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2022-04-29 13:50:19,500:INFO:PyCaret Supervised Module
2022-04-29 13:50:19,500:INFO:ML Usecase: regression
2022-04-29 13:50:19,500:INFO:version 2.3.10
2022-04-29 13:50:19,500:INFO:Initializing setup()
2022-04-29 13:50:19,501:INFO:setup(target=LambdaMax, ml_usecase=regression, available_plots={'parameter': 'Hyperparameters', 'residuals': 'Residuals', 'error': 'Prediction Error', 'cooks': 'Cooks Distance', 'rfe': 'Feature Selection', 'learning': 'Learning Curve', 'manifold': 'Manifold Learning', 'vc': 'Validation Curve', 'feature': 'Feature Importance', 'feature_all': 'Feature Importance (All)', 'tree': 'Decision Tree', 'residuals_interactive': 'Interactive Residuals'}, 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=True, 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=True, 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=kfold, fold=10, fold_shuffle=False, fold_groups=None, n_jobs=-1, use_gpu=False, custom_pipeline=None, html=True, session_id=123, log_experiment=False, experiment_name=None, experiment_custom_tags=None, log_plots=False, log_profile=False, log_data=False, silent=True, verbose=True, profile=False, profile_kwargs=None, display=None)
2022-04-29 13:50:19,501:INFO:Checking environment
2022-04-29 13:50:19,501:INFO:python_version: 3.8.13
2022-04-29 13:50:19,501:INFO:python_build: ('default', 'Mar 28 2022 06:59:08')
2022-04-29 13:50:19,501:INFO:machine: AMD64
2022-04-29 13:50:19,514:INFO:platform: Windows-10-10.0.19044-SP0
2022-04-29 13:50:19,514:INFO:Memory: svmem(total=12762226688, available=3382067200, percent=73.5, used=9380159488, free=3382067200)
2022-04-29 13:50:19,515:INFO:Physical Core: 4
2022-04-29 13:50:19,515:INFO:Logical Core: 8
2022-04-29 13:50:19,515:INFO:Checking libraries
2022-04-29 13:50:19,515:INFO:pd==1.4.2
2022-04-29 13:50:19,515:INFO:numpy==1.19.5
2022-04-29 13:50:19,515:INFO:sklearn==0.23.2
2022-04-29 13:50:19,515:INFO:lightgbm==3.3.2
2022-04-29 13:50:20,129:INFO:catboost==1.0.5
2022-04-29 13:50:20,129:INFO:xgboost==1.6.0
2022-04-29 13:50:20,129:INFO:mlflow==1.25.1
2022-04-29 13:50:20,129:INFO:Checking Exceptions
2022-04-29 13:50:20,130:INFO:Declaring global variables
2022-04-29 13:50:20,130:INFO:USI: d721
2022-04-29 13:50:20,130:INFO:pycaret_globals: {'y_test', 'imputation_classifier', '_ml_usecase', 'create_model_container', 'log_plots_param', 'data_before_preprocess', 'fix_imbalance_param', 'fold_groups_param_full', 'fold_groups_param', 'display_container', 'experiment__', 'pycaret_globals', '_all_models_internal', 'stratify_param', '_internal_pipeline', '_gpu_n_jobs_param', 'html_param', '_all_metrics', 'X_test', 'seed', 'fold_shuffle_param', '_all_models', 'y_train', 'transform_target_param', 'USI', '_available_plots', 'X', 'fix_imbalance_method_param', 'fold_generator', 'X_train', 'iterative_imputation_iters_param', 'n_jobs_param', 'imputation_regressor', 'target_param', 'exp_name_log', 'prep_pipe', 'logging_param', 'transform_target_method_param', 'gpu_param', 'master_model_container', 'dashboard_logger', 'y', 'fold_param'}
2022-04-29 13:50:20,130:INFO:Preparing display monitor
2022-04-29 13:50:20,130:INFO:Preparing display monitor
2022-04-29 13:50:20,145:INFO:Importing libraries
2022-04-29 13:50:20,146:INFO:Copying data for preprocessing
2022-04-29 13:50:20,148:INFO:Declaring preprocessing parameters
2022-04-29 13:50:20,149:INFO:Creating preprocessing pipeline
2022-04-29 13:50:20,178:INFO:Preprocessing pipeline created successfully
2022-04-29 13:50:20,178:ERROR:(Process Exit): setup has been interupted with user command 'quit'. setup must rerun.
2022-04-29 13:50:20,178:INFO:Creating global containers
2022-04-29 13:50:20,184:INFO:Internal pipeline: Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False)
2022-04-29 13:50:22,544:INFO:Creating grid variables
2022-04-29 13:50:22,550:INFO:create_model_container: 0
2022-04-29 13:50:22,550:INFO:master_model_container: 0
2022-04-29 13:50:22,550:INFO:display_container: 1
2022-04-29 13:50:22,558:INFO:Pipeline(memory=None,
steps=[('dtypes',
DataTypes_Auto_infer(categorical_features=[],
display_types=False, features_todrop=[],
id_columns=[], ml_usecase='regression',
numerical_features=[], target='LambdaMax',
time_features=[])),
('imputer',
Simple_Imputer(categorical_strategy='not_available',
fill_value_categorical=None,
fill_value_numerical=None,
numeric_strat...
('fix_perfect', Remove_100(target='LambdaMax')),
('clean_names', Clean_Colum_Names()),
('feature_select',
Advanced_Feature_Selection_Classic(ml_usecase='regression',
n_jobs=-1, random_state=123,
subclass='binary',
target='LambdaMax',
top_features_to_pick=0.19999999999999996)),
('fix_multi', 'passthrough'), ('dfs', 'passthrough'),
('pca', 'passthrough')],
verbose=False)
2022-04-29 13:50:22,558:INFO:setup() succesfully completed......................................
