-07 14:43:09,507:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 14:43:09,507:INFO:Checking exceptions
2021-09-07 14:43:09,507:INFO:Preloading libraries
2021-09-07 14:43:09,963:INFO:Uploading results into container
2021-09-07 14:43:09,963:INFO:Uploading model into container now
2021-09-07 14:43:09,971:INFO:create_model_container: 1
2021-09-07 14:43:09,971:INFO:master_model_container: 1
2021-09-07 14:43:09,971:INFO:display_container: 2
2021-09-07 14:43:09,979: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)
2021-09-07 14:43:09,979:INFO:create_model() succesfully completed......................................
2021-09-07 14:43:10,542:INFO:Initializing create_model()
2021-09-07 14:43:10,542:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 14:43:10,542:INFO:Checking exceptions
2021-09-07 14:43:10,550:INFO:Preparing display monitor
2021-09-07 14:43:10,574:INFO:Importing libraries
2021-09-07 14:43:10,574:INFO:Copying training dataset
2021-09-07 14:43:10,574:INFO:Defining folds
2021-09-07 14:43:10,574:INFO:Declaring metric variables
2021-09-07 14:43:10,590:INFO:Importing untrained model
2021-09-07 14:43:10,590:INFO:Declaring custom model
2021-09-07 14:43:10,606:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 14:43:10,626:INFO:Starting cross validation
2021-09-07 14:43:10,626:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 14:43:13,832:INFO:Calculating mean and std
2021-09-07 14:43:13,832:INFO:Creating metrics dataframe
2021-09-07 14:43:13,848:INFO:Finalizing model
2021-09-07 14:45:27,317:INFO:Initializing create_model()
2021-09-07 14:45:27,317:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 14:45:27,317:INFO:Checking exceptions
2021-09-07 14:45:27,317:INFO:Preparing display monitor
2021-09-07 14:45:27,341:INFO:Importing libraries
2021-09-07 14:45:27,341:INFO:Copying training dataset
2021-09-07 14:45:27,341:INFO:Defining folds
2021-09-07 14:45:27,341:INFO:Declaring metric variables
2021-09-07 14:45:27,349:INFO:Importing untrained model
2021-09-07 14:45:27,349:INFO:Declaring custom model
2021-09-07 14:45:27,365:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 14:45:27,381:INFO:Starting cross validation
2021-09-07 14:45:27,381:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 14:45:30,081:INFO:Calculating mean and std
2021-09-07 14:45:30,081:INFO:Creating metrics dataframe
2021-09-07 14:45:30,097:INFO:Finalizing model
2021-09-07 14:47:06,914:INFO:Initializing create_model()
2021-09-07 14:47:06,914:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=True)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 14:47:06,914:INFO:Checking exceptions
2021-09-07 14:47:06,914:INFO:Preparing display monitor
2021-09-07 14:47:06,938:INFO:Importing libraries
2021-09-07 14:47:06,938:INFO:Copying training dataset
2021-09-07 14:47:06,938:INFO:Defining folds
2021-09-07 14:47:06,938:INFO:Declaring metric variables
2021-09-07 14:47:06,946:INFO:Importing untrained model
2021-09-07 14:47:06,946:INFO:Declaring custom model
2021-09-07 14:47:06,954:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 14:47:06,978:INFO:Starting cross validation
2021-09-07 14:47:06,978:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 14:47:07,655:INFO:Calculating mean and std
2021-09-07 14:47:07,655:INFO:Creating metrics dataframe
2021-09-07 14:47:07,671:INFO:Finalizing model
2021-09-07 14:47:40,939:INFO:Initializing create_model()
2021-09-07 14:47:40,939:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.float32'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 14:47:40,939:INFO:Checking exceptions
2021-09-07 14:47:40,939:INFO:Preparing display monitor
2021-09-07 14:47:40,955:INFO:Importing libraries
2021-09-07 14:47:40,955:INFO:Copying training dataset
2021-09-07 14:47:40,955:INFO:Defining folds
2021-09-07 14:47:40,955:INFO:Declaring metric variables
2021-09-07 14:47:40,971:INFO:Importing untrained model
2021-09-07 14:47:40,971:INFO:Declaring custom model
2021-09-07 14:47:40,979:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 14:47:40,995:INFO:Starting cross validation
2021-09-07 14:47:40,995:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 14:47:41,043:INFO:Calculating mean and std
2021-09-07 14:47:41,043:INFO:Creating metrics dataframe
2021-09-07 14:47:41,051:INFO:Finalizing model
2021-09-07 14:47:51,877:INFO:Initializing create_model()
2021-09-07 14:47:51,877:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 14:47:51,877:INFO:Checking exceptions
2021-09-07 14:47:51,877:INFO:Preparing display monitor
2021-09-07 14:47:51,893:INFO:Importing libraries
2021-09-07 14:47:51,893:INFO:Copying training dataset
2021-09-07 14:47:51,901:INFO:Defining folds
2021-09-07 14:47:51,901:INFO:Declaring metric variables
2021-09-07 14:47:51,909:INFO:Importing untrained model
2021-09-07 14:47:51,909:INFO:Declaring custom model
2021-09-07 14:47:51,917:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 14:47:51,941:INFO:Starting cross validation
2021-09-07 14:47:51,941:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 14:47:51,981:INFO:Calculating mean and std
2021-09-07 14:47:51,981:INFO:Creating metrics dataframe
2021-09-07 14:47:51,997:INFO:Finalizing model
2021-09-07 14:54:08,348:INFO:Initializing get_config()
2021-09-07 14:54:08,348:INFO:get_config(variable=prep_pipe)
2021-09-07 14:54:08,356:INFO:Global variable: prep_pipe returned as Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 14:54:08,356:INFO:get_config() succesfully completed......................................
2021-09-07 14:54:17,411:INFO:Initializing get_config()
2021-09-07 14:54:17,419:INFO:get_config(variable=prep_pipe)
2021-09-07 14:54:17,419:INFO:Global variable: prep_pipe returned as Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 14:54:17,419:INFO:get_config() succesfully completed......................................
2021-09-07 14:58:32,108:INFO:PyCaret Supervised Module
2021-09-07 14:58:32,108:INFO:ML Usecase: classification
2021-09-07 14:58:32,108:INFO:version 2.3.2
2021-09-07 14:58:32,108:INFO:Initializing setup()
2021-09-07 14:58:32,108:INFO:setup(target=stabf, 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=None, categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-09-07 14:58:32,108:INFO:Checking environment
2021-09-07 14:58:32,108:INFO:python_version: 3.7.11
2021-09-07 14:58:32,108:INFO:python_build: ('default', 'Jul 27 2021 09:42:29')
2021-09-07 14:58:32,108:INFO:machine: AMD64
2021-09-07 14:58:32,108:INFO:platform: Windows-10-10.0.19041-SP0
2021-09-07 14:58:32,108:INFO:Memory: svmem(total=16929984512, available=4609097728, percent=72.8, used=12320886784, free=4609097728)
2021-09-07 14:58:32,108:INFO:Physical Core: 6
2021-09-07 14:58:32,108:INFO:Logical Core: 12
2021-09-07 14:58:32,108:INFO:Checking libraries
2021-09-07 14:58:32,108:INFO:pd==1.3.1
2021-09-07 14:58:32,108:INFO:numpy==1.19.5
2021-09-07 14:58:32,108:INFO:sklearn==0.23.2
2021-09-07 14:58:32,108:INFO:lightgbm==3.2.1
2021-09-07 14:58:32,116:WARNING:catboost not found
2021-09-07 14:58:32,116:WARNING:xgboost not found
2021-09-07 14:58:32,470:INFO:mlflow==1.19.0
2021-09-07 14:58:32,470:INFO:Checking Exceptions
2021-09-07 14:58:32,470:INFO:Declaring global variables
2021-09-07 14:58:32,470:INFO:USI: e9cb
2021-09-07 14:58:32,470:INFO:pycaret_globals: {'USI', 'stratify_param', 'experiment__', 'data_before_preprocess', 'y', 'master_model_container', 'gpu_param', 'X', 'X_test', 'seed', 'create_model_container', 'fold_generator', 'exp_name_log', 'fix_imbalance_param', '_ml_usecase', '_all_metrics', 'target_param', 'logging_param', 'log_plots_param', '_gpu_n_jobs_param', '_internal_pipeline', 'html_param', 'transform_target_method_param', 'fix_imbalance_method_param', 'pycaret_globals', 'n_jobs_param', 'transform_target_param', 'fold_shuffle_param', '_all_models_internal', 'y_test', 'prep_pipe', 'iterative_imputation_iters_param', 'fold_param', 'imputation_classifier', 'fold_groups_param', 'X_train', 'fold_groups_param_full', 'imputation_regressor', 'y_train', '_available_plots', '_all_models', 'display_container'}
2021-09-07 14:58:32,470:INFO:Preparing display monitor
2021-09-07 14:58:32,470:INFO:Preparing display monitor
2021-09-07 14:58:32,480:INFO:Importing libraries
2021-09-07 14:58:32,480:INFO:Copying data for preprocessing
2021-09-07 14:58:32,488:INFO:Declaring preprocessing parameters
2021-09-07 14:58:32,488:INFO:Creating preprocessing pipeline
2021-09-07 14:58:32,504:INFO:Preprocessing pipeline created successfully
2021-09-07 14:58:32,504:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-09-07 14:58:32,504:INFO:Creating global containers
2021-09-07 14:58:32,512:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-09-07 14:58:32,748:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 14:58:32,748:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 14:58:32,817:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 14:58:32,817:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 14:58:32,825:INFO:Creating grid variables
2021-09-07 14:58:32,835:INFO:Logging experiment in MLFlow
2021-09-07 14:58:32,853:WARNING:Couldn't create mlflow experiment. Exception:
2021-09-07 14:58:32,861:WARNING:Traceback (most recent call last):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 1702, in setup
    mlflow.create_experiment(exp_name_log)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\fluent.py", line 875, in create_experiment
    return MlflowClient().create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\client.py", line 500, in create_experiment
    return self._tracking_client.create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\_tracking_service\client.py", line 167, in create_experiment
    return self.store.create_experiment(name=name, artifact_location=artifact_location,)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 313, in create_experiment
    self._validate_experiment_name(name)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 308, in _validate_experiment_name
    databricks_pb2.RESOURCE_ALREADY_EXISTS,
mlflow.exceptions.MlflowException: Experiment 'electrical_grid_1' already exists.

2021-09-07 14:58:33,133:INFO:SubProcess save_model() called ==================================
2021-09-07 14:58:33,141:INFO:Initializing save_model()
2021-09-07 14:58:33,141:INFO:save_model(model=Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('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=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False), verbose=False, kwargs={})
2021-09-07 14:58:33,141:INFO:Adding model into prep_pipe
2021-09-07 14:58:33,149:WARNING:Only Model saved as it was a pipeline.
2021-09-07 14:58:33,149:INFO:Transformation Pipeline.pkl saved in current working directory
2021-09-07 14:58:33,157:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 14:58:33,157:INFO:save_model() successfully completed......................................
2021-09-07 14:58:33,213:INFO:SubProcess save_model() end ==================================
2021-09-07 14:58:33,237:INFO:create_model_container: 0
2021-09-07 14:58:33,237:INFO:master_model_container: 0
2021-09-07 14:58:33,237:INFO:display_container: 1
2021-09-07 14:58:33,245:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 14:58:33,245:INFO:setup() succesfully completed......................................
2021-09-07 14:58:33,301:INFO:Initializing create_model()
2021-09-07 14:58:33,301:INFO:create_model(estimator=rf, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 14:58:33,301:INFO:Checking exceptions
2021-09-07 14:58:33,301:INFO:Preparing display monitor
2021-09-07 14:58:33,333:INFO:Importing libraries
2021-09-07 14:58:33,333:INFO:Copying training dataset
2021-09-07 14:58:33,333:INFO:Defining folds
2021-09-07 14:58:33,333:INFO:Declaring metric variables
2021-09-07 14:58:33,341:INFO:Importing untrained model
2021-09-07 14:58:33,349:INFO:Random Forest Classifier Imported succesfully
2021-09-07 14:58:33,381:INFO:Starting cross validation
2021-09-07 14:58:33,381:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 14:58:37,893:INFO:Calculating mean and std
2021-09-07 14:58:37,893:INFO:Creating metrics dataframe
2021-09-07 14:58:37,909:INFO:Finalizing model
2021-09-07 14:58:38,779:INFO:Creating MLFlow logs
2021-09-07 14:58:38,810:INFO:Model: Random Forest Classifier
2021-09-07 14:58:38,850: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}
2021-09-07 14:58:38,906:INFO:Initializing predict_model()
2021-09-07 14:58:38,906:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 14:58:38,906:INFO:Checking exceptions
2021-09-07 14:58:38,906:INFO:Preloading libraries
2021-09-07 14:58:39,362:INFO:Uploading results into container
2021-09-07 14:58:39,362:INFO:Uploading model into container now
2021-09-07 14:58:39,378:INFO:create_model_container: 1
2021-09-07 14:58:39,378:INFO:master_model_container: 1
2021-09-07 14:58:39,378:INFO:display_container: 2
2021-09-07 14:58:39,378: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)
2021-09-07 14:58:39,378:INFO:create_model() succesfully completed......................................
2021-09-07 14:58:39,922:INFO:Initializing create_model()
2021-09-07 14:58:39,922:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 14:58:39,922:INFO:Checking exceptions
2021-09-07 14:58:39,922:INFO:Preparing display monitor
2021-09-07 14:58:39,947:INFO:Importing libraries
2021-09-07 14:58:39,947:INFO:Copying training dataset
2021-09-07 14:58:39,951:INFO:Defining folds
2021-09-07 14:58:39,951:INFO:Declaring metric variables
2021-09-07 14:58:39,961:INFO:Importing untrained model
2021-09-07 14:58:39,961:INFO:Declaring custom model
2021-09-07 14:58:39,976:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 14:58:40,004:INFO:Starting cross validation
2021-09-07 14:58:40,008:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 14:58:43,332:INFO:Calculating mean and std
2021-09-07 14:58:43,332:INFO:Creating metrics dataframe
2021-09-07 14:58:43,348:INFO:Finalizing model
2021-09-07 14:58:56,623:INFO:Initializing get_config()
2021-09-07 14:58:56,623:INFO:get_config(variable=prep_pipe)
2021-09-07 14:58:56,631:INFO:Global variable: prep_pipe returned as Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 14:58:56,631:INFO:get_config() succesfully completed......................................