2022-04-29 13:50:22,669:INFO:Initializing compare_models()
2022-04-29 13:50:22,669:INFO:compare_models(include=['lr', 'ridge', 'lar', 'br', 'par', 'huber', 'lasso', 'et', 'xgboost', 'lightgbm', 'rf', 'dt', 'ada'], fold=None, round=4, cross_validation=True, sort=MAE, n_select=5, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=False, display=None, exclude=None)
2022-04-29 13:50:22,669:INFO:Checking exceptions
2022-04-29 13:50:22,669:INFO:Preparing display monitor
2022-04-29 13:50:22,669:INFO:Initializing Linear Regression
2022-04-29 13:50:22,669:INFO:Total runtime is 0.0 minutes
2022-04-29 13:50:22,670:INFO:SubProcess create_model() called ==================================
2022-04-29 13:50:22,670:INFO:Initializing create_model()
2022-04-29 13:50:22,670:INFO:create_model(estimator=lr, fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x0000021ABCE77D90>, return_train_score=False, kwargs={})
2022-04-29 13:50:22,670:INFO:Checking exceptions
2022-04-29 13:50:22,670:INFO:Importing libraries
2022-04-29 13:50:22,670:INFO:Copying training dataset
2022-04-29 13:50:22,671:INFO:Defining folds
2022-04-29 13:50:22,671:INFO:Declaring metric variables
2022-04-29 13:50:22,671:INFO:Importing untrained model
2022-04-29 13:50:22,671:INFO:Linear Regression Imported succesfully
2022-04-29 13:50:22,671:INFO:Starting cross validation
2022-04-29 13:50:22,703:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-04-29 13:50:48,106:INFO:Calculating mean and std
2022-04-29 13:50:48,107:INFO:Creating metrics dataframe
2022-04-29 13:50:48,118:INFO:Uploading results into container
2022-04-29 13:50:48,119:INFO:Uploading model into container now
2022-04-29 13:50:48,119:INFO:create_model_container: 1
2022-04-29 13:50:48,119:INFO:master_model_container: 1
2022-04-29 13:50:48,120:INFO:display_container: 2
2022-04-29 13:50:48,121:INFO:LinearRegression(copy_X=True, fit_intercept=True, n_jobs=-1, normalize=False)
2022-04-29 13:50:48,121:INFO:create_model() succesfully completed......................................
2022-04-29 13:50:48,260:INFO:SubProcess create_model() end ==================================
2022-04-29 13:50:48,260:INFO:Creating metrics dataframe
2022-04-29 13:50:48,270:INFO:Initializing Ridge Regression
2022-04-29 13:50:48,271:INFO:Total runtime is 0.42670674324035646 minutes
2022-04-29 13:50:48,271:INFO:SubProcess create_model() called ==================================
2022-04-29 13:50:48,271:INFO:Initializing create_model()
2022-04-29 13:50:48,271:INFO:create_model(estimator=ridge, fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x0000021ABCE77D90>, return_train_score=False, kwargs={})
2022-04-29 13:50:48,271:INFO:Checking exceptions
2022-04-29 13:50:48,271:INFO:Importing libraries
2022-04-29 13:50:48,271:INFO:Copying training dataset
2022-04-29 13:50:48,272:INFO:Defining folds
2022-04-29 13:50:48,272:INFO:Declaring metric variables
2022-04-29 13:50:48,272:INFO:Importing untrained model
2022-04-29 13:50:48,272:INFO:Ridge Regression Imported succesfully
2022-04-29 13:50:48,272:INFO:Starting cross validation
2022-04-29 13:50:48,273:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-04-29 13:50:48,348:INFO:Calculating mean and std
2022-04-29 13:50:48,348:INFO:Creating metrics dataframe
2022-04-29 13:50:48,355:INFO:Uploading results into container
2022-04-29 13:50:48,355:INFO:Uploading model into container now
2022-04-29 13:50:48,355:INFO:create_model_container: 2
2022-04-29 13:50:48,356:INFO:master_model_container: 2
2022-04-29 13:50:48,356:INFO:display_container: 2
2022-04-29 13:50:48,356:INFO:Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,
normalize=False, random_state=123, solver='auto', tol=0.001)
2022-04-29 13:50:48,356:INFO:create_model() succesfully completed......................................
2022-04-29 13:50:48,460:INFO:SubProcess create_model() end ==================================
2022-04-29 13:50:48,460:INFO:Creating metrics dataframe
2022-04-29 13:50:48,471:INFO:Initializing Least Angle Regression
2022-04-29 13:50:48,472:INFO:Total runtime is 0.4300517717997233 minutes
2022-04-29 13:50:48,472:INFO:SubProcess create_model() called ==================================
2022-04-29 13:50:48,472:INFO:Initializing create_model()
2022-04-29 13:50:48,472:INFO:create_model(estimator=lar, fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x0000021ABCE77D90>, return_train_score=False, kwargs={})
2022-04-29 13:50:48,472:INFO:Checking exceptions
2022-04-29 13:50:48,472:INFO:Importing libraries
2022-04-29 13:50:48,473:INFO:Copying training dataset
2022-04-29 13:50:48,473:INFO:Defining folds
2022-04-29 13:50:48,473:INFO:Declaring metric variables
2022-04-29 13:50:48,473:INFO:Importing untrained model
2022-04-29 13:50:48,474:INFO:Least Angle Regression Imported succesfully
2022-04-29 13:50:48,474:INFO:Starting cross validation
2022-04-29 13:50:48,474:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-04-29 13:50:49,387:WARNING:create_model() for lar raised an exception or returned all 0.0, trying without fit_kwargs:
2022-04-29 13:50:49,392:WARNING:joblib.externals.loky.process_executor._RemoteTraceback:
"""
Traceback (most recent call last):
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\externals\loky\process_executor.py", line 431, in _process_worker
r = call_item()
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\externals\loky\process_executor.py", line 285, in __call__
return self.fn(*self.args, **self.kwargs)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\_parallel_backends.py", line 595, in __call__
return self.func(*args, **kwargs)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\model_selection\_validation.py", line 560, in _fit_and_score
test_scores = _score(estimator, X_test, y_test, scorer)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\model_selection\_validation.py", line 607, in _score
scores = scorer(estimator, X_test, y_test)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\metrics\_scorer.py", line 87, in __call__
score = scorer._score(cached_call, estimator,
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\metrics\_scorer.py", line 212, in _score
return self._sign * self._score_func(y_true, y_pred,
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\utils\validation.py", line 72, in inner_f
return f(**kwargs)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\metrics\_regression.py", line 178, in mean_absolute_error
y_type, y_true, y_pred, multioutput = _check_reg_targets(
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\metrics\_regression.py", line 86, in _check_reg_targets
y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\utils\validation.py", line 72, in inner_f
return f(**kwargs)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\utils\validation.py", line 644, in check_array
_assert_all_finite(array,
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\utils\validation.py", line 96, in _assert_all_finite
raise ValueError(
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\pycaret-2.3.10-py3.8.egg\pycaret\internal\tabular.py", line 2203, in compare_models
model, model_fit_time = create_model_supervised(**create_model_args)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\pycaret-2.3.10-py3.8.egg\pycaret\internal\tabular.py", line 3200, in create_model_supervised
scores = cross_validate(
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\utils\validation.py", line 72, in inner_f
return f(**kwargs)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\model_selection\_validation.py", line 242, in cross_validate
scores = parallel(
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\parallel.py", line 1054, in __call__
self.retrieve()
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\parallel.py", line 933, in retrieve
self._output.extend(job.get(timeout=self.timeout))
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\_parallel_backends.py", line 542, in wrap_future_result
return future.result(timeout=timeout)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\concurrent\futures\_base.py", line 444, in result
return self.__get_result()
File "C:\Users\mahdi\anaconda3\envs\test5\lib\concurrent\futures\_base.py", line 389, in __get_result
raise self._exception
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
2022-04-29 13:50:49,392:INFO:Initializing create_model()
2022-04-29 13:50:49,392:INFO:create_model(estimator=lar, fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x0000021ABCE77D90>, return_train_score=False, kwargs={})
2022-04-29 13:50:49,392:INFO:Checking exceptions
2022-04-29 13:50:49,392:INFO:Importing libraries
2022-04-29 13:50:49,392:INFO:Copying training dataset
2022-04-29 13:50:49,393:INFO:Defining folds
2022-04-29 13:50:49,393:INFO:Declaring metric variables
2022-04-29 13:50:49,393:INFO:Importing untrained model
2022-04-29 13:50:49,393:INFO:Least Angle Regression Imported succesfully
2022-04-29 13:50:49,393:INFO:Starting cross validation
2022-04-29 13:50:49,394:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-04-29 13:51:15,168:ERROR:create_model() for lar raised an exception or returned all 0.0:
2022-04-29 13:51:15,169:ERROR:joblib.externals.loky.process_executor._RemoteTraceback:
"""
Traceback (most recent call last):
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\externals\loky\process_executor.py", line 431, in _process_worker
r = call_item()
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\externals\loky\process_executor.py", line 285, in __call__
return self.fn(*self.args, **self.kwargs)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\_parallel_backends.py", line 595, in __call__
return self.func(*args, **kwargs)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\parallel.py", line 262, in __call__
return [func(*args, **kwargs)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\parallel.py", line 262, in <listcomp>
return [func(*args, **kwargs)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\model_selection\_validation.py", line 560, in _fit_and_score
test_scores = _score(estimator, X_test, y_test, scorer)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\model_selection\_validation.py", line 607, in _score
scores = scorer(estimator, X_test, y_test)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\metrics\_scorer.py", line 87, in __call__
score = scorer._score(cached_call, estimator,
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\metrics\_scorer.py", line 212, in _score
return self._sign * self._score_func(y_true, y_pred,
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\utils\validation.py", line 72, in inner_f
return f(**kwargs)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\metrics\_regression.py", line 178, in mean_absolute_error
y_type, y_true, y_pred, multioutput = _check_reg_targets(
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\metrics\_regression.py", line 86, in _check_reg_targets