2021-09-07 15:00:42,145:INFO:PyCaret Supervised Module
2021-09-07 15:00:42,145:INFO:ML Usecase: classification
2021-09-07 15:00:42,145:INFO:version 2.3.2
2021-09-07 15:00:42,145:INFO:Initializing setup()
2021-09-07 15:00:42,145:INFO:setup(target=stabf, 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=None, categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-09-07 15:00:42,145:INFO:Checking environment
2021-09-07 15:00:42,145:INFO:python_version: 3.7.11
2021-09-07 15:00:42,145:INFO:python_build: ('default', 'Jul 27 2021 09:42:29')
2021-09-07 15:00:42,145:INFO:machine: AMD64
2021-09-07 15:00:42,145:INFO:platform: Windows-10-10.0.19041-SP0
2021-09-07 15:00:42,145:INFO:Memory: svmem(total=16929984512, available=2789203968, percent=83.5, used=14140780544, free=2789203968)
2021-09-07 15:00:42,145:INFO:Physical Core: 6
2021-09-07 15:00:42,145:INFO:Logical Core: 12
2021-09-07 15:00:42,145:INFO:Checking libraries
2021-09-07 15:00:42,145:INFO:pd==1.3.1
2021-09-07 15:00:42,145:INFO:numpy==1.19.5
2021-09-07 15:00:42,145:INFO:sklearn==0.23.2
2021-09-07 15:00:42,145:INFO:lightgbm==3.2.1
2021-09-07 15:00:42,145:WARNING:catboost not found
2021-09-07 15:00:42,145:WARNING:xgboost not found
2021-09-07 15:00:42,145:INFO:mlflow==1.19.0
2021-09-07 15:00:42,145:INFO:Checking Exceptions
2021-09-07 15:00:42,145:INFO:Declaring global variables
2021-09-07 15:00:42,145:INFO:USI: f2f7
2021-09-07 15:00:42,145:INFO:pycaret_globals: {'USI', 'stratify_param', 'experiment__', 'data_before_preprocess', 'y', 'master_model_container', 'gpu_param', 'X', 'X_test', 'seed', 'create_model_container', 'fold_generator', 'exp_name_log', 'fix_imbalance_param', '_ml_usecase', '_all_metrics', 'target_param', 'logging_param', 'log_plots_param', '_gpu_n_jobs_param', '_internal_pipeline', 'html_param', 'transform_target_method_param', 'fix_imbalance_method_param', 'pycaret_globals', 'n_jobs_param', 'transform_target_param', 'fold_shuffle_param', '_all_models_internal', 'y_test', 'prep_pipe', 'iterative_imputation_iters_param', 'fold_param', 'imputation_classifier', 'fold_groups_param', 'X_train', 'fold_groups_param_full', 'imputation_regressor', 'y_train', '_available_plots', '_all_models', 'display_container'}
2021-09-07 15:00:42,145:INFO:Preparing display monitor
2021-09-07 15:00:42,145:INFO:Preparing display monitor
2021-09-07 15:00:42,161:INFO:Importing libraries
2021-09-07 15:00:42,161:INFO:Copying data for preprocessing
2021-09-07 15:00:42,169:INFO:Declaring preprocessing parameters
2021-09-07 15:00:42,169:INFO:Creating preprocessing pipeline
2021-09-07 15:00:42,193:INFO:Preprocessing pipeline created successfully
2021-09-07 15:00:42,193:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-09-07 15:00:42,193:INFO:Creating global containers
2021-09-07 15:00:42,193:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-09-07 15:00:42,441:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 15:00:42,441:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 15:00:42,513:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 15:00:42,513:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 15:00:42,513:INFO:Creating grid variables
2021-09-07 15:00:42,523:INFO:Logging experiment in MLFlow
2021-09-07 15:00:42,534:WARNING:Couldn't create mlflow experiment. Exception:
2021-09-07 15:00:42,534:WARNING:Traceback (most recent call last):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 1702, in setup
    mlflow.create_experiment(exp_name_log)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\fluent.py", line 875, in create_experiment
    return MlflowClient().create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\client.py", line 500, in create_experiment
    return self._tracking_client.create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\_tracking_service\client.py", line 167, in create_experiment
    return self.store.create_experiment(name=name, artifact_location=artifact_location,)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 313, in create_experiment
    self._validate_experiment_name(name)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 308, in _validate_experiment_name
    databricks_pb2.RESOURCE_ALREADY_EXISTS,
mlflow.exceptions.MlflowException: Experiment 'electrical_grid_1' already exists.

2021-09-07 15:00:42,627:INFO:SubProcess save_model() called ==================================
2021-09-07 15:00:42,635:INFO:Initializing save_model()
2021-09-07 15:00:42,635:INFO:save_model(model=Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('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=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False), verbose=False, kwargs={})
2021-09-07 15:00:42,635:INFO:Adding model into prep_pipe
2021-09-07 15:00:42,635:WARNING:Only Model saved as it was a pipeline.
2021-09-07 15:00:42,643:INFO:Transformation Pipeline.pkl saved in current working directory
2021-09-07 15:00:42,643:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:00:42,643:INFO:save_model() successfully completed......................................
2021-09-07 15:00:42,707:INFO:SubProcess save_model() end ==================================
2021-09-07 15:00:42,739:INFO:create_model_container: 0
2021-09-07 15:00:42,739:INFO:master_model_container: 0
2021-09-07 15:00:42,739:INFO:display_container: 1
2021-09-07 15:00:42,739:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:00:42,739:INFO:setup() succesfully completed......................................
2021-09-07 15:00:43,109:INFO:Initializing create_model()
2021-09-07 15:00:43,109:INFO:create_model(estimator=rf, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 15:00:43,109:INFO:Checking exceptions
2021-09-07 15:00:43,109:INFO:Preparing display monitor
2021-09-07 15:00:43,133:INFO:Importing libraries
2021-09-07 15:00:43,133:INFO:Copying training dataset
2021-09-07 15:00:43,133:INFO:Defining folds
2021-09-07 15:00:43,133:INFO:Declaring metric variables
2021-09-07 15:00:43,141:INFO:Importing untrained model
2021-09-07 15:00:43,149:INFO:Random Forest Classifier Imported succesfully
2021-09-07 15:00:43,173:INFO:Starting cross validation
2021-09-07 15:00:43,173:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:00:46,825:INFO:Calculating mean and std
2021-09-07 15:00:46,833:INFO:Creating metrics dataframe
2021-09-07 15:00:46,849:INFO:Finalizing model
2021-09-07 15:00:47,705:INFO:Creating MLFlow logs
2021-09-07 15:00:47,745:INFO:Model: Random Forest Classifier
2021-09-07 15:00:47,785: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}
2021-09-07 15:00:47,849:INFO:Initializing predict_model()
2021-09-07 15:00:47,849:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 15:00:47,849:INFO:Checking exceptions
2021-09-07 15:00:47,849:INFO:Preloading libraries
2021-09-07 15:00:48,329:INFO:Uploading results into container
2021-09-07 15:00:48,329:INFO:Uploading model into container now
2021-09-07 15:00:48,345:INFO:create_model_container: 1
2021-09-07 15:00:48,345:INFO:master_model_container: 1
2021-09-07 15:00:48,345:INFO:display_container: 2
2021-09-07 15:00:48,345: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)
2021-09-07 15:00:48,345:INFO:create_model() succesfully completed......................................
2021-09-07 15:00:48,955:INFO:Initializing create_model()
2021-09-07 15:00:48,955:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 15:00:48,955:INFO:Checking exceptions
2021-09-07 15:00:48,955:INFO:Preparing display monitor
2021-09-07 15:00:48,971:INFO:Importing libraries
2021-09-07 15:00:48,971:INFO:Copying training dataset
2021-09-07 15:00:48,979:INFO:Defining folds
2021-09-07 15:00:48,979:INFO:Declaring metric variables
2021-09-07 15:00:48,987:INFO:Importing untrained model
2021-09-07 15:00:48,987:INFO:Declaring custom model
2021-09-07 15:00:48,995:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 15:00:49,019:INFO:Starting cross validation
2021-09-07 15:00:49,019:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:00:49,699:INFO:Calculating mean and std
2021-09-07 15:00:49,699:INFO:Creating metrics dataframe
2021-09-07 15:00:49,707:INFO:Finalizing model
2021-09-07 15:01:09,076:INFO:Initializing get_config()
2021-09-07 15:01:09,076:INFO:get_config(variable=prep_pipe)
2021-09-07 15:01:09,084:INFO:Global variable: prep_pipe returned as Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:01:09,084:INFO:get_config() succesfully completed......................................
2021-09-07 15:05:08,399:INFO:PyCaret Supervised Module
2021-09-07 15:05:08,399:INFO:ML Usecase: classification
2021-09-07 15:05:08,399:INFO:version 2.3.2
2021-09-07 15:05:08,399:INFO:Initializing setup()
2021-09-07 15:05:08,399:INFO:setup(target=stabf, 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=None, categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-09-07 15:05:08,399:INFO:Checking environment
2021-09-07 15:05:08,399:INFO:python_version: 3.7.11
2021-09-07 15:05:08,399:INFO:python_build: ('default', 'Jul 27 2021 09:42:29')
2021-09-07 15:05:08,399:INFO:machine: AMD64
2021-09-07 15:05:08,399:INFO:platform: Windows-10-10.0.19041-SP0
2021-09-07 15:05:08,399:INFO:Memory: svmem(total=16929984512, available=3061157888, percent=81.9, used=13868826624, free=3061157888)
2021-09-07 15:05:08,399:INFO:Physical Core: 6
2021-09-07 15:05:08,399:INFO:Logical Core: 12
2021-09-07 15:05:08,399:INFO:Checking libraries
2021-09-07 15:05:08,399:INFO:pd==1.3.1
2021-09-07 15:05:08,399:INFO:numpy==1.19.5
2021-09-07 15:05:08,399:INFO:sklearn==0.23.2
2021-09-07 15:05:08,399:INFO:lightgbm==3.2.1
2021-09-07 15:05:08,407:WARNING:catboost not found
2021-09-07 15:05:08,407:WARNING:xgboost not found
2021-09-07 15:05:08,407:INFO:mlflow==1.19.0
2021-09-07 15:05:08,407:INFO:Checking Exceptions
2021-09-07 15:05:08,407:INFO:Declaring global variables
2021-09-07 15:05:08,407:INFO:USI: 6dec
2021-09-07 15:05:08,407:INFO:pycaret_globals: {'USI', 'stratify_param', 'experiment__', 'data_before_preprocess', 'y', 'master_model_container', 'gpu_param', 'X', 'X_test', 'seed', 'create_model_container', 'fold_generator', 'exp_name_log', 'fix_imbalance_param', '_ml_usecase', '_all_metrics', 'target_param', 'logging_param', 'log_plots_param', '_gpu_n_jobs_param', '_internal_pipeline', 'html_param', 'transform_target_method_param', 'fix_imbalance_method_param', 'pycaret_globals', 'n_jobs_param', 'transform_target_param', 'fold_shuffle_param', '_all_models_internal', 'y_test', 'prep_pipe', 'iterative_imputation_iters_param', 'fold_param', 'imputation_classifier', 'fold_groups_param', 'X_train', 'fold_groups_param_full', 'imputation_regressor', 'y_train', '_available_plots', '_all_models', 'display_container'}
2021-09-07 15:05:08,407:INFO:Preparing display monitor
2021-09-07 15:05:08,407:INFO:Preparing display monitor
2021-09-07 15:05:08,423:INFO:Importing libraries
2021-09-07 15:05:08,423:INFO:Copying data for preprocessing
2021-09-07 15:05:08,431:INFO:Declaring preprocessing parameters
2021-09-07 15:05:08,439:INFO:Creating preprocessing pipeline
2021-09-07 15:05:08,455:INFO:Preprocessing pipeline created successfully
2021-09-07 15:05:08,455:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-09-07 15:05:08,455:INFO:Creating global containers
2021-09-07 15:05:08,455:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-09-07 15:05:08,728:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 15:05:08,728:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 15:05:08,816:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 15:05:08,816:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 15:05:08,824:INFO:Creating grid variables
2021-09-07 15:05:08,832:INFO:Logging experiment in MLFlow
2021-09-07 15:05:08,832:WARNING:Couldn't create mlflow experiment. Exception:
2021-09-07 15:05:08,832:WARNING:Traceback (most recent call last):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 1702, in setup
    mlflow.create_experiment(exp_name_log)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\fluent.py", line 875, in create_experiment
    return MlflowClient().create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\client.py", line 500, in create_experiment
    return self._tracking_client.create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\_tracking_service\client.py", line 167, in create_experiment
    return self.store.create_experiment(name=name, artifact_location=artifact_location,)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 313, in create_experiment
    self._validate_experiment_name(name)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 308, in _validate_experiment_name
    databricks_pb2.RESOURCE_ALREADY_EXISTS,
mlflow.exceptions.MlflowException: Experiment 'electrical_grid_1' already exists.

2021-09-07 15:05:08,935:INFO:SubProcess save_model() called ==================================
2021-09-07 15:05:08,943:INFO:Initializing save_model()
2021-09-07 15:05:08,943:INFO:save_model(model=Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('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=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False), verbose=False, kwargs={})
2021-09-07 15:05:08,943:INFO:Adding model into prep_pipe
2021-09-07 15:05:08,943:WARNING:Only Model saved as it was a pipeline.
2021-09-07 15:05:08,951:INFO:Transformation Pipeline.pkl saved in current working directory
2021-09-07 15:05:08,951:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:05:08,951:INFO:save_model() successfully completed......................................