y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\utils\validation.py", line 72, in inner_f
return f(**kwargs)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\utils\validation.py", line 644, in check_array
_assert_all_finite(array,
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\utils\validation.py", line 96, in _assert_all_finite
raise ValueError(
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\pycaret-2.3.10-py3.8.egg\pycaret\internal\tabular.py", line 2212, in compare_models
model, model_fit_time = create_model_supervised(**create_model_args)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\pycaret-2.3.10-py3.8.egg\pycaret\internal\tabular.py", line 3200, in create_model_supervised
scores = cross_validate(
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\utils\validation.py", line 72, in inner_f
return f(**kwargs)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\scikit_learn-0.23.2-py3.8-win-amd64.egg\sklearn\model_selection\_validation.py", line 242, in cross_validate
scores = parallel(
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\parallel.py", line 1054, in __call__
self.retrieve()
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\parallel.py", line 933, in retrieve
self._output.extend(job.get(timeout=self.timeout))
File "C:\Users\mahdi\anaconda3\envs\test5\lib\site-packages\joblib-1.0.1-py3.8.egg\joblib\_parallel_backends.py", line 542, in wrap_future_result
return future.result(timeout=timeout)
File "C:\Users\mahdi\anaconda3\envs\test5\lib\concurrent\futures\_base.py", line 437, in result
return self.__get_result()
File "C:\Users\mahdi\anaconda3\envs\test5\lib\concurrent\futures\_base.py", line 389, in __get_result
raise self._exception
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
2022-04-29 13:51:15,169:INFO:Initializing Bayesian Ridge
2022-04-29 13:51:15,169:INFO:Total runtime is 0.8749974886576335 minutes
2022-04-29 13:51:15,169:INFO:SubProcess create_model() called ==================================
2022-04-29 13:51:15,170:INFO:Initializing create_model()
2022-04-29 13:51:15,170:INFO:create_model(estimator=br, fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x0000021ABCE77D90>, return_train_score=False, kwargs={})
2022-04-29 13:51:15,170:INFO:Checking exceptions
2022-04-29 13:51:15,170:INFO:Importing libraries
2022-04-29 13:51:15,170:INFO:Copying training dataset
2022-04-29 13:51:15,170:INFO:Defining folds
2022-04-29 13:51:15,170:INFO:Declaring metric variables
2022-04-29 13:51:15,170:INFO:Importing untrained model
2022-04-29 13:51:15,171:INFO:Bayesian Ridge Imported succesfully
2022-04-29 13:51:15,171:INFO:Starting cross validation
2022-04-29 13:51:15,171:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-04-29 13:51:41,269:INFO:Calculating mean and std
2022-04-29 13:51:41,269:INFO:Creating metrics dataframe
2022-04-29 13:51:41,277:INFO:Uploading results into container
2022-04-29 13:51:41,278:INFO:Uploading model into container now
2022-04-29 13:51:41,278:INFO:create_model_container: 3
2022-04-29 13:51:41,278:INFO:master_model_container: 3
2022-04-29 13:51:41,278:INFO:display_container: 2
2022-04-29 13:51:41,279:INFO:BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, alpha_init=None,
compute_score=False, copy_X=True, fit_intercept=True,
lambda_1=1e-06, lambda_2=1e-06, lambda_init=None, n_iter=300,
normalize=False, tol=0.001, verbose=False)
2022-04-29 13:51:41,280:INFO:create_model() succesfully completed......................................
2022-04-29 13:51:41,424:INFO:SubProcess create_model() end ==================================
2022-04-29 13:51:41,424:INFO:Creating metrics dataframe
2022-04-29 13:51:41,435:INFO:Initializing Passive Aggressive Regressor
2022-04-29 13:51:41,435:INFO:Total runtime is 1.3127722024917603 minutes
2022-04-29 13:51:41,436:INFO:SubProcess create_model() called ==================================
2022-04-29 13:51:41,436:INFO:Initializing create_model()
2022-04-29 13:51:41,436:INFO:create_model(estimator=par, fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x0000021ABCE77D90>, return_train_score=False, kwargs={})
2022-04-29 13:51:41,436:INFO:Checking exceptions
2022-04-29 13:51:41,436:INFO:Importing libraries
2022-04-29 13:51:41,436:INFO:Copying training dataset
2022-04-29 13:51:41,437:INFO:Defining folds
2022-04-29 13:51:41,437:INFO:Declaring metric variables
2022-04-29 13:51:41,437:INFO:Importing untrained model
2022-04-29 13:51:41,437:INFO:Passive Aggressive Regressor Imported succesfully
2022-04-29 13:51:41,437:INFO:Starting cross validation
2022-04-29 13:51:41,438:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-04-29 13:51:41,534:INFO:Calculating mean and std
2022-04-29 13:51:41,535:INFO:Creating metrics dataframe
2022-04-29 13:51:41,540:INFO:Uploading results into container
2022-04-29 13:51:41,540:INFO:Uploading model into container now
2022-04-29 13:51:41,540:INFO:create_model_container: 4
2022-04-29 13:51:41,540:INFO:master_model_container: 4
2022-04-29 13:51:41,540:INFO:display_container: 2
2022-04-29 13:51:41,541:INFO:PassiveAggressiveRegressor(C=1.0, average=False, early_stopping=False,
epsilon=0.1, fit_intercept=True,
loss='epsilon_insensitive', max_iter=1000,
n_iter_no_change=5, random_state=123, shuffle=True,
tol=0.001, validation_fraction=0.1, verbose=0,
warm_start=False)
2022-04-29 13:51:41,541:INFO:create_model() succesfully completed......................................
2022-04-29 13:51:41,646:INFO:SubProcess create_model() end ==================================
2022-04-29 13:51:41,647:INFO:Creating metrics dataframe
2022-04-29 13:51:41,657:INFO:Initializing Huber Regressor
2022-04-29 13:51:41,657:INFO:Total runtime is 1.3164742430051168 minutes
2022-04-29 13:51:41,657:INFO:SubProcess create_model() called ==================================
2022-04-29 13:51:41,657:INFO:Initializing create_model()
2022-04-29 13:51:41,657:INFO:create_model(estimator=huber, fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x0000021ABCE77D90>, return_train_score=False, kwargs={})
2022-04-29 13:51:41,658:INFO:Checking exceptions
2022-04-29 13:51:41,658:INFO:Importing libraries
2022-04-29 13:51:41,658:INFO:Copying training dataset
2022-04-29 13:51:41,658:INFO:Defining folds
2022-04-29 13:51:41,658:INFO:Declaring metric variables
2022-04-29 13:51:41,658:INFO:Importing untrained model
2022-04-29 13:51:41,658:INFO:Huber Regressor Imported succesfully
2022-04-29 13:51:41,658:INFO:Starting cross validation
2022-04-29 13:51:41,659:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-04-29 13:51:41,944:INFO:Calculating mean and std
2022-04-29 13:51:41,945:INFO:Creating metrics dataframe
2022-04-29 13:51:41,951:INFO:Uploading results into container
2022-04-29 13:51:41,952:INFO:Uploading model into container now
2022-04-29 13:51:41,952:INFO:create_model_container: 5
2022-04-29 13:51:41,952:INFO:master_model_container: 5
2022-04-29 13:51:41,952:INFO:display_container: 2
2022-04-29 13:51:41,952:INFO:HuberRegressor(alpha=0.0001, epsilon=1.35, fit_intercept=True, max_iter=100,
tol=1e-05, warm_start=False)
2022-04-29 13:51:41,952:INFO:create_model() succesfully completed......................................