2021-09-07 15:05:09,039:INFO:SubProcess save_model() end ==================================
2021-09-07 15:05:09,071:INFO:create_model_container: 0
2021-09-07 15:05:09,071:INFO:master_model_container: 0
2021-09-07 15:05:09,071:INFO:display_container: 1
2021-09-07 15:05:09,079:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:05:09,079:INFO:setup() succesfully completed......................................
2021-09-07 15:05:10,023:INFO:Initializing create_model()
2021-09-07 15:05:10,031:INFO:create_model(estimator=rf, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 15:05:10,031:INFO:Checking exceptions
2021-09-07 15:05:10,031:INFO:Preparing display monitor
2021-09-07 15:05:10,047:INFO:Importing libraries
2021-09-07 15:05:10,047:INFO:Copying training dataset
2021-09-07 15:05:10,047:INFO:Defining folds
2021-09-07 15:05:10,047:INFO:Declaring metric variables
2021-09-07 15:05:10,055:INFO:Importing untrained model
2021-09-07 15:05:10,071:INFO:Random Forest Classifier Imported succesfully
2021-09-07 15:05:10,095:INFO:Starting cross validation
2021-09-07 15:05:10,095:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:05:11,786:INFO:Calculating mean and std
2021-09-07 15:05:11,786:INFO:Creating metrics dataframe
2021-09-07 15:05:11,834:INFO:Finalizing model
2021-09-07 15:05:12,761:INFO:Creating MLFlow logs
2021-09-07 15:05:12,792:INFO:Model: Random Forest Classifier
2021-09-07 15:05:12,832: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}
2021-09-07 15:05:12,888:INFO:Initializing predict_model()
2021-09-07 15:05:12,888:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 15:05:12,888:INFO:Checking exceptions
2021-09-07 15:05:12,888:INFO:Preloading libraries
2021-09-07 15:05:13,389:INFO:Uploading results into container
2021-09-07 15:05:13,389:INFO:Uploading model into container now
2021-09-07 15:05:13,405:INFO:create_model_container: 1
2021-09-07 15:05:13,405:INFO:master_model_container: 1
2021-09-07 15:05:13,405:INFO:display_container: 2
2021-09-07 15:05:13,405: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)
2021-09-07 15:05:13,405:INFO:create_model() succesfully completed......................................
2021-09-07 15:05:19,366:INFO:Initializing get_config()
2021-09-07 15:05:19,366:INFO:get_config(variable=prep_pipe)
2021-09-07 15:05:19,374:INFO:Global variable: prep_pipe returned as Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:05:19,374:INFO:get_config() succesfully completed......................................
2021-09-07 15:05:31,358:INFO:Initializing create_model()
2021-09-07 15:05:31,358:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 15:05:31,358:INFO:Checking exceptions
2021-09-07 15:05:31,358:INFO:Preparing display monitor
2021-09-07 15:05:31,374:INFO:Importing libraries
2021-09-07 15:05:31,374:INFO:Copying training dataset
2021-09-07 15:05:31,382:INFO:Defining folds
2021-09-07 15:05:31,382:INFO:Declaring metric variables
2021-09-07 15:05:31,390:INFO:Importing untrained model
2021-09-07 15:05:31,390:INFO:Declaring custom model
2021-09-07 15:05:31,398:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 15:05:31,422:INFO:Starting cross validation
2021-09-07 15:05:31,422:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:05:31,470:INFO:Calculating mean and std
2021-09-07 15:05:31,470:INFO:Creating metrics dataframe
2021-09-07 15:05:31,478:INFO:Finalizing model
2021-09-07 15:07:27,789:INFO:PyCaret Supervised Module
2021-09-07 15:07:27,789:INFO:ML Usecase: classification
2021-09-07 15:07:27,789:INFO:version 2.3.2
2021-09-07 15:07:27,789:INFO:Initializing setup()
2021-09-07 15:07:27,789:INFO:setup(target=stabf, 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=['stabf'], categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-09-07 15:07:27,789:INFO:Checking environment
2021-09-07 15:07:27,789:INFO:python_version: 3.7.11
2021-09-07 15:07:27,789:INFO:python_build: ('default', 'Jul 27 2021 09:42:29')
2021-09-07 15:07:27,789:INFO:machine: AMD64
2021-09-07 15:07:27,789:INFO:platform: Windows-10-10.0.19041-SP0
2021-09-07 15:07:27,789:INFO:Memory: svmem(total=16929984512, available=3242262528, percent=80.8, used=13687721984, free=3242262528)
2021-09-07 15:07:27,789:INFO:Physical Core: 6
2021-09-07 15:07:27,789:INFO:Logical Core: 12
2021-09-07 15:07:27,789:INFO:Checking libraries
2021-09-07 15:07:27,789:INFO:pd==1.3.1
2021-09-07 15:07:27,789:INFO:numpy==1.19.5
2021-09-07 15:07:27,789:INFO:sklearn==0.23.2
2021-09-07 15:07:27,789:INFO:lightgbm==3.2.1
2021-09-07 15:07:27,797:WARNING:catboost not found
2021-09-07 15:07:27,797:WARNING:xgboost not found
2021-09-07 15:07:27,797:INFO:mlflow==1.19.0
2021-09-07 15:07:27,797:INFO:Checking Exceptions
2021-09-07 15:07:43,187:INFO:PyCaret Supervised Module
2021-09-07 15:07:43,187:INFO:ML Usecase: classification
2021-09-07 15:07:43,187:INFO:version 2.3.2
2021-09-07 15:07:43,187:INFO:Initializing setup()
2021-09-07 15:07:43,187:INFO:setup(target=stabf, 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=None, categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-09-07 15:07:43,187:INFO:Checking environment
2021-09-07 15:07:43,187:INFO:python_version: 3.7.11
2021-09-07 15:07:43,187:INFO:python_build: ('default', 'Jul 27 2021 09:42:29')
2021-09-07 15:07:43,187:INFO:machine: AMD64
2021-09-07 15:07:43,187:INFO:platform: Windows-10-10.0.19041-SP0
2021-09-07 15:07:43,187:INFO:Memory: svmem(total=16929984512, available=3367763968, percent=80.1, used=13562220544, free=3367763968)
2021-09-07 15:07:43,187:INFO:Physical Core: 6
2021-09-07 15:07:43,187:INFO:Logical Core: 12
2021-09-07 15:07:43,187:INFO:Checking libraries
2021-09-07 15:07:43,187:INFO:pd==1.3.1
2021-09-07 15:07:43,187:INFO:numpy==1.19.5
2021-09-07 15:07:43,187:INFO:sklearn==0.23.2
2021-09-07 15:07:43,187:INFO:lightgbm==3.2.1
2021-09-07 15:07:43,187:WARNING:catboost not found
2021-09-07 15:07:43,187:WARNING:xgboost not found
2021-09-07 15:07:43,187:INFO:mlflow==1.19.0
2021-09-07 15:07:43,187:INFO:Checking Exceptions
2021-09-07 15:07:43,187:INFO:Declaring global variables
2021-09-07 15:07:43,187:INFO:USI: 4050
2021-09-07 15:07:43,187:INFO:pycaret_globals: {'USI', 'stratify_param', 'experiment__', 'data_before_preprocess', 'y', 'master_model_container', 'gpu_param', 'X', 'X_test', 'seed', 'create_model_container', 'fold_generator', 'exp_name_log', 'fix_imbalance_param', '_ml_usecase', '_all_metrics', 'target_param', 'logging_param', 'log_plots_param', '_gpu_n_jobs_param', '_internal_pipeline', 'html_param', 'transform_target_method_param', 'fix_imbalance_method_param', 'pycaret_globals', 'n_jobs_param', 'transform_target_param', 'fold_shuffle_param', '_all_models_internal', 'y_test', 'prep_pipe', 'iterative_imputation_iters_param', 'fold_param', 'imputation_classifier', 'fold_groups_param', 'X_train', 'fold_groups_param_full', 'imputation_regressor', 'y_train', '_available_plots', '_all_models', 'display_container'}
2021-09-07 15:07:43,187:INFO:Preparing display monitor
2021-09-07 15:07:43,187:INFO:Preparing display monitor
2021-09-07 15:07:43,203:INFO:Importing libraries
2021-09-07 15:07:43,203:INFO:Copying data for preprocessing
2021-09-07 15:07:43,211:INFO:Declaring preprocessing parameters
2021-09-07 15:07:43,211:INFO:Creating preprocessing pipeline
2021-09-07 15:07:43,235:INFO:Preprocessing pipeline created successfully
2021-09-07 15:07:43,235:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-09-07 15:07:43,235:INFO:Creating global containers
2021-09-07 15:07:43,235:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-09-07 15:07:43,491:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 15:07:43,491:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 15:07:43,563:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 15:07:43,563:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 15:07:43,563:INFO:Creating grid variables
2021-09-07 15:07:43,579:INFO:Logging experiment in MLFlow
2021-09-07 15:07:43,579:WARNING:Couldn't create mlflow experiment. Exception:
2021-09-07 15:07:43,579:WARNING:Traceback (most recent call last):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 1702, in setup
    mlflow.create_experiment(exp_name_log)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\fluent.py", line 875, in create_experiment
    return MlflowClient().create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\client.py", line 500, in create_experiment
    return self._tracking_client.create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\_tracking_service\client.py", line 167, in create_experiment
    return self.store.create_experiment(name=name, artifact_location=artifact_location,)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 313, in create_experiment
    self._validate_experiment_name(name)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 308, in _validate_experiment_name
    databricks_pb2.RESOURCE_ALREADY_EXISTS,
mlflow.exceptions.MlflowException: Experiment 'electrical_grid_1' already exists.

2021-09-07 15:07:43,675:INFO:SubProcess save_model() called ==================================
2021-09-07 15:07:43,683:INFO:Initializing save_model()
2021-09-07 15:07:43,683:INFO:save_model(model=Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('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=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False), verbose=False, kwargs={})
2021-09-07 15:07:43,683:INFO:Adding model into prep_pipe
2021-09-07 15:07:43,683:WARNING:Only Model saved as it was a pipeline.
2021-09-07 15:07:43,691:INFO:Transformation Pipeline.pkl saved in current working directory
2021-09-07 15:07:43,691:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:07:43,691:INFO:save_model() successfully completed......................................
2021-09-07 15:07:43,779:INFO:SubProcess save_model() end ==================================
2021-09-07 15:07:43,803:INFO:create_model_container: 0
2021-09-07 15:07:43,803:INFO:master_model_container: 0
2021-09-07 15:07:43,803:INFO:display_container: 1
2021-09-07 15:07:43,811:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:07:43,811:INFO:setup() succesfully completed......................................
2021-09-07 15:08:43,354:INFO:PyCaret Supervised Module
2021-09-07 15:08:43,354:INFO:ML Usecase: classification
2021-09-07 15:08:43,354:INFO:version 2.3.1
2021-09-07 15:08:43,354:INFO:Initializing setup()
2021-09-07 15:08:43,354:INFO:setup(target=stabf, 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'}, train_size=0.8, test_data=None, preprocess=True, imputation_type=simple, iterative_imputation_iters=5, categorical_features=None, categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-09-07 15:08:43,354:INFO:Checking environment
2021-09-07 15:08:43,354:INFO:python_version: 3.7.11
2021-09-07 15:08:43,354:INFO:python_build: ('default', 'Jul 27 2021 09:42:29')
2021-09-07 15:08:43,354:INFO:machine: AMD64
2021-09-07 15:08:43,354:INFO:platform: Windows-10-10.0.19041-SP0
2021-09-07 15:08:43,354:INFO:Memory: svmem(total=16929984512, available=5219844096, percent=69.2, used=11710140416, free=5219844096)
2021-09-07 15:08:43,354:INFO:Physical Core: 6
2021-09-07 15:08:43,354:INFO:Logical Core: 12
2021-09-07 15:08:43,354:INFO:Checking libraries
2021-09-07 15:08:43,354:INFO:pd==1.3.1
2021-09-07 15:08:43,354:INFO:numpy==1.19.5
2021-09-07 15:08:43,354:INFO:sklearn==0.23.2
2021-09-07 15:08:43,362:INFO:lightgbm==3.2.1
2021-09-07 15:08:43,362:WARNING:catboost not found
2021-09-07 15:08:43,362:WARNING:xgboost not found
2021-09-07 15:08:43,722:INFO:mlflow==1.19.0
2021-09-07 15:08:43,722:INFO:Checking Exceptions
2021-09-07 15:08:43,722:INFO:Declaring global variables
2021-09-07 15:08:43,722:INFO:USI: 04ae
2021-09-07 15:08:43,722:INFO:pycaret_globals: {'y_test', 'fold_param', '_all_models', 'fold_groups_param', 'X_train', 'master_model_container', 'prep_pipe', 'fold_groups_param_full', 'iterative_imputation_iters_param', 'y', 'X', 'pycaret_globals', 'exp_name_log', 'target_param', 'stratify_param', 'y_train', '_available_plots', 'fold_shuffle_param', 'logging_param', 'fix_imbalance_method_param', 'USI', '_all_metrics', 'n_jobs_param', '_internal_pipeline', 'gpu_param', 'data_before_preprocess', 'html_param', 'X_test', 'imputation_regressor', 'log_plots_param', 'transform_target_method_param', 'imputation_classifier', '_gpu_n_jobs_param', 'display_container', '_all_models_internal', 'fix_imbalance_param', 'fold_generator', 'transform_target_param', 'seed', 'experiment__', 'create_model_container', '_ml_usecase'}
2021-09-07 15:08:43,722:INFO:Preparing display monitor
2021-09-07 15:08:43,722:INFO:Preparing display monitor
2021-09-07 15:08:43,730:INFO:Importing libraries
2021-09-07 15:08:43,730:INFO:Copying data for preprocessing
2021-09-07 15:08:43,738:INFO:Declaring preprocessing parameters
2021-09-07 15:08:43,738:INFO:Creating preprocessing pipeline
2021-09-07 15:08:43,762:INFO:Preprocessing pipeline created successfully
2021-09-07 15:08:43,762:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-09-07 15:08:43,762:INFO:Creating global containers
2021-09-07 15:08:43,762:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-09-07 15:08:44,002:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 15:08:44,002:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 15:08:44,074:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 15:08:44,074:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 15:08:44,074:INFO:Creating grid variables
2021-09-07 15:08:44,090:INFO:Logging experiment in MLFlow
2021-09-07 15:08:44,114:WARNING:Couldn't create mlflow experiment. Exception:
2021-09-07 15:08:44,114:WARNING:Traceback (most recent call last):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 1702, in setup
    mlflow.create_experiment(exp_name_log)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\fluent.py", line 875, in create_experiment
    return MlflowClient().create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\client.py", line 500, in create_experiment
    return self._tracking_client.create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\_tracking_service\client.py", line 167, in create_experiment
    return self.store.create_experiment(name=name, artifact_location=artifact_location,)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 313, in create_experiment
    self._validate_experiment_name(name)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 308, in _validate_experiment_name
    databricks_pb2.RESOURCE_ALREADY_EXISTS,
mlflow.exceptions.MlflowException: Experiment 'electrical_grid_1' already exists.