2022-04-29 13:51:42,060:INFO:SubProcess create_model() end ==================================
2022-04-29 13:51:42,060:INFO:Creating metrics dataframe
2022-04-29 13:51:42,072:INFO:Initializing Lasso Regression
2022-04-29 13:51:42,072:INFO:Total runtime is 1.323390857378642 minutes
2022-04-29 13:51:42,072:INFO:SubProcess create_model() called ==================================
2022-04-29 13:51:42,073:INFO:Initializing create_model()
2022-04-29 13:51:42,073:INFO:create_model(estimator=lasso, fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x0000021ABCE77D90>, return_train_score=False, kwargs={})
2022-04-29 13:51:42,073:INFO:Checking exceptions
2022-04-29 13:51:42,073:INFO:Importing libraries
2022-04-29 13:51:42,073:INFO:Copying training dataset
2022-04-29 13:51:42,073:INFO:Defining folds
2022-04-29 13:51:42,074:INFO:Declaring metric variables
2022-04-29 13:51:42,074:INFO:Importing untrained model
2022-04-29 13:51:42,074:INFO:Lasso Regression Imported succesfully
2022-04-29 13:51:42,074:INFO:Starting cross validation
2022-04-29 13:51:42,075:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-04-29 13:51:42,143:INFO:Calculating mean and std
2022-04-29 13:51:42,144:INFO:Creating metrics dataframe
2022-04-29 13:51:42,151:INFO:Uploading results into container
2022-04-29 13:51:42,151:INFO:Uploading model into container now
2022-04-29 13:51:42,152:INFO:create_model_container: 6
2022-04-29 13:51:42,152:INFO:master_model_container: 6
2022-04-29 13:51:42,152:INFO:display_container: 2
2022-04-29 13:51:42,152:INFO:Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=123,
selection='cyclic', tol=0.0001, warm_start=False)
2022-04-29 13:51:42,152:INFO:create_model() succesfully completed......................................
2022-04-29 13:51:42,255:INFO:SubProcess create_model() end ==================================
2022-04-29 13:51:42,255:INFO:Creating metrics dataframe
2022-04-29 13:51:42,265:INFO:Initializing Extra Trees Regressor
2022-04-29 13:51:42,266:INFO:Total runtime is 1.3266251087188723 minutes
2022-04-29 13:51:42,266:INFO:SubProcess create_model() called ==================================
2022-04-29 13:51:42,266:INFO:Initializing create_model()
2022-04-29 13:51:42,266:INFO:create_model(estimator=et, fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x0000021ABCE77D90>, return_train_score=False, kwargs={})
2022-04-29 13:51:42,266:INFO:Checking exceptions
2022-04-29 13:51:42,266:INFO:Importing libraries
2022-04-29 13:51:42,266:INFO:Copying training dataset
2022-04-29 13:51:42,266:INFO:Defining folds
2022-04-29 13:51:42,267:INFO:Declaring metric variables
2022-04-29 13:51:42,267:INFO:Importing untrained model
2022-04-29 13:51:42,267:INFO:Extra Trees Regressor Imported succesfully
2022-04-29 13:51:42,267:INFO:Starting cross validation
2022-04-29 13:51:42,268:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-04-29 13:51:43,539:INFO:Calculating mean and std
2022-04-29 13:51:43,540:INFO:Creating metrics dataframe
2022-04-29 13:51:43,548:INFO:Uploading results into container
2022-04-29 13:51:43,548:INFO:Uploading model into container now
2022-04-29 13:51:43,548:INFO:create_model_container: 7
2022-04-29 13:51:43,548:INFO:master_model_container: 7
2022-04-29 13:51:43,549:INFO:display_container: 2
2022-04-29 13:51:43,549:INFO:ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',
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)
2022-04-29 13:51:43,549:INFO:create_model() succesfully completed......................................
2022-04-29 13:51:43,654:INFO:SubProcess create_model() end ==================================
2022-04-29 13:51:43,654:INFO:Creating metrics dataframe
2022-04-29 13:51:43,665:INFO:Initializing Extreme Gradient Boosting
2022-04-29 13:51:43,666:INFO:Total runtime is 1.349958451588949 minutes
2022-04-29 13:51:43,666:INFO:SubProcess create_model() called ==================================
2022-04-29 13:51:43,666:INFO:Initializing create_model()
2022-04-29 13:51:43,666:INFO:create_model(estimator=xgboost, fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x0000021ABCE77D90>, return_train_score=False, kwargs={})
2022-04-29 13:51:43,666:INFO:Checking exceptions
2022-04-29 13:51:43,666:INFO:Importing libraries
2022-04-29 13:51:43,666:INFO:Copying training dataset
2022-04-29 13:51:43,667:INFO:Defining folds
2022-04-29 13:51:43,667:INFO:Declaring metric variables
2022-04-29 13:51:43,667:INFO:Importing untrained model
2022-04-29 13:51:43,668:INFO:Extreme Gradient Boosting Imported succesfully
2022-04-29 13:51:43,668:INFO:Starting cross validation
2022-04-29 13:51:43,671:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-04-29 13:51:46,435:INFO:Calculating mean and std
2022-04-29 13:51:46,436:INFO:Creating metrics dataframe
2022-04-29 13:51:46,441:INFO:Uploading results into container
2022-04-29 13:51:46,441:INFO:Uploading model into container now
2022-04-29 13:51:46,442:INFO:create_model_container: 8
2022-04-29 13:51:46,442:INFO:master_model_container: 8
2022-04-29 13:51:46,442:INFO:display_container: 2
2022-04-29 13:51:46,443:INFO:XGBRegressor(base_score=None, booster='gbtree', callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, gamma=None,
gpu_id=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=100, n_jobs=-1,
num_parallel_tree=None, objective='reg:squarederror',
predictor=None, random_state=123, reg_alpha=None, ...)