2021-09-07 15:08:44,386:INFO:SubProcess save_model() called ==================================
2021-09-07 15:08:44,394:INFO:Initializing save_model()
2021-09-07 15:08:44,394:INFO:save_model(model=Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('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=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False), verbose=False, kwargs={})
2021-09-07 15:08:44,394:INFO:Adding model into prep_pipe
2021-09-07 15:08:44,394:WARNING:Only Model saved as it was a pipeline.
2021-09-07 15:08:44,394:INFO:Transformation Pipeline.pkl saved in current working directory
2021-09-07 15:08:44,402:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:08:44,402:INFO:save_model() succesfully completed......................................
2021-09-07 15:08:44,450:INFO:SubProcess save_model() end ==================================
2021-09-07 15:08:44,482:INFO:create_model_container: 0
2021-09-07 15:08:44,482:INFO:master_model_container: 0
2021-09-07 15:08:44,482:INFO:display_container: 1
2021-09-07 15:08:44,482:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:08:44,482:INFO:setup() succesfully completed......................................
2021-09-07 15:08:44,546:INFO:Initializing create_model()
2021-09-07 15:08:44,546:INFO:create_model(estimator=rf, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 15:08:44,546:INFO:Checking exceptions
2021-09-07 15:08:44,546:INFO:Preparing display monitor
2021-09-07 15:08:44,570:INFO:Importing libraries
2021-09-07 15:08:44,570:INFO:Copying training dataset
2021-09-07 15:08:44,570:INFO:Defining folds
2021-09-07 15:08:44,570:INFO:Declaring metric variables
2021-09-07 15:08:44,578:INFO:Importing untrained model
2021-09-07 15:08:44,594:INFO:Random Forest Classifier Imported succesfully
2021-09-07 15:08:44,610:INFO:Starting cross validation
2021-09-07 15:08:44,610:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:08:49,237:INFO:Calculating mean and std
2021-09-07 15:08:49,237:INFO:Creating metrics dataframe
2021-09-07 15:08:49,269:INFO:Finalizing model
2021-09-07 15:08:50,125:INFO:Creating MLFlow logs
2021-09-07 15:08:50,157:INFO:Model: Random Forest Classifier
2021-09-07 15:08:50,205: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}
2021-09-07 15:08:50,269:INFO:Initializing predict_model()
2021-09-07 15:08:50,269:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 15:08:50,269:INFO:Checking exceptions
2021-09-07 15:08:50,269:INFO:Preloading libraries
2021-09-07 15:08:50,713:INFO:Uploading results into container
2021-09-07 15:08:50,713:INFO:Uploading model into container now
2021-09-07 15:08:50,729:INFO:create_model_container: 1
2021-09-07 15:08:50,729:INFO:master_model_container: 1
2021-09-07 15:08:50,729:INFO:display_container: 2
2021-09-07 15:08:50,729: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)
2021-09-07 15:08:50,729:INFO:create_model() succesfully completed......................................
2021-09-07 15:08:51,286:INFO:Initializing get_config()
2021-09-07 15:08:51,286:INFO:get_config(variable=prep_pipe)
2021-09-07 15:08:51,294:INFO:Global variable: prep_pipe returned as Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:08:51,294:INFO:get_config() succesfully completed......................................
2021-09-07 15:08:51,444:INFO:Initializing create_model()
2021-09-07 15:08:51,444:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 15:08:51,444:INFO:Checking exceptions
2021-09-07 15:08:51,444:INFO:Preparing display monitor
2021-09-07 15:08:51,468:INFO:Importing libraries
2021-09-07 15:08:51,468:INFO:Copying training dataset
2021-09-07 15:08:51,468:INFO:Defining folds
2021-09-07 15:08:51,468:INFO:Declaring metric variables
2021-09-07 15:08:51,476:INFO:Importing untrained model
2021-09-07 15:08:51,476:INFO:Declaring custom model
2021-09-07 15:08:51,492:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 15:08:51,516:INFO:Starting cross validation
2021-09-07 15:08:51,516:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:08:54,866:INFO:Calculating mean and std
2021-09-07 15:08:54,866:INFO:Creating metrics dataframe
2021-09-07 15:08:54,885:INFO:Finalizing model
2021-09-07 15:14:25,292:INFO:Initializing create_model()
2021-09-07 15:14:25,292:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 15:14:25,292:INFO:Checking exceptions
2021-09-07 15:14:25,292:INFO:Preparing display monitor
2021-09-07 15:14:25,316:INFO:Importing libraries
2021-09-07 15:14:25,316:INFO:Copying training dataset
2021-09-07 15:14:25,316:INFO:Defining folds
2021-09-07 15:14:25,316:INFO:Declaring metric variables
2021-09-07 15:14:25,332:INFO:Importing untrained model
2021-09-07 15:14:25,332:INFO:Declaring custom model
2021-09-07 15:14:25,340:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 15:14:25,364:INFO:Starting cross validation
2021-09-07 15:14:25,364:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:14:28,682:INFO:Calculating mean and std
2021-09-07 15:14:28,682:INFO:Creating metrics dataframe
2021-09-07 15:14:28,698:INFO:Finalizing model
2021-09-07 15:15:33,509:INFO:Initializing create_model()
2021-09-07 15:15:33,509:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 15:15:33,509:INFO:Checking exceptions
2021-09-07 15:15:33,509:INFO:Preparing display monitor
2021-09-07 15:15:33,533:INFO:Importing libraries
2021-09-07 15:15:33,533:INFO:Copying training dataset
2021-09-07 15:15:33,533:INFO:Defining folds
2021-09-07 15:15:33,533:INFO:Declaring metric variables
2021-09-07 15:15:33,541:INFO:Importing untrained model
2021-09-07 15:15:33,541:INFO:Declaring custom model
2021-09-07 15:15:33,549:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 15:15:33,573:INFO:Starting cross validation
2021-09-07 15:15:33,573:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:15:47,090:INFO:Calculating mean and std
2021-09-07 15:15:47,090:INFO:Creating metrics dataframe
2021-09-07 15:15:47,106:INFO:Finalizing model
2021-09-07 15:16:00,204:INFO:Creating MLFlow logs
2021-09-07 15:16:00,222:INFO:Model: NeuralNetClassifier
2021-09-07 15:16:00,248:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class '__main__.customBCELoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 30, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x000001928B7FEEC8>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x000001928B5E9C48>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x000001928BE01708>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x000001928BB05F48>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x0000019288CC8438>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x000001928C1F9588>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-09-07 15:16:00,332:INFO:Initializing predict_model()
2021-09-07 15:16:00,332:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 15:16:00,332:INFO:Checking exceptions
2021-09-07 15:16:00,332:INFO:Preloading libraries
2021-09-07 15:16:00,334:WARNING:Couldn't create holdout prediction for model, exception below:
2021-09-07 15:16:00,334:WARNING:Traceback (most recent call last):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 9985, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 8405, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\torch\nn\modules\module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-09-07 15:16:00,564:INFO:Uploading results into container
2021-09-07 15:16:00,564:INFO:Uploading model into container now
2021-09-07 15:16:00,575:INFO:create_model_container: 2
2021-09-07 15:16:00,575:INFO:master_model_container: 2
2021-09-07 15:16:00,575:INFO:display_container: 3
2021-09-07 15:16:00,575:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-09-07 15:16:00,575:INFO:create_model() succesfully completed......................................
2021-09-07 15:16:46,911:INFO:PyCaret Supervised Module
2021-09-07 15:16:46,911:INFO:ML Usecase: classification
2021-09-07 15:16:46,911:INFO:version 2.3.1
2021-09-07 15:16:46,911:INFO:Initializing setup()
2021-09-07 15:16:46,911:INFO:setup(target=stabf, 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'}, train_size=0.8, test_data=None, preprocess=True, imputation_type=simple, iterative_imputation_iters=5, categorical_features=None, categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-09-07 15:16:46,911:INFO:Checking environment
2021-09-07 15:16:46,911:INFO:python_version: 3.7.11
2021-09-07 15:16:46,911:INFO:python_build: ('default', 'Jul 27 2021 09:42:29')
2021-09-07 15:16:46,911:INFO:machine: AMD64
2021-09-07 15:16:46,911:INFO:platform: Windows-10-10.0.19041-SP0
2021-09-07 15:16:46,911:INFO:Memory: svmem(total=16929984512, available=5346983936, percent=68.4, used=11583000576, free=5346983936)
2021-09-07 15:16:46,911:INFO:Physical Core: 6
2021-09-07 15:16:46,911:INFO:Logical Core: 12
2021-09-07 15:16:46,911:INFO:Checking libraries
2021-09-07 15:16:46,911:INFO:pd==1.3.1
2021-09-07 15:16:46,911:INFO:numpy==1.19.5
2021-09-07 15:16:46,911:INFO:sklearn==0.23.2
2021-09-07 15:16:46,911:INFO:lightgbm==3.2.1
2021-09-07 15:16:46,911:WARNING:catboost not found
2021-09-07 15:16:46,911:WARNING:xgboost not found
2021-09-07 15:16:47,284:INFO:mlflow==1.19.0
2021-09-07 15:16:47,284:INFO:Checking Exceptions
2021-09-07 15:16:47,284:INFO:Declaring global variables
2021-09-07 15:16:47,284:INFO:USI: 426f
2021-09-07 15:16:47,284:INFO:pycaret_globals: {'prep_pipe', 'imputation_classifier', 'seed', 'gpu_param', 'fold_shuffle_param', '_all_models_internal', '_all_metrics', 'stratify_param', 'iterative_imputation_iters_param', 'target_param', 'USI', 'pycaret_globals', 'y', 'fold_generator', '_gpu_n_jobs_param', 'html_param', '_ml_usecase', 'fix_imbalance_method_param', 'exp_name_log', 'data_before_preprocess', 'logging_param', 'y_test', '_internal_pipeline', 'X', 'fold_groups_param', 'display_container', 'transform_target_param', 'log_plots_param', 'fix_imbalance_param', '_available_plots', 'imputation_regressor', 'experiment__', 'create_model_container', 'X_test', 'master_model_container', 'y_train', 'fold_groups_param_full', '_all_models', 'X_train', 'n_jobs_param', 'transform_target_method_param', 'fold_param'}
2021-09-07 15:16:47,284:INFO:Preparing display monitor
2021-09-07 15:16:47,284:INFO:Preparing display monitor
2021-09-07 15:16:47,292:INFO:Importing libraries
2021-09-07 15:16:47,292:INFO:Copying data for preprocessing
2021-09-07 15:16:47,300:INFO:Declaring preprocessing parameters
2021-09-07 15:16:47,300:INFO:Creating preprocessing pipeline
2021-09-07 15:16:47,324:INFO:Preprocessing pipeline created successfully
2021-09-07 15:16:47,324:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-09-07 15:16:47,324:INFO:Creating global containers
2021-09-07 15:16:47,324:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-09-07 15:16:47,580:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 15:16:47,580:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 15:16:47,660:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 15:16:47,660:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 15:16:47,660:INFO:Creating grid variables
2021-09-07 15:16:47,668:INFO:Logging experiment in MLFlow
2021-09-07 15:16:47,700:WARNING:Couldn't create mlflow experiment. Exception:
2021-09-07 15:16:47,700:WARNING:Traceback (most recent call last):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 1702, in setup
    mlflow.create_experiment(exp_name_log)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\fluent.py", line 875, in create_experiment
    return MlflowClient().create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\client.py", line 500, in create_experiment
    return self._tracking_client.create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\_tracking_service\client.py", line 167, in create_experiment
    return self.store.create_experiment(name=name, artifact_location=artifact_location,)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 313, in create_experiment
    self._validate_experiment_name(name)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 308, in _validate_experiment_name
    databricks_pb2.RESOURCE_ALREADY_EXISTS,
mlflow.exceptions.MlflowException: Experiment 'electrical_grid_1' already exists.

2021-09-07 15:16:47,980:INFO:SubProcess save_model() called ==================================
2021-09-07 15:16:47,988:INFO:Initializing save_model()
2021-09-07 15:16:47,988:INFO:save_model(model=Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('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=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False), verbose=False, kwargs={})
2021-09-07 15:16:47,988:INFO:Adding model into prep_pipe
2021-09-07 15:16:47,988:WARNING:Only Model saved as it was a pipeline.
2021-09-07 15:16:47,996:INFO:Transformation Pipeline.pkl saved in current working directory
2021-09-07 15:16:47,996:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:16:47,996:INFO:save_model() succesfully completed......................................
2021-09-07 15:16:48,052:INFO:SubProcess save_model() end ==================================
2021-09-07 15:16:48,076:INFO:create_model_container: 0
2021-09-07 15:16:48,076:INFO:master_model_container: 0
2021-09-07 15:16:48,076:INFO:display_container: 1
2021-09-07 15:16:48,076:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:16:48,076:INFO:setup() succesfully completed......................................