2022-04-29 13:51:46,443:INFO:create_model() succesfully completed......................................
2022-04-29 13:51:46,553:INFO:SubProcess create_model() end ==================================
2022-04-29 13:51:46,553:INFO:Creating metrics dataframe
2022-04-29 13:51:46,561:INFO:Initializing Light Gradient Boosting Machine
2022-04-29 13:51:46,562:INFO:Total runtime is 1.3982210755348208 minutes
2022-04-29 13:51:46,562:INFO:SubProcess create_model() called ==================================
2022-04-29 13:51:46,562:INFO:Initializing create_model()
2022-04-29 13:51:46,563:INFO:create_model(estimator=lightgbm, fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x0000021ABCE77D90>, return_train_score=False, kwargs={})
2022-04-29 13:51:46,563:INFO:Checking exceptions
2022-04-29 13:51:46,563:INFO:Importing libraries
2022-04-29 13:51:46,563:INFO:Copying training dataset
2022-04-29 13:51:46,564:INFO:Defining folds
2022-04-29 13:51:46,564:INFO:Declaring metric variables
2022-04-29 13:51:46,564:INFO:Importing untrained model
2022-04-29 13:51:46,564:INFO:Light Gradient Boosting Machine Imported succesfully
2022-04-29 13:51:46,564:INFO:Starting cross validation
2022-04-29 13:51:46,565:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-04-29 13:51:47,413:INFO:Calculating mean and std
2022-04-29 13:51:47,414:INFO:Creating metrics dataframe
2022-04-29 13:51:47,420:INFO:Uploading results into container
2022-04-29 13:51:47,420:INFO:Uploading model into container now
2022-04-29 13:51:47,420:INFO:create_model_container: 9
2022-04-29 13:51:47,420:INFO:master_model_container: 9
2022-04-29 13:51:47,420:INFO:display_container: 2
2022-04-29 13:51:47,421:INFO:LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,
importance_type='split', learning_rate=0.1, max_depth=-1,
min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,
random_state=123, reg_alpha=0.0, reg_lambda=0.0, silent='warn',
subsample=1.0, subsample_for_bin=200000, subsample_freq=0)
2022-04-29 13:51:47,421:INFO:create_model() succesfully completed......................................
2022-04-29 13:51:47,531:INFO:SubProcess create_model() end ==================================
2022-04-29 13:51:47,531:INFO:Creating metrics dataframe
2022-04-29 13:51:47,540:INFO:Initializing Random Forest Regressor
2022-04-29 13:51:47,540:INFO:Total runtime is 1.4145195921262108 minutes
2022-04-29 13:51:47,540:INFO:SubProcess create_model() called ==================================
2022-04-29 13:51:47,540:INFO:Initializing create_model()
2022-04-29 13:51:47,540:INFO:create_model(estimator=rf, fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x0000021ABCE77D90>, return_train_score=False, kwargs={})
2022-04-29 13:51:47,540:INFO:Checking exceptions
2022-04-29 13:51:47,540:INFO:Importing libraries
2022-04-29 13:51:47,541:INFO:Copying training dataset
2022-04-29 13:51:47,541:INFO:Defining folds
2022-04-29 13:51:47,541:INFO:Declaring metric variables
2022-04-29 13:51:47,541:INFO:Importing untrained model
2022-04-29 13:51:47,542:INFO:Random Forest Regressor Imported succesfully
2022-04-29 13:51:47,542:INFO:Starting cross validation
2022-04-29 13:51:47,542:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-04-29 13:51:49,489:INFO:Calculating mean and std
2022-04-29 13:51:49,489:INFO:Creating metrics dataframe
2022-04-29 13:51:49,497:INFO:Uploading results into container
2022-04-29 13:51:49,497:INFO:Uploading model into container now
2022-04-29 13:51:49,497:INFO:create_model_container: 10
2022-04-29 13:51:49,497:INFO:master_model_container: 10
2022-04-29 13:51:49,497:INFO:display_container: 2
2022-04-29 13:51:49,497:INFO:RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',
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)
2022-04-29 13:51:49,497:INFO:create_model() succesfully completed......................................
2022-04-29 13:51:49,609:INFO:SubProcess create_model() end ==================================
2022-04-29 13:51:49,609:INFO:Creating metrics dataframe
2022-04-29 13:51:49,622:INFO:Initializing Decision Tree Regressor
2022-04-29 13:51:49,622:INFO:Total runtime is 1.4492123723030093 minutes
2022-04-29 13:51:49,622:INFO:SubProcess create_model() called ==================================
2022-04-29 13:51:49,623:INFO:Initializing create_model()
2022-04-29 13:51:49,623:INFO:create_model(estimator=dt, fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x0000021ABCE77D90>, return_train_score=False, kwargs={})
2022-04-29 13:51:49,623:INFO:Checking exceptions
2022-04-29 13:51:49,623:INFO:Importing libraries
2022-04-29 13:51:49,623:INFO:Copying training dataset
2022-04-29 13:51:49,623:INFO:Defining folds
2022-04-29 13:51:49,623:INFO:Declaring metric variables
2022-04-29 13:51:49,623:INFO:Importing untrained model
2022-04-29 13:51:49,624:INFO:Decision Tree Regressor Imported succesfully
2022-04-29 13:51:49,624:INFO:Starting cross validation
2022-04-29 13:51:49,624:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-04-29 13:51:49,720:INFO:Calculating mean and std
2022-04-29 13:51:49,721:INFO:Creating metrics dataframe
2022-04-29 13:51:49,727:INFO:Uploading results into container
2022-04-29 13:51:49,728:INFO:Uploading model into container now
2022-04-29 13:51:49,728:INFO:create_model_container: 11
2022-04-29 13:51:49,728:INFO:master_model_container: 11
2022-04-29 13:51:49,728:INFO:display_container: 2
2022-04-29 13:51:49,729:INFO:DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', 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')
2022-04-29 13:51:49,729:INFO:create_model() succesfully completed......................................