2021-09-07 15:16:48,140:INFO:Initializing create_model()
2021-09-07 15:16:48,140:INFO:create_model(estimator=rf, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 15:16:48,140:INFO:Checking exceptions
2021-09-07 15:16:48,140:INFO:Preparing display monitor
2021-09-07 15:16:48,164:INFO:Importing libraries
2021-09-07 15:16:48,164:INFO:Copying training dataset
2021-09-07 15:16:48,164:INFO:Defining folds
2021-09-07 15:16:48,164:INFO:Declaring metric variables
2021-09-07 15:16:48,172:INFO:Importing untrained model
2021-09-07 15:16:48,180:INFO:Random Forest Classifier Imported succesfully
2021-09-07 15:16:48,204:INFO:Starting cross validation
2021-09-07 15:16:48,204:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:16:53,209:INFO:Calculating mean and std
2021-09-07 15:16:53,217:INFO:Creating metrics dataframe
2021-09-07 15:16:53,233:INFO:Finalizing model
2021-09-07 15:16:54,105:INFO:Creating MLFlow logs
2021-09-07 15:16:54,137:INFO:Model: Random Forest Classifier
2021-09-07 15:16:54,177: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}
2021-09-07 15:16:54,241:INFO:Initializing predict_model()
2021-09-07 15:16:54,241:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 15:16:54,241:INFO:Checking exceptions
2021-09-07 15:16:54,241:INFO:Preloading libraries
2021-09-07 15:16:54,685:INFO:Uploading results into container
2021-09-07 15:16:54,685:INFO:Uploading model into container now
2021-09-07 15:16:54,701:INFO:create_model_container: 1
2021-09-07 15:16:54,701:INFO:master_model_container: 1
2021-09-07 15:16:54,701:INFO:display_container: 2
2021-09-07 15:16:54,701: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)
2021-09-07 15:16:54,701:INFO:create_model() succesfully completed......................................
2021-09-07 15:17:13,966:INFO:PyCaret Supervised Module
2021-09-07 15:17:13,966:INFO:ML Usecase: classification
2021-09-07 15:17:13,966:INFO:version 2.3.1
2021-09-07 15:17:13,966:INFO:Initializing setup()
2021-09-07 15:17:13,966:INFO:setup(target=stabf, 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'}, train_size=0.8, test_data=None, preprocess=True, imputation_type=simple, iterative_imputation_iters=5, categorical_features=None, categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-09-07 15:17:13,966:INFO:Checking environment
2021-09-07 15:17:13,966:INFO:python_version: 3.7.11
2021-09-07 15:17:13,966:INFO:python_build: ('default', 'Jul 27 2021 09:42:29')
2021-09-07 15:17:13,966:INFO:machine: AMD64
2021-09-07 15:17:13,966:INFO:platform: Windows-10-10.0.19041-SP0
2021-09-07 15:17:13,966:INFO:Memory: svmem(total=16929984512, available=5958262784, percent=64.8, used=10971721728, free=5958262784)
2021-09-07 15:17:13,966:INFO:Physical Core: 6
2021-09-07 15:17:13,966:INFO:Logical Core: 12
2021-09-07 15:17:13,966:INFO:Checking libraries
2021-09-07 15:17:13,966:INFO:pd==1.3.1
2021-09-07 15:17:13,966:INFO:numpy==1.19.5
2021-09-07 15:17:13,966:INFO:sklearn==0.23.2
2021-09-07 15:17:13,966:INFO:lightgbm==3.2.1
2021-09-07 15:17:13,966:WARNING:catboost not found
2021-09-07 15:17:13,966:WARNING:xgboost not found
2021-09-07 15:17:14,326:INFO:mlflow==1.19.0
2021-09-07 15:17:14,326:INFO:Checking Exceptions
2021-09-07 15:17:14,326:INFO:Declaring global variables
2021-09-07 15:17:14,326:INFO:USI: 1168
2021-09-07 15:17:14,326:INFO:pycaret_globals: {'fold_groups_param', 'USI', 'fold_shuffle_param', 'pycaret_globals', 'create_model_container', '_all_models_internal', 'seed', 'fold_param', 'data_before_preprocess', 'transform_target_param', 'imputation_classifier', 'fix_imbalance_param', 'X_test', 'fix_imbalance_method_param', '_gpu_n_jobs_param', 'imputation_regressor', 'n_jobs_param', 'stratify_param', 'exp_name_log', 'logging_param', 'html_param', 'gpu_param', '_all_metrics', '_ml_usecase', 'prep_pipe', 'y_test', 'iterative_imputation_iters_param', 'y', 'log_plots_param', 'target_param', 'master_model_container', 'fold_generator', 'fold_groups_param_full', '_available_plots', 'X', 'transform_target_method_param', '_all_models', 'X_train', '_internal_pipeline', 'experiment__', 'y_train', 'display_container'}
2021-09-07 15:17:14,326:INFO:Preparing display monitor
2021-09-07 15:17:14,326:INFO:Preparing display monitor
2021-09-07 15:17:14,333:INFO:Importing libraries
2021-09-07 15:17:14,333:INFO:Copying data for preprocessing
2021-09-07 15:17:14,341:INFO:Declaring preprocessing parameters
2021-09-07 15:17:14,341:INFO:Creating preprocessing pipeline
2021-09-07 15:17:14,365:INFO:Preprocessing pipeline created successfully
2021-09-07 15:17:14,365:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-09-07 15:17:14,365:INFO:Creating global containers
2021-09-07 15:17:14,365:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-09-07 15:17:14,614:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 15:17:14,614:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 15:17:14,689:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 15:17:14,691:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 15:17:14,691:INFO:Creating grid variables
2021-09-07 15:17:14,699:INFO:Logging experiment in MLFlow
2021-09-07 15:17:14,723:WARNING:Couldn't create mlflow experiment. Exception:
2021-09-07 15:17:14,723:WARNING:Traceback (most recent call last):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 1702, in setup
    mlflow.create_experiment(exp_name_log)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\fluent.py", line 875, in create_experiment
    return MlflowClient().create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\client.py", line 500, in create_experiment
    return self._tracking_client.create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\_tracking_service\client.py", line 167, in create_experiment
    return self.store.create_experiment(name=name, artifact_location=artifact_location,)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 313, in create_experiment
    self._validate_experiment_name(name)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 308, in _validate_experiment_name
    databricks_pb2.RESOURCE_ALREADY_EXISTS,
mlflow.exceptions.MlflowException: Experiment 'electrical_grid_1' already exists.

2021-09-07 15:17:14,995:INFO:SubProcess save_model() called ==================================
2021-09-07 15:17:15,003:INFO:Initializing save_model()
2021-09-07 15:17:15,003:INFO:save_model(model=Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('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=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False), verbose=False, kwargs={})
2021-09-07 15:17:15,003:INFO:Adding model into prep_pipe
2021-09-07 15:17:15,003:WARNING:Only Model saved as it was a pipeline.
2021-09-07 15:17:15,011:INFO:Transformation Pipeline.pkl saved in current working directory
2021-09-07 15:17:15,019:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:17:15,019:INFO:save_model() succesfully completed......................................
2021-09-07 15:17:15,067:INFO:SubProcess save_model() end ==================================
2021-09-07 15:17:15,091:INFO:create_model_container: 0
2021-09-07 15:17:15,091:INFO:master_model_container: 0
2021-09-07 15:17:15,091:INFO:display_container: 1
2021-09-07 15:17:15,099:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:17:15,099:INFO:setup() succesfully completed......................................
2021-09-07 15:17:15,157:INFO:Initializing create_model()
2021-09-07 15:17:15,157:INFO:create_model(estimator=rf, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 15:17:15,157:INFO:Checking exceptions
2021-09-07 15:17:15,157:INFO:Preparing display monitor
2021-09-07 15:17:15,183:INFO:Importing libraries
2021-09-07 15:17:15,183:INFO:Copying training dataset
2021-09-07 15:17:15,183:INFO:Defining folds
2021-09-07 15:17:15,183:INFO:Declaring metric variables
2021-09-07 15:17:15,191:INFO:Importing untrained model
2021-09-07 15:17:15,199:INFO:Random Forest Classifier Imported succesfully
2021-09-07 15:17:15,223:INFO:Starting cross validation
2021-09-07 15:17:15,223:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:17:19,809:INFO:Calculating mean and std
2021-09-07 15:17:19,809:INFO:Creating metrics dataframe
2021-09-07 15:17:19,825:INFO:Finalizing model
2021-09-07 15:17:20,664:INFO:Creating MLFlow logs
2021-09-07 15:17:20,696:INFO:Model: Random Forest Classifier
2021-09-07 15:17:20,744: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}
2021-09-07 15:17:20,808:INFO:Initializing predict_model()
2021-09-07 15:17:20,808:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 15:17:20,808:INFO:Checking exceptions
2021-09-07 15:17:20,808:INFO:Preloading libraries
2021-09-07 15:17:21,276:INFO:Uploading results into container
2021-09-07 15:17:21,276:INFO:Uploading model into container now
2021-09-07 15:17:21,292:INFO:create_model_container: 1
2021-09-07 15:17:21,292:INFO:master_model_container: 1
2021-09-07 15:17:21,292:INFO:display_container: 2
2021-09-07 15:17:21,292: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)
2021-09-07 15:17:21,292:INFO:create_model() succesfully completed......................................
2021-09-07 15:17:21,850:INFO:Initializing create_model()
2021-09-07 15:17:21,850:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 15:17:21,850:INFO:Checking exceptions
2021-09-07 15:17:21,850:INFO:Preparing display monitor
2021-09-07 15:17:21,875:INFO:Importing libraries
2021-09-07 15:17:21,875:INFO:Copying training dataset
2021-09-07 15:17:21,878:INFO:Defining folds
2021-09-07 15:17:21,878:INFO:Declaring metric variables
2021-09-07 15:17:21,890:INFO:Importing untrained model
2021-09-07 15:17:21,890:INFO:Declaring custom model
2021-09-07 15:17:21,901:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 15:17:21,927:INFO:Starting cross validation
2021-09-07 15:17:21,927:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:17:35,567:INFO:Calculating mean and std
2021-09-07 15:17:35,567:INFO:Creating metrics dataframe
2021-09-07 15:17:35,575:INFO:Finalizing model
2021-09-07 15:17:48,225:INFO:Creating MLFlow logs
2021-09-07 15:17:48,241:INFO:Model: NeuralNetClassifier
2021-09-07 15:17:48,273:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class '__main__.customNLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 30, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x000001E708D38908>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x000001E7119AD648>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x000001E7119C7B08>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x000001E708D45908>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x000001E708C97558>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x000001E708D1F908>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-09-07 15:17:48,393:INFO:Initializing predict_model()
2021-09-07 15:17:48,393:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 15:17:48,393:INFO:Checking exceptions
2021-09-07 15:17:48,393:INFO:Preloading libraries
2021-09-07 15:17:48,401:WARNING:Couldn't create holdout prediction for model, exception below:
2021-09-07 15:17:48,401:WARNING:Traceback (most recent call last):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 9985, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 8405, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\torch\nn\modules\module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-09-07 15:17:48,633:INFO:Uploading results into container
2021-09-07 15:17:48,633:INFO:Uploading model into container now
2021-09-07 15:17:48,649:INFO:create_model_container: 2
2021-09-07 15:17:48,649:INFO:master_model_container: 2
2021-09-07 15:17:48,649:INFO:display_container: 3
2021-09-07 15:17:48,657:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-09-07 15:17:48,657:INFO:create_model() succesfully completed......................................
2021-09-07 15:17:48,745:INFO:Initializing compare_models()
2021-09-07 15:17:48,745:INFO:compare_models(include=[Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 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)], fold=None, round=4, cross_validation=True, sort=AUC, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-09-07 15:17:48,745:INFO:Checking exceptions
2021-09-07 15:17:48,745:INFO:Preparing display monitor
2021-09-07 15:17:48,745:INFO:Preparing display monitor
2021-09-07 15:17:48,793:INFO:Initializing custom model NeuralNetClassifier
2021-09-07 15:17:48,793:INFO:Total runtime is 0.0 minutes
2021-09-07 15:17:48,809:INFO:SubProcess create_model() called ==================================
2021-09-07 15:17:48,809:INFO:Initializing create_model()
2021-09-07 15:17:48,809:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 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, display=<pycaret.internal.Display.Display object at 0x000001E708D383C8>, kwargs={})
2021-09-07 15:17:48,809:INFO:Checking exceptions
2021-09-07 15:17:48,809:INFO:Importing libraries
2021-09-07 15:17:48,809:INFO:Copying training dataset
2021-09-07 15:17:48,809:INFO:Defining folds
2021-09-07 15:17:48,809:INFO:Declaring metric variables
2021-09-07 15:17:48,825:INFO:Importing untrained model
2021-09-07 15:17:48,825:INFO:Declaring custom model
2021-09-07 15:17:48,833:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 15:17:48,857:INFO:Starting cross validation
2021-09-07 15:17:48,857:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:18:09,002:INFO:Calculating mean and std
2021-09-07 15:18:09,002:INFO:Creating metrics dataframe
2021-09-07 15:18:09,011:INFO:Uploading results into container
2021-09-07 15:18:09,011:INFO:Uploading model into container now
2021-09-07 15:18:09,011:INFO:create_model_container: 3
2021-09-07 15:18:09,011:INFO:master_model_container: 3
2021-09-07 15:18:09,011:INFO:display_container: 4
2021-09-07 15:18:09,011:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False)
2021-09-07 15:18:09,011:INFO:create_model() succesfully completed......................................
2021-09-07 15:18:09,081:INFO:SubProcess create_model() end ==================================
2021-09-07 15:18:09,081:INFO:Creating metrics dataframe
2021-09-07 15:18:09,102:INFO:Initializing custom model Random Forest Classifier
2021-09-07 15:18:09,102:INFO:Total runtime is 0.3384785532951355 minutes
2021-09-07 15:18:09,117:INFO:SubProcess create_model() called ==================================
2021-09-07 15:18:09,117:INFO:Initializing create_model()
2021-09-07 15:18:09,117:INFO:create_model(estimator=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), 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, display=<pycaret.internal.Display.Display object at 0x000001E708D383C8>, kwargs={})
2021-09-07 15:18:09,117:INFO:Checking exceptions
2021-09-07 15:18:09,117:INFO:Importing libraries
2021-09-07 15:18:09,117:INFO:Copying training dataset
2021-09-07 15:18:09,117:INFO:Defining folds
2021-09-07 15:18:09,117:INFO:Declaring metric variables
2021-09-07 15:18:09,122:INFO:Importing untrained model
2021-09-07 15:18:09,122:INFO:Declaring custom model
2021-09-07 15:18:09,132:INFO:Random Forest Classifier Imported succesfully
2021-09-07 15:18:09,157:INFO:Starting cross validation
2021-09-07 15:18:09,157:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:18:10,854:INFO:Calculating mean and std
2021-09-07 15:18:10,854:INFO:Creating metrics dataframe
2021-09-07 15:18:10,859:INFO:Uploading results into container
2021-09-07 15:18:10,859:INFO:Uploading model into container now
2021-09-07 15:18:10,859:INFO:create_model_container: 4
2021-09-07 15:18:10,859:INFO:master_model_container: 4
2021-09-07 15:18:10,859:INFO:display_container: 4
2021-09-07 15:18:10,859: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)
2021-09-07 15:18:10,859:INFO:create_model() succesfully completed......................................
2021-09-07 15:18:10,921:INFO:SubProcess create_model() end ==================================
2021-09-07 15:18:10,921:INFO:Creating metrics dataframe
2021-09-07 15:18:10,970:INFO:Initializing create_model()
2021-09-07 15:18:10,970:INFO:create_model(estimator=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), 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, display=None, kwargs={})
2021-09-07 15:18:10,970:INFO:Checking exceptions
2021-09-07 15:18:10,970:INFO:Importing libraries
2021-09-07 15:18:10,970:INFO:Copying training dataset
2021-09-07 15:18:10,970:INFO:Defining folds
2021-09-07 15:18:10,970:INFO:Declaring metric variables
2021-09-07 15:18:10,970:INFO:Importing untrained model
2021-09-07 15:18:10,970:INFO:Declaring custom model
2021-09-07 15:18:10,970:INFO:Random Forest Classifier Imported succesfully
2021-09-07 15:18:10,970:INFO:Cross validation set to False
2021-09-07 15:18:10,970:INFO:Fitting Model
2021-09-07 15:18:11,819: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)
2021-09-07 15:18:11,819:INFO:create_models() succesfully completed......................................
2021-09-07 15:18:11,890:INFO:Creating MLFlow logs
2021-09-07 15:18:11,908:INFO:Model: Random Forest Classifier
2021-09-07 15:18:11,932: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}
2021-09-07 15:18:11,995:INFO:Initializing predict_model()
2021-09-07 15:18:11,995:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 15:18:11,995:INFO:Checking exceptions
2021-09-07 15:18:11,995:INFO:Preloading libraries
2021-09-07 15:18:12,551:INFO:Creating MLFlow logs
2021-09-07 15:18:12,572:INFO:Model: NeuralNetClassifier
2021-09-07 15:18:12,612:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class '__main__.customNLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 30, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x000001E7118FF248>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x000001E703DB03C8>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x000001E708D2CD08>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x000001E708D2C148>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x000001E708C97558>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x000001E711A02E08>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-09-07 15:18:12,813:INFO:create_model_container: 4
2021-09-07 15:18:12,813:INFO:master_model_container: 4
2021-09-07 15:18:12,813:INFO:display_container: 4
2021-09-07 15:18:12,813: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)
2021-09-07 15:18:12,813:INFO:compare_models() succesfully completed......................................
2021-09-07 15:19:57,532:INFO:PyCaret Supervised Module
2021-09-07 15:19:57,532:INFO:ML Usecase: classification
2021-09-07 15:19:57,532:INFO:version 2.3.1
2021-09-07 15:19:57,532:INFO:Initializing setup()
2021-09-07 15:19:57,532:INFO:setup(target=stabf, 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'}, train_size=0.8, test_data=None, preprocess=True, imputation_type=simple, iterative_imputation_iters=5, categorical_features=None, categorical_imputation=constant, categorical_iterative_imputer=lightgbm, ordinal_features=None, high_cardinality_features=None, high_cardinality_method=frequency, numeric_features=None, 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=False, rare_level_threshold=0.1, 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=electrical_grid_1, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2021-09-07 15:19:57,532:INFO:Checking environment
2021-09-07 15:19:57,532:INFO:python_version: 3.7.11
2021-09-07 15:19:57,532:INFO:python_build: ('default', 'Jul 27 2021 09:42:29')
2021-09-07 15:19:57,532:INFO:machine: AMD64
2021-09-07 15:19:57,535:INFO:platform: Windows-10-10.0.19041-SP0
2021-09-07 15:19:57,535:INFO:Memory: svmem(total=16929984512, available=5260472320, percent=68.9, used=11669512192, free=5260472320)
2021-09-07 15:19:57,535:INFO:Physical Core: 6
2021-09-07 15:19:57,535:INFO:Logical Core: 12
2021-09-07 15:19:57,535:INFO:Checking libraries
2021-09-07 15:19:57,535:INFO:pd==1.3.1
2021-09-07 15:19:57,535:INFO:numpy==1.19.5
2021-09-07 15:19:57,535:INFO:sklearn==0.23.2
2021-09-07 15:19:57,535:INFO:lightgbm==3.2.1
2021-09-07 15:19:57,535:WARNING:catboost not found
2021-09-07 15:19:57,535:WARNING:xgboost not found
2021-09-07 15:19:57,896:INFO:mlflow==1.19.0
2021-09-07 15:19:57,900:INFO:Checking Exceptions
2021-09-07 15:19:57,900:INFO:Declaring global variables
2021-09-07 15:19:57,900:INFO:USI: 86c8
2021-09-07 15:19:57,900:INFO:pycaret_globals: {'_all_metrics', 'transform_target_param', 'fold_param', 'y_test', 'USI', 'master_model_container', 'create_model_container', 'html_param', 'fold_generator', 'stratify_param', 'transform_target_method_param', 'fix_imbalance_param', '_internal_pipeline', 'X_train', 'logging_param', 'imputation_classifier', '_gpu_n_jobs_param', 'target_param', 'imputation_regressor', '_ml_usecase', '_all_models', '_available_plots', 'prep_pipe', 'y_train', 'fold_groups_param', 'data_before_preprocess', 'y', 'display_container', 'fold_groups_param_full', 'n_jobs_param', 'gpu_param', 'X', 'X_test', 'exp_name_log', 'pycaret_globals', 'seed', 'log_plots_param', '_all_models_internal', 'experiment__', 'fix_imbalance_method_param', 'iterative_imputation_iters_param', 'fold_shuffle_param'}
2021-09-07 15:19:57,900:INFO:Preparing display monitor
2021-09-07 15:19:57,900:INFO:Preparing display monitor
2021-09-07 15:19:57,908:INFO:Importing libraries
2021-09-07 15:19:57,908:INFO:Copying data for preprocessing
2021-09-07 15:19:57,916:INFO:Declaring preprocessing parameters
2021-09-07 15:19:57,920:INFO:Creating preprocessing pipeline
2021-09-07 15:19:57,938:INFO:Preprocessing pipeline created successfully
2021-09-07 15:19:57,938:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2021-09-07 15:19:57,938:INFO:Creating global containers
2021-09-07 15:19:57,938:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2021-09-07 15:19:58,184:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 15:19:58,188:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 15:19:58,263:WARNING:Couldn't import xgboost.XGBClassifier
2021-09-07 15:19:58,267:WARNING:Couldn't import catboost.CatBoostClassifier
2021-09-07 15:19:58,267:INFO:Creating grid variables
2021-09-07 15:19:58,276:INFO:Logging experiment in MLFlow
2021-09-07 15:19:58,305:WARNING:Couldn't create mlflow experiment. Exception:
2021-09-07 15:19:58,309:WARNING:Traceback (most recent call last):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 1702, in setup
    mlflow.create_experiment(exp_name_log)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\fluent.py", line 875, in create_experiment
    return MlflowClient().create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\client.py", line 500, in create_experiment
    return self._tracking_client.create_experiment(name, artifact_location)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\tracking\_tracking_service\client.py", line 167, in create_experiment
    return self.store.create_experiment(name=name, artifact_location=artifact_location,)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 313, in create_experiment
    self._validate_experiment_name(name)
  File "C:\Users\prana\AppData\Roaming\Python\Python37\site-packages\mlflow\store\tracking\file_store.py", line 308, in _validate_experiment_name
    databricks_pb2.RESOURCE_ALREADY_EXISTS,
mlflow.exceptions.MlflowException: Experiment 'electrical_grid_1' already exists.

2021-09-07 15:19:58,614:INFO:SubProcess save_model() called ==================================
2021-09-07 15:19:58,622:INFO:Initializing save_model()
2021-09-07 15:19:58,622:INFO:save_model(model=Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('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=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False), verbose=False, kwargs={})
2021-09-07 15:19:58,622:INFO:Adding model into prep_pipe
2021-09-07 15:19:58,622:WARNING:Only Model saved as it was a pipeline.
2021-09-07 15:19:58,626:INFO:Transformation Pipeline.pkl saved in current working directory
2021-09-07 15:19:58,634:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:19:58,634:INFO:save_model() succesfully completed......................................
2021-09-07 15:19:58,692:INFO:SubProcess save_model() end ==================================
2021-09-07 15:19:58,722:INFO:create_model_container: 0
2021-09-07 15:19:58,722:INFO:master_model_container: 0
2021-09-07 15:19:58,722:INFO:display_container: 1
2021-09-07 15:19:58,726:INFO:Pipeline(memory=None,
         steps=[('dtypes',
                 DataTypes_Auto_infer(categorical_features=[],
                                      display_types=False, features_todrop=[],
                                      id_columns=[],
                                      ml_usecase='classification',
                                      numerical_features=[], target='stabf',
                                      time_features=[])),
                ('imputer',
                 Simple_Imputer(categorical_strategy='not_available',
                                fill_value_categorical=None,
                                fill_value_numerical=None,
                                numeric_strat...
                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),
                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),
                ('cluster_all', 'passthrough'),
                ('dummy', Dummify(target='stabf')),
                ('fix_perfect', Remove_100(target='stabf')),
                ('clean_names', Clean_Colum_Names()),
                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),
                ('dfs', 'passthrough'), ('pca', 'passthrough')],
         verbose=False)
2021-09-07 15:19:58,726:INFO:setup() succesfully completed......................................
2021-09-07 15:19:58,787:INFO:Initializing create_model()
2021-09-07 15:19:58,787:INFO:create_model(estimator=rf, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 15:19:58,787:INFO:Checking exceptions
2021-09-07 15:19:58,787:INFO:Preparing display monitor
2021-09-07 15:19:58,811:INFO:Importing libraries
2021-09-07 15:19:58,811:INFO:Copying training dataset
2021-09-07 15:19:58,815:INFO:Defining folds
2021-09-07 15:19:58,815:INFO:Declaring metric variables
2021-09-07 15:19:58,823:INFO:Importing untrained model
2021-09-07 15:19:58,835:INFO:Random Forest Classifier Imported succesfully
2021-09-07 15:19:58,852:INFO:Starting cross validation
2021-09-07 15:19:58,852:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:20:03,541:INFO:Calculating mean and std
2021-09-07 15:20:03,545:INFO:Creating metrics dataframe
2021-09-07 15:20:03,565:INFO:Finalizing model
2021-09-07 15:20:04,440:INFO:Creating MLFlow logs
2021-09-07 15:20:04,472:INFO:Model: Random Forest Classifier
2021-09-07 15:20:04,506: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}
2021-09-07 15:20:04,566:INFO:Initializing predict_model()
2021-09-07 15:20:04,566:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 15:20:04,566:INFO:Checking exceptions
2021-09-07 15:20:04,566:INFO:Preloading libraries
2021-09-07 15:20:05,037:INFO:Uploading results into container
2021-09-07 15:20:05,037:INFO:Uploading model into container now
2021-09-07 15:20:05,050:INFO:create_model_container: 1
2021-09-07 15:20:05,050:INFO:master_model_container: 1
2021-09-07 15:20:05,050:INFO:display_container: 2
2021-09-07 15:20:05,054: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)
2021-09-07 15:20:05,055:INFO:create_model() succesfully completed......................................
2021-09-07 15:20:05,613:INFO:Initializing create_model()
2021-09-07 15:20:05,613:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, verbose=True, system=True, metrics=None, display=None, kwargs={})
2021-09-07 15:20:05,613:INFO:Checking exceptions
2021-09-07 15:20:05,613:INFO:Preparing display monitor
2021-09-07 15:20:05,636:INFO:Importing libraries
2021-09-07 15:20:05,636:INFO:Copying training dataset
2021-09-07 15:20:05,639:INFO:Defining folds
2021-09-07 15:20:05,640:INFO:Declaring metric variables
2021-09-07 15:20:05,653:INFO:Importing untrained model
2021-09-07 15:20:05,653:INFO:Declaring custom model
2021-09-07 15:20:05,664:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 15:20:05,689:INFO:Starting cross validation
2021-09-07 15:20:05,689:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:20:19,876:INFO:Calculating mean and std
2021-09-07 15:20:19,876:INFO:Creating metrics dataframe
2021-09-07 15:20:19,887:INFO:Finalizing model
2021-09-07 15:20:32,796:INFO:Creating MLFlow logs
2021-09-07 15:20:32,808:INFO:Model: NeuralNetClassifier
2021-09-07 15:20:32,838:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class '__main__.customNLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 30, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x0000026F11C45988>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x0000026F089AA508>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x0000026F040F6EC8>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x0000026F0904DB08>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x0000026F08F97558>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x0000026F0904D748>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-09-07 15:20:32,915:INFO:Initializing predict_model()
2021-09-07 15:20:32,915:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 15:20:32,915:INFO:Checking exceptions
2021-09-07 15:20:32,915:INFO:Preloading libraries
2021-09-07 15:20:32,923:WARNING:Couldn't create holdout prediction for model, exception below:
2021-09-07 15:20:32,923:WARNING:Traceback (most recent call last):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 9985, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 8405, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\torch\nn\modules\module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-09-07 15:20:33,115:INFO:Uploading results into container
2021-09-07 15:20:33,115:INFO:Uploading model into container now
2021-09-07 15:20:33,126:INFO:create_model_container: 2
2021-09-07 15:20:33,126:INFO:master_model_container: 2
2021-09-07 15:20:33,127:INFO:display_container: 3
2021-09-07 15:20:33,127:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-09-07 15:20:33,127:INFO:create_model() succesfully completed......................................