2022-04-29 13:51:49,841:INFO:SubProcess create_model() end ==================================
2022-04-29 13:51:49,842:INFO:Creating metrics dataframe
2022-04-29 13:51:49,855:INFO:Initializing AdaBoost Regressor
2022-04-29 13:51:49,855:INFO:Total runtime is 1.453096652030945 minutes
2022-04-29 13:51:49,855:INFO:SubProcess create_model() called ==================================
2022-04-29 13:51:49,855:INFO:Initializing create_model()
2022-04-29 13:51:49,855:INFO:create_model(estimator=ada, fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=<pycaret.internal.Display.Display object at 0x0000021ABCE77D90>, return_train_score=False, kwargs={})
2022-04-29 13:51:49,856:INFO:Checking exceptions
2022-04-29 13:51:49,856:INFO:Importing libraries
2022-04-29 13:51:49,856:INFO:Copying training dataset
2022-04-29 13:51:49,856:INFO:Defining folds
2022-04-29 13:51:49,856:INFO:Declaring metric variables
2022-04-29 13:51:49,856:INFO:Importing untrained model
2022-04-29 13:51:49,856:INFO:AdaBoost Regressor Imported succesfully
2022-04-29 13:51:49,856:INFO:Starting cross validation
2022-04-29 13:51:49,857:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-04-29 13:51:50,499:INFO:Calculating mean and std
2022-04-29 13:51:50,499:INFO:Creating metrics dataframe
2022-04-29 13:51:50,505:INFO:Uploading results into container
2022-04-29 13:51:50,505:INFO:Uploading model into container now
2022-04-29 13:51:50,505:INFO:create_model_container: 12
2022-04-29 13:51:50,505:INFO:master_model_container: 12
2022-04-29 13:51:50,505:INFO:display_container: 2
2022-04-29 13:51:50,505:INFO:AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',
n_estimators=50, random_state=123)
2022-04-29 13:51:50,505:INFO:create_model() succesfully completed......................................
2022-04-29 13:51:50,613:INFO:SubProcess create_model() end ==================================
2022-04-29 13:51:50,613:INFO:Creating metrics dataframe
2022-04-29 13:51:50,625:INFO:Initializing create_model()
2022-04-29 13:51:50,625:INFO:create_model(estimator=BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, alpha_init=None,
compute_score=False, copy_X=True, fit_intercept=True,
lambda_1=1e-06, lambda_2=1e-06, lambda_init=None, n_iter=300,
normalize=False, tol=0.001, verbose=False), fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=None, return_train_score=False, kwargs={})
2022-04-29 13:51:50,625:INFO:Checking exceptions
2022-04-29 13:51:50,625:INFO:Importing libraries
2022-04-29 13:51:50,625:INFO:Copying training dataset
2022-04-29 13:51:50,626:INFO:Defining folds
2022-04-29 13:51:50,626:INFO:Declaring metric variables
2022-04-29 13:51:50,626:INFO:Importing untrained model
2022-04-29 13:51:50,626:INFO:Declaring custom model
2022-04-29 13:51:50,627:INFO:Bayesian Ridge Imported succesfully
2022-04-29 13:51:50,627:INFO:Cross validation set to False
2022-04-29 13:51:50,627:INFO:Fitting Model
2022-04-29 13:51:50,691:INFO:BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, alpha_init=None,
compute_score=False, copy_X=True, fit_intercept=True,
lambda_1=1e-06, lambda_2=1e-06, lambda_init=None, n_iter=300,
normalize=False, tol=0.001, verbose=False)
2022-04-29 13:51:50,691:INFO:create_models() succesfully completed......................................
2022-04-29 13:51:50,810:INFO:Initializing create_model()
2022-04-29 13:51:50,810:INFO:create_model(estimator=XGBRegressor(base_score=None, booster='gbtree', callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, gamma=None,
gpu_id=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=None, max_bin=None,
max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None, missing=nan,
monotone_constraints=None, n_estimators=100, n_jobs=-1,
num_parallel_tree=None, objective='reg:squarederror',
predictor=None, random_state=123, reg_alpha=None, ...), fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=None, return_train_score=False, kwargs={})
2022-04-29 13:51:50,810:INFO:Checking exceptions
2022-04-29 13:51:50,810:INFO:Importing libraries
2022-04-29 13:51:50,810:INFO:Copying training dataset
2022-04-29 13:51:50,811:INFO:Defining folds
2022-04-29 13:51:50,811:INFO:Declaring metric variables
2022-04-29 13:51:50,812:INFO:Importing untrained model
2022-04-29 13:51:50,812:INFO:Declaring custom model
2022-04-29 13:51:50,815:INFO:Extreme Gradient Boosting Imported succesfully
2022-04-29 13:51:50,816:INFO:Cross validation set to False
2022-04-29 13:51:50,816:INFO:Fitting Model
2022-04-29 13:51:51,652:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,
colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
early_stopping_rounds=None, enable_categorical=False,
eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
importance_type=None, interaction_constraints='',
learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
missing=nan, monotone_constraints='()', n_estimators=100,
n_jobs=-1, num_parallel_tree=1, objective='reg:squarederror',
predictor='auto', random_state=123, reg_alpha=0, ...)
2022-04-29 13:51:51,652:INFO:create_models() succesfully completed......................................