2021-09-07 15:20:33,206:INFO:Initializing compare_models()
2021-09-07 15:20:33,206:INFO:compare_models(include=[Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 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)], fold=None, round=4, cross_validation=True, sort=AUC, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-09-07 15:20:33,206:INFO:Checking exceptions
2021-09-07 15:20:33,206:INFO:Preparing display monitor
2021-09-07 15:20:33,206:INFO:Preparing display monitor
2021-09-07 15:20:33,242:INFO:Initializing custom model NeuralNetClassifier
2021-09-07 15:20:33,242:INFO:Total runtime is 6.66499137878418e-05 minutes
2021-09-07 15:20:33,250:INFO:SubProcess create_model() called ==================================
2021-09-07 15:20:33,250:INFO:Initializing create_model()
2021-09-07 15:20:33,254:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 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, display=<pycaret.internal.Display.Display object at 0x0000026F11C88288>, kwargs={})
2021-09-07 15:20:33,254:INFO:Checking exceptions
2021-09-07 15:20:33,254:INFO:Importing libraries
2021-09-07 15:20:33,254:INFO:Copying training dataset
2021-09-07 15:20:33,254:INFO:Defining folds
2021-09-07 15:20:33,254:INFO:Declaring metric variables
2021-09-07 15:20:33,262:INFO:Importing untrained model
2021-09-07 15:20:33,262:INFO:Declaring custom model
2021-09-07 15:20:33,278:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 15:20:33,294:INFO:Starting cross validation
2021-09-07 15:20:33,294:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:20:47,149:INFO:Calculating mean and std
2021-09-07 15:20:47,149:INFO:Creating metrics dataframe
2021-09-07 15:20:47,155:INFO:Uploading results into container
2021-09-07 15:20:47,155:INFO:Uploading model into container now
2021-09-07 15:20:47,155:INFO:create_model_container: 3
2021-09-07 15:20:47,155:INFO:master_model_container: 3
2021-09-07 15:20:47,155:INFO:display_container: 4
2021-09-07 15:20:47,155:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False)
2021-09-07 15:20:47,155:INFO:create_model() succesfully completed......................................
2021-09-07 15:20:47,226:INFO:SubProcess create_model() end ==================================
2021-09-07 15:20:47,226:INFO:Creating metrics dataframe
2021-09-07 15:20:47,243:INFO:Initializing custom model Random Forest Classifier
2021-09-07 15:20:47,243:INFO:Total runtime is 0.23341177304585775 minutes
2021-09-07 15:20:47,251:INFO:SubProcess create_model() called ==================================
2021-09-07 15:20:47,251:INFO:Initializing create_model()
2021-09-07 15:20:47,251:INFO:create_model(estimator=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), 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, display=<pycaret.internal.Display.Display object at 0x0000026F11C88288>, kwargs={})
2021-09-07 15:20:47,251:INFO:Checking exceptions
2021-09-07 15:20:47,251:INFO:Importing libraries
2021-09-07 15:20:47,251:INFO:Copying training dataset
2021-09-07 15:20:47,255:INFO:Defining folds
2021-09-07 15:20:47,255:INFO:Declaring metric variables
2021-09-07 15:20:47,261:INFO:Importing untrained model
2021-09-07 15:20:47,261:INFO:Declaring custom model
2021-09-07 15:20:47,271:INFO:Random Forest Classifier Imported succesfully
2021-09-07 15:20:47,287:INFO:Starting cross validation
2021-09-07 15:20:47,287:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:20:48,984:INFO:Calculating mean and std
2021-09-07 15:20:48,984:INFO:Creating metrics dataframe
2021-09-07 15:20:48,993:INFO:Uploading results into container
2021-09-07 15:20:49,001:INFO:Uploading model into container now
2021-09-07 15:20:49,001:INFO:create_model_container: 4
2021-09-07 15:20:49,001:INFO:master_model_container: 4
2021-09-07 15:20:49,001:INFO:display_container: 4
2021-09-07 15:20:49,001: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)
2021-09-07 15:20:49,001:INFO:create_model() succesfully completed......................................
2021-09-07 15:20:49,072:INFO:SubProcess create_model() end ==================================
2021-09-07 15:20:49,072:INFO:Creating metrics dataframe
2021-09-07 15:20:49,112:INFO:Initializing create_model()
2021-09-07 15:20:49,112:INFO:create_model(estimator=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), 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, display=None, kwargs={})
2021-09-07 15:20:49,112:INFO:Checking exceptions
2021-09-07 15:20:49,112:INFO:Importing libraries
2021-09-07 15:20:49,112:INFO:Copying training dataset
2021-09-07 15:20:49,112:INFO:Defining folds
2021-09-07 15:20:49,112:INFO:Declaring metric variables
2021-09-07 15:20:49,112:INFO:Importing untrained model
2021-09-07 15:20:49,112:INFO:Declaring custom model
2021-09-07 15:20:49,112:INFO:Random Forest Classifier Imported succesfully
2021-09-07 15:20:49,112:INFO:Cross validation set to False
2021-09-07 15:20:49,112:INFO:Fitting Model
2021-09-07 15:20:49,973: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)
2021-09-07 15:20:49,973:INFO:create_models() succesfully completed......................................
2021-09-07 15:20:50,054:INFO:Creating MLFlow logs
2021-09-07 15:20:50,063:INFO:Model: Random Forest Classifier
2021-09-07 15:20:50,095: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}
2021-09-07 15:20:50,155:INFO:Initializing predict_model()
2021-09-07 15:20:50,155:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 15:20:50,155:INFO:Checking exceptions
2021-09-07 15:20:50,155:INFO:Preloading libraries
2021-09-07 15:20:50,600:INFO:Creating MLFlow logs
2021-09-07 15:20:50,612:INFO:Model: NeuralNetClassifier
2021-09-07 15:20:50,644:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class '__main__.customNLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 30, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x0000026F0902F048>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x0000026F11CC5048>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x0000026F11C869C8>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x0000026F03C41F88>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x0000026F08F97558>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x0000026F11CA9588>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-09-07 15:20:50,839:INFO:create_model_container: 4
2021-09-07 15:20:50,839:INFO:master_model_container: 4
2021-09-07 15:20:50,839:INFO:display_container: 4
2021-09-07 15:20:50,839: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)
2021-09-07 15:20:50,839:INFO:compare_models() succesfully completed......................................
2021-09-07 15:20:50,906:INFO:Initializing tune_model()
2021-09-07 15:20:50,906:INFO:tune_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=None, round=4, n_iter=10, custom_grid={'net__max_epochs': [20, 30], 'net__lr': [0.01, 0.05, 0.1], 'net__module__num_units_d1': [50, 100, 150, 200], 'net__module__num_units_d2': [50, 100, 150, 200], 'net__optimizer': [<class 'torch.optim.adam.Adam'>, <class 'torch.optim.sgd.SGD'>, <class 'torch.optim.rmsprop.RMSprop'>]}, optimize=Accuracy, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=False, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, display=None, kwargs={})
2021-09-07 15:20:50,906:INFO:Checking exceptions
2021-09-07 15:20:50,906:INFO:Preparing display monitor
2021-09-07 15:20:50,935:INFO:Copying training dataset
2021-09-07 15:20:50,939:INFO:Checking base model
2021-09-07 15:20:50,939:INFO:A custom model has been passed
2021-09-07 15:20:50,939:INFO:Base model : NeuralNetClassifier
2021-09-07 15:20:50,951:INFO:Declaring metric variables
2021-09-07 15:20:50,963:INFO:Defining Hyperparameters
2021-09-07 15:20:51,039:INFO:custom_grid: {'actual_estimator__net__max_epochs': [20, 30], 'actual_estimator__net__lr': [0.01, 0.05, 0.1], 'actual_estimator__net__module__num_units_d1': [50, 100, 150, 200], 'actual_estimator__net__module__num_units_d2': [50, 100, 150, 200], 'actual_estimator__net__optimizer': [<class 'torch.optim.adam.Adam'>, <class 'torch.optim.sgd.SGD'>, <class 'torch.optim.rmsprop.RMSprop'>]}
2021-09-07 15:20:51,039:INFO:Tuning with n_jobs=-1
2021-09-07 15:20:51,039:INFO:Initializing RandomizedSearchCV
2021-09-07 15:22:15,033:INFO:best_params: {'actual_estimator__net__optimizer': <class 'torch.optim.sgd.SGD'>, 'actual_estimator__net__module__num_units_d2': 200, 'actual_estimator__net__module__num_units_d1': 50, 'actual_estimator__net__max_epochs': 20, 'actual_estimator__net__lr': 0.05}
2021-09-07 15:22:15,033:INFO:Hyperparameter search completed
2021-09-07 15:22:15,033:INFO:SubProcess create_model() called ==================================
2021-09-07 15:22:15,033:INFO:Initializing create_model()
2021-09-07 15:22:15,033:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False), fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, verbose=True, system=False, metrics=None, display=<pycaret.internal.Display.Display object at 0x0000026F11B6FA88>, kwargs={'net__optimizer': <class 'torch.optim.sgd.SGD'>, 'net__module__num_units_d2': 200, 'net__module__num_units_d1': 50, 'net__max_epochs': 20, 'net__lr': 0.05})
2021-09-07 15:22:15,033:INFO:Checking exceptions
2021-09-07 15:22:15,033:INFO:Importing libraries
2021-09-07 15:22:15,033:INFO:Copying training dataset
2021-09-07 15:22:15,033:INFO:Defining folds
2021-09-07 15:22:15,033:INFO:Declaring metric variables
2021-09-07 15:22:15,041:INFO:Importing untrained model
2021-09-07 15:22:15,041:INFO:Declaring custom model
2021-09-07 15:22:15,057:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 15:22:15,073:INFO:Starting cross validation
2021-09-07 15:22:15,073:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:22:22,150:INFO:Calculating mean and std
2021-09-07 15:22:22,150:INFO:Creating metrics dataframe
2021-09-07 15:22:22,158:INFO:Finalizing model
2021-09-07 15:22:30,202:INFO:Uploading results into container
2021-09-07 15:22:30,202:INFO:Uploading model into container now
2021-09-07 15:22:30,202:INFO:create_model_container: 5
2021-09-07 15:22:30,202:INFO:master_model_container: 5
2021-09-07 15:22:30,202:INFO:display_container: 5
2021-09-07 15:22:30,202:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=50, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=50, out_features=200, bias=True)
    (output): Linear(in_features=200, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-09-07 15:22:30,202:INFO:create_model() succesfully completed......................................
2021-09-07 15:22:30,264:INFO:SubProcess create_model() end ==================================
2021-09-07 15:22:30,264:INFO:Creating MLFlow logs
2021-09-07 15:22:30,274:INFO:Model: NeuralNetClassifier
2021-09-07 15:22:30,309:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class '__main__.customNLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.05, 'max_epochs': 20, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'module__num_units_d2': 200, 'module__num_units_d1': 50, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x0000026F03925188>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x0000026F0902CB08>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x0000026F0019C508>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x0000026EEDAFCD88>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x0000026F08F97558>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x0000026EEDAEC248>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-09-07 15:22:30,385:INFO:Initializing predict_model()
2021-09-07 15:22:30,385:INFO:predict_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=50, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=50, out_features=200, bias=True)
    (output): Linear(in_features=200, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 15:22:30,385:INFO:Checking exceptions
2021-09-07 15:22:30,385:INFO:Preloading libraries
2021-09-07 15:22:30,395:WARNING:Couldn't create holdout prediction for model, exception below:
2021-09-07 15:22:30,395:WARNING:Traceback (most recent call last):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 9985, in _mlflow_log_model
    holdout = predict_model(model, verbose=False)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\pycaret\internal\tabular.py", line 8405, in predict_model
    pred = np.nan_to_num(estimator.predict(X_test_))
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\classifier.py", line 209, in predict
    return super().predict_proba(X).argmax(axis=1)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 1136, in predict_proba
    for yp in self.forward_iter(X, training=False):
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 982, in forward_iter
    yp = self.evaluation_step(Xi, training=training)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 738, in evaluation_step
    return self.infer(Xi)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\skorch\net.py", line 1061, in infer
    return self.module_(**x_dict)
  File "C:\Users\prana\anaconda3\envs\ml\lib\site-packages\torch\nn\modules\module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 1 required positional argument: 'X'

2021-09-07 15:22:30,564:INFO:create_model_container: 5
2021-09-07 15:22:30,564:INFO:master_model_container: 5
2021-09-07 15:22:30,564:INFO:display_container: 5
2021-09-07 15:22:30,564:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=50, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=50, out_features=200, bias=True)
    (output): Linear(in_features=200, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False)
2021-09-07 15:22:30,564:INFO:tune_model() succesfully completed......................................
2021-09-07 15:22:30,640:INFO:Initializing compare_models()
2021-09-07 15:22:30,640:INFO:compare_models(include=[Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=50, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=50, out_features=200, bias=True)
    (output): Linear(in_features=200, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 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)], fold=None, round=4, cross_validation=True, sort=AUC, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, verbose=True, display=None, exclude=None)
2021-09-07 15:22:30,640:INFO:Checking exceptions
2021-09-07 15:22:30,640:INFO:Preparing display monitor
2021-09-07 15:22:30,640:INFO:Preparing display monitor
2021-09-07 15:22:30,680:INFO:Initializing custom model NeuralNetClassifier
2021-09-07 15:22:30,680:INFO:Total runtime is 0.0 minutes
2021-09-07 15:22:30,696:INFO:SubProcess create_model() called ==================================
2021-09-07 15:22:30,696:INFO:Initializing create_model()
2021-09-07 15:22:30,696:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=50, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=50, out_features=200, bias=True)
    (output): Linear(in_features=200, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 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, display=<pycaret.internal.Display.Display object at 0x0000026F0511DC88>, kwargs={})
2021-09-07 15:22:30,696:INFO:Checking exceptions
2021-09-07 15:22:30,696:INFO:Importing libraries
2021-09-07 15:22:30,696:INFO:Copying training dataset
2021-09-07 15:22:30,696:INFO:Defining folds
2021-09-07 15:22:30,696:INFO:Declaring metric variables
2021-09-07 15:22:30,712:INFO:Importing untrained model
2021-09-07 15:22:30,712:INFO:Declaring custom model
2021-09-07 15:22:30,720:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 15:22:30,736:INFO:Starting cross validation
2021-09-07 15:22:30,744:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:22:38,091:INFO:Calculating mean and std
2021-09-07 15:22:38,091:INFO:Creating metrics dataframe
2021-09-07 15:22:38,099:INFO:Uploading results into container
2021-09-07 15:22:38,099:INFO:Uploading model into container now
2021-09-07 15:22:38,099:INFO:create_model_container: 6
2021-09-07 15:22:38,099:INFO:master_model_container: 6
2021-09-07 15:22:38,099:INFO:display_container: 6
2021-09-07 15:22:38,099:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
  module__num_units_d1=50,
  module__num_units_d2=200,
))],
         verbose=False)
2021-09-07 15:22:38,099:INFO:create_model() succesfully completed......................................