2022-04-29 13:51:51,766:INFO:Initializing create_model()
2022-04-29 13:51:51,766:INFO:create_model(estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,
normalize=False, random_state=123, solver='auto', tol=0.001), fold=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=None, return_train_score=False, kwargs={})
2022-04-29 13:51:51,766:INFO:Checking exceptions
2022-04-29 13:51:51,767:INFO:Importing libraries
2022-04-29 13:51:51,767:INFO:Copying training dataset
2022-04-29 13:51:51,767:INFO:Defining folds
2022-04-29 13:51:51,767:INFO:Declaring metric variables
2022-04-29 13:51:51,767:INFO:Importing untrained model
2022-04-29 13:51:51,767:INFO:Declaring custom model
2022-04-29 13:51:51,768:INFO:Ridge Regression Imported succesfully
2022-04-29 13:51:51,768:INFO:Cross validation set to False
2022-04-29 13:51:51,768:INFO:Fitting Model
2022-04-29 13:51:51,778:INFO:Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,
normalize=False, random_state=123, solver='auto', tol=0.001)
2022-04-29 13:51:51,778:INFO:create_models() succesfully completed......................................
2022-04-29 13:51:51,896:INFO:Initializing create_model()
2022-04-29 13:51:51,896:INFO:create_model(estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',
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=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=None, return_train_score=False, kwargs={})
2022-04-29 13:51:51,896:INFO:Checking exceptions
2022-04-29 13:51:51,896:INFO:Importing libraries
2022-04-29 13:51:51,896:INFO:Copying training dataset
2022-04-29 13:51:51,897:INFO:Defining folds
2022-04-29 13:51:51,897:INFO:Declaring metric variables
2022-04-29 13:51:51,897:INFO:Importing untrained model
2022-04-29 13:51:51,897:INFO:Declaring custom model
2022-04-29 13:51:51,898:INFO:Random Forest Regressor Imported succesfully
2022-04-29 13:51:51,899:INFO:Cross validation set to False
2022-04-29 13:51:51,899:INFO:Fitting Model
2022-04-29 13:51:52,301:INFO:RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',
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)
2022-04-29 13:51:52,301:INFO:create_models() succesfully completed......................................
2022-04-29 13:51:52,416:INFO:Initializing create_model()
2022-04-29 13:51:52,416:INFO:create_model(estimator=ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',
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=KFold(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, experiment_custom_tags=None, add_to_model_list=True, probability_threshold=None, display=None, return_train_score=False, kwargs={})
2022-04-29 13:51:52,417:INFO:Checking exceptions
2022-04-29 13:51:52,417:INFO:Importing libraries
2022-04-29 13:51:52,417:INFO:Copying training dataset
2022-04-29 13:51:52,417:INFO:Defining folds
2022-04-29 13:51:52,417:INFO:Declaring metric variables
2022-04-29 13:51:52,417:INFO:Importing untrained model
2022-04-29 13:51:52,417:INFO:Declaring custom model
2022-04-29 13:51:52,418:INFO:Extra Trees Regressor Imported succesfully
2022-04-29 13:51:52,419:INFO:Cross validation set to False
2022-04-29 13:51:52,419:INFO:Fitting Model
2022-04-29 13:51:52,723:INFO:ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',
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)
2022-04-29 13:51:52,723:INFO:create_models() succesfully completed......................................
2022-04-29 13:51:52,851:INFO:create_model_container: 12
2022-04-29 13:51:52,851:INFO:master_model_container: 12
2022-04-29 13:51:52,852:INFO:display_container: 2
2022-04-29 13:51:52,856:INFO:[BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, alpha_init=None,
compute_score=False, copy_X=True, fit_intercept=True,
lambda_1=1e-06, lambda_2=1e-06, lambda_init=None, n_iter=300,
normalize=False, tol=0.001, verbose=False), XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,
colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
early_stopping_rounds=None, enable_categorical=False,
eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
importance_type=None, interaction_constraints='',
learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
missing=nan, monotone_constraints='()', n_estimators=100,
n_jobs=-1, num_parallel_tree=1, objective='reg:squarederror',
predictor='auto', random_state=123, reg_alpha=0, ...), Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,
normalize=False, random_state=123, solver='auto', tol=0.001), RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',
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), ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',
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)]
2022-04-29 13:51:52,856:INFO:compare_models() succesfully completed......................................
2022-04-29 13:51:52,859:INFO:Initializing get_config()
2022-04-29 13:51:52,859:INFO:get_config(variable=X_train)
2022-04-29 13:51:53,103:INFO:Global variable: X_train returned as p124_A p163_A p260_A p39_ILF p173_L p63_V p309_M ... p304_V p39_M p297_T p104_V p127_S p169_GFLM p36_K
121_pigmentd_pigmentd_Rod_ancestor 1.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 1.0 0.0 0.0 0.0
5_HQ444180.1_Rhinomuraena_quaesita 1.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 1.0 0.0 0.0
20_GQ422472.1_Melanochromis_vermivorus 0.0 1.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 1.0 1.0 0.0 0.0
138_AB084927.1_Benthochromis_tricoti 0.0 1.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 1.0 0.0 0.0
30_AY214141.1_Oncorhynchus_keta 1.0 0.0 0.0 1.0 1.0 0.0 1.0 ... 0.0 0.0 0.0 1.0 1.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
18_L11863.1_Carassius_auratus 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 1.0 0.0 1.0 1.0 1.0 0.0 0.0
99_AB043817.1_S292A_Conger_myriaster 1.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 1.0 0.0 1.0 0.0
67_NM_001014890_E134R_R135E_Bos_taurus 1.0 0.0 1.0 0.0 0.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 0.0 0.0
127_pigmentj_pigmentj_Rod_ancestor 1.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 1.0 0.0 0.0 1.0 1.0 0.0 0.0
110_EU407248.1_Aristostomias_scintillans 1.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 0.0 0.0
[122 rows x 207 columns]
2022-04-29 13:51:53,103:INFO:get_config() succesfully completed......................................