2021-09-07 15:22:38,155:INFO:SubProcess create_model() end ==================================
2021-09-07 15:22:38,163:INFO:Creating metrics dataframe
2021-09-07 15:22:38,179:INFO:Initializing custom model NeuralNetClassifier
2021-09-07 15:22:38,179:INFO:Total runtime is 0.12497512499491374 minutes
2021-09-07 15:22:38,187:INFO:SubProcess create_model() called ==================================
2021-09-07 15:22:38,187:INFO:Initializing create_model()
2021-09-07 15:22:38,187:INFO:create_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=200, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=200, out_features=100, bias=True)
    (output): Linear(in_features=100, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), 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, display=<pycaret.internal.Display.Display object at 0x0000026F0511DC88>, kwargs={})
2021-09-07 15:22:38,187:INFO:Checking exceptions
2021-09-07 15:22:38,187:INFO:Importing libraries
2021-09-07 15:22:38,195:INFO:Copying training dataset
2021-09-07 15:22:38,195:INFO:Defining folds
2021-09-07 15:22:38,195:INFO:Declaring metric variables
2021-09-07 15:22:38,203:INFO:Importing untrained model
2021-09-07 15:22:38,203:INFO:Declaring custom model
2021-09-07 15:22:38,211:INFO:NeuralNetClassifier Imported succesfully
2021-09-07 15:22:38,235:INFO:Starting cross validation
2021-09-07 15:22:38,235:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:22:49,924:INFO:Calculating mean and std
2021-09-07 15:22:49,924:INFO:Creating metrics dataframe
2021-09-07 15:22:49,924:INFO:Uploading results into container
2021-09-07 15:22:49,924:INFO:Uploading model into container now
2021-09-07 15:22:49,924:INFO:create_model_container: 7
2021-09-07 15:22:49,924:INFO:master_model_container: 7
2021-09-07 15:22:49,924:INFO:display_container: 6
2021-09-07 15:22:49,924:INFO:Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](
  module=<class '__main__.Net'>,
))],
         verbose=False)
2021-09-07 15:22:49,924:INFO:create_model() succesfully completed......................................
2021-09-07 15:22:49,989:INFO:SubProcess create_model() end ==================================
2021-09-07 15:22:49,989:INFO:Creating metrics dataframe
2021-09-07 15:22:50,007:INFO:Initializing custom model Random Forest Classifier
2021-09-07 15:22:50,007:INFO:Total runtime is 0.3221127192179362 minutes
2021-09-07 15:22:50,015:INFO:SubProcess create_model() called ==================================
2021-09-07 15:22:50,015:INFO:Initializing create_model()
2021-09-07 15:22:50,015:INFO:create_model(estimator=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), 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, display=<pycaret.internal.Display.Display object at 0x0000026F0511DC88>, kwargs={})
2021-09-07 15:22:50,015:INFO:Checking exceptions
2021-09-07 15:22:50,015:INFO:Importing libraries
2021-09-07 15:22:50,015:INFO:Copying training dataset
2021-09-07 15:22:50,015:INFO:Defining folds
2021-09-07 15:22:50,015:INFO:Declaring metric variables
2021-09-07 15:22:50,031:INFO:Importing untrained model
2021-09-07 15:22:50,031:INFO:Declaring custom model
2021-09-07 15:22:50,039:INFO:Random Forest Classifier Imported succesfully
2021-09-07 15:22:50,055:INFO:Starting cross validation
2021-09-07 15:22:50,063:INFO:Cross validating with StratifiedKFold(n_splits=5, random_state=None, shuffle=False), n_jobs=-1
2021-09-07 15:22:51,958:INFO:Calculating mean and std
2021-09-07 15:22:51,958:INFO:Creating metrics dataframe
2021-09-07 15:22:51,974:INFO:Uploading results into container
2021-09-07 15:22:51,974:INFO:Uploading model into container now
2021-09-07 15:22:51,974:INFO:create_model_container: 8
2021-09-07 15:22:51,974:INFO:master_model_container: 8
2021-09-07 15:22:51,974:INFO:display_container: 6
2021-09-07 15:22:51,974: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)
2021-09-07 15:22:51,974:INFO:create_model() succesfully completed......................................
2021-09-07 15:22:52,038:INFO:SubProcess create_model() end ==================================
2021-09-07 15:22:52,038:INFO:Creating metrics dataframe
2021-09-07 15:22:52,078:INFO:Initializing create_model()
2021-09-07 15:22:52,078:INFO:create_model(estimator=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), 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, display=None, kwargs={})
2021-09-07 15:22:52,086:INFO:Checking exceptions
2021-09-07 15:22:52,086:INFO:Importing libraries
2021-09-07 15:22:52,086:INFO:Copying training dataset
2021-09-07 15:22:52,086:INFO:Defining folds
2021-09-07 15:22:52,086:INFO:Declaring metric variables
2021-09-07 15:22:52,086:INFO:Importing untrained model
2021-09-07 15:22:52,086:INFO:Declaring custom model
2021-09-07 15:22:52,086:INFO:Random Forest Classifier Imported succesfully
2021-09-07 15:22:52,086:INFO:Cross validation set to False
2021-09-07 15:22:52,086:INFO:Fitting Model
2021-09-07 15:22:52,938: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)
2021-09-07 15:22:52,938:INFO:create_models() succesfully completed......................................
2021-09-07 15:22:53,001:INFO:Creating MLFlow logs
2021-09-07 15:22:53,017:INFO:Model: Random Forest Classifier
2021-09-07 15:22:53,049: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}
2021-09-07 15:22:53,105:INFO:Initializing predict_model()
2021-09-07 15:22:53,105:INFO:predict_model(estimator=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), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=False, ml_usecase=None, display=None)
2021-09-07 15:22:53,105:INFO:Checking exceptions
2021-09-07 15:22:53,105:INFO:Preloading libraries
2021-09-07 15:22:53,581:INFO:Creating MLFlow logs
2021-09-07 15:22:53,589:INFO:Model: NeuralNetClassifier
2021-09-07 15:22:53,621:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class '__main__.customNLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.1, 'max_epochs': 30, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x0000026F11D09F88>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x0000026ED02B05C8>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x0000026F0901BB88>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x0000026F11EAA148>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x0000026F08F97558>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x0000026EFFDB3988>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-09-07 15:22:53,789:INFO:Creating MLFlow logs
2021-09-07 15:22:53,805:INFO:Model: NeuralNetClassifier
2021-09-07 15:22:53,837:INFO:logged params: {'module': <class '__main__.Net'>, 'criterion': <class '__main__.customNLLLoss'>, 'optimizer': <class 'torch.optim.sgd.SGD'>, 'lr': 0.05, 'max_epochs': 20, 'batch_size': 32, 'iterator_train': <class 'torch.utils.data.dataloader.DataLoader'>, 'iterator_valid': <class 'torch.utils.data.dataloader.DataLoader'>, 'dataset': <class 'skorch.dataset.Dataset'>, 'train_split': None, 'callbacks': None, 'predict_nonlinearity': 'auto', 'warm_start': False, 'verbose': 1, 'device': 'cpu', 'module__num_units_d2': 200, 'module__num_units_d1': 50, 'classes': None, 'callbacks__epoch_timer': <skorch.callbacks.logging.EpochTimer object at 0x0000026F0902C208>, 'callbacks__train_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x0000026F09051148>, 'callbacks__train_loss__name': 'train_loss', 'callbacks__train_loss__lower_is_better': True, 'callbacks__train_loss__on_train': True, 'callbacks__valid_loss': <skorch.callbacks.scoring.PassthroughScoring object at 0x0000026F120F9748>, 'callbacks__valid_loss__name': 'valid_loss', 'callbacks__valid_loss__lower_is_better': True, 'callbacks__valid_loss__on_train': False, 'callbacks__valid_acc': <skorch.callbacks.scoring.EpochScoring object at 0x0000026F120F9148>, 'callbacks__valid_acc__scoring': 'accuracy', 'callbacks__valid_acc__lower_is_better': False, 'callbacks__valid_acc__on_train': False, 'callbacks__valid_acc__name': 'valid_acc', 'callbacks__valid_acc__target_extractor': <function to_numpy at 0x0000026F08F97558>, 'callbacks__valid_acc__use_caching': True, 'callbacks__print_log': <skorch.callbacks.logging.PrintLog object at 0x0000026F09044A08>, 'callbacks__print_log__keys_ignored': None, 'callbacks__print_log__sink': <built-in function print>, 'callbacks__print_log__tablefmt': 'simple', 'callbacks__print_log__floatfmt': '.4f', 'callbacks__print_log__stralign': 'right'}
2021-09-07 15:22:54,029:INFO:create_model_container: 8
2021-09-07 15:22:54,029:INFO:master_model_container: 8
2021-09-07 15:22:54,029:INFO:display_container: 6
2021-09-07 15:22:54,029: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)
2021-09-07 15:22:54,029:INFO:compare_models() succesfully completed......................................
2021-09-07 15:22:54,045:INFO:Initializing evaluate_model()
2021-09-07 15:22:54,045:INFO:evaluate_model(estimator=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), fold=None, fit_kwargs=None, feature_name=None, groups=None, use_train_data=False)
2021-09-07 15:22:54,069:INFO:Initializing plot_model()
2021-09-07 15:22:54,069:INFO:plot_model(plot=parameter, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), use_train_data=False, verbose=True, is_in_evaluate=True, display=None, display_format=None, estimator=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), feature_name=None, fit_kwargs={}, groups=None, label=False, save=False, scale=1, system=True)
2021-09-07 15:22:54,069:INFO:Checking exceptions
2021-09-07 15:22:54,181:INFO:Preparing display monitor
2021-09-07 15:22:54,189:INFO:Preloading libraries
2021-09-07 15:22:54,213:INFO:Copying training dataset
2021-09-07 15:22:54,213:INFO:Copying test dataset
2021-09-07 15:22:54,213:INFO:Plot type: parameter
2021-09-07 15:22:54,582:INFO:Visual Rendered Successfully
2021-09-07 15:22:54,659:INFO:plot_model() succesfully completed......................................
2021-09-07 15:22:54,675:INFO:Initializing evaluate_model()
2021-09-07 15:22:54,675:INFO:evaluate_model(estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=50, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=50, out_features=200, bias=True)
    (output): Linear(in_features=200, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), fold=None, fit_kwargs=None, feature_name=None, groups=None, use_train_data=False)
2021-09-07 15:22:54,699:INFO:Initializing plot_model()
2021-09-07 15:22:54,699:INFO:plot_model(plot=parameter, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), use_train_data=False, verbose=True, is_in_evaluate=True, display=None, display_format=None, estimator=Pipeline(memory=None,
         steps=[('transform',
                 DataFrameTransformer(float_dtype=<class 'numpy.float32'>,
                                      int_dtype=<class 'numpy.int64'>,
                                      treat_int_as_categorical=False)),
                ('net',
                 <class 'skorch.classifier.NeuralNetClassifier'>[initialized](
  module_=Net(
    (dense0): Linear(in_features=12, out_features=50, bias=True)
    (nonlin): ReLU()
    (dropout): Dropout(p=0.5, inplace=False)
    (dense1): Linear(in_features=50, out_features=200, bias=True)
    (output): Linear(in_features=200, out_features=2, bias=True)
    (softmax): Softmax(dim=-1)
  ),
))],
         verbose=False), feature_name=None, fit_kwargs={}, groups=None, label=False, save=False, scale=1, system=True)
2021-09-07 15:22:54,699:INFO:Checking exceptions
2021-09-07 15:22:54,707:INFO:Preparing display monitor
2021-09-07 15:22:54,715:INFO:Preloading libraries
2021-09-07 15:22:54,747:INFO:Copying training dataset
2021-09-07 15:22:54,747:INFO:Copying test dataset
2021-09-07 15:22:54,747:INFO:Plot type: parameter
2021-09-07 15:22:54,755:INFO:Visual Rendered Successfully
2021-09-07 15:22:54,819:INFO:plot_model() succesfully completed......................................
2021-09-07 15:23:04,645:INFO:Initializing plot_model()
2021-09-07 15:23:04,645:INFO:plot_model(plot=auc, fold=StratifiedKFold(n_splits=5, random_state=None, shuffle=False), use_train_data=False, verbose=True, is_in_evaluate=True, display=None, display_format=None, estimator=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), feature_name=None, fit_kwargs={}, groups=None, label=False, save=False, scale=1, system=True)
2021-09-07 15:23:04,645:INFO:Checking exceptions
2021-09-07 15:23:04,757:INFO:Preparing display monitor
2021-09-07 15:23:04,773:INFO:Preloading libraries
2021-09-07 15:23:04,813:INFO:Copying training dataset
2021-09-07 15:23:04,813:INFO:Copying test dataset
2021-09-07 15:23:04,813:INFO:Plot type: auc
2021-09-07 15:23:05,053:INFO:Fitting Model
2021-09-07 15:23:05,061:INFO:Scoring test/hold-out set
2021-09-07 15:23:05,589:INFO:Visual Rendered Successfully
2021-09-07 15:23:05,650:INFO:plot_model() succesfully completed......................................