diff --git "a/logs.log" "b/logs.log" new file mode 100644--- /dev/null +++ "b/logs.log" @@ -0,0 +1,14178 @@ +2024-04-25 13:15:13,659:WARNING:C:\Users\Jason\AppData\Local\Temp\ipykernel_19132\1099768027.py:1: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing. + data['Date'] = pd.to_datetime(data['Date']) + +2024-04-25 15:03:38,337:WARNING:C:\Users\Jason\AppData\Local\Temp\ipykernel_12636\1099768027.py:1: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing. + data['Date'] = pd.to_datetime(data['Date']) + +2024-04-25 15:16:45,126:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-04-25 15:16:45,129:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-04-25 15:16:45,129:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-04-25 15:16:45,130:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-04-25 15:20:40,655:INFO:PyCaret RegressionExperiment +2024-04-25 15:20:40,656:INFO:Logging name: reg-default-name +2024-04-25 15:20:40,657:INFO:ML Usecase: MLUsecase.REGRESSION +2024-04-25 15:20:40,658:INFO:version 3.3.0 +2024-04-25 15:20:40,658:INFO:Initializing setup() +2024-04-25 15:20:40,659:INFO:self.USI: 9cff +2024-04-25 15:20:40,660:INFO:self._variable_keys: {'transform_target_param', 'gpu_param', 'pipeline', 'log_plots_param', 'USI', 'y', 'logging_param', 'data', 'y_train', 'idx', '_ml_usecase', 'X_train', 'seed', 'exp_id', 'memory', 'html_param', 'fold_shuffle_param', 'target_param', 'fold_generator', 'n_jobs_param', 'gpu_n_jobs_param', 'X_test', 'y_test', 'fold_groups_param', '_available_plots', 'exp_name_log', 'X'} +2024-04-25 15:20:40,660:INFO:Checking environment +2024-04-25 15:20:40,661:INFO:python_version: 3.11.0 +2024-04-25 15:20:40,664:INFO:python_build: ('main', 'Oct 24 2022 18:26:48') +2024-04-25 15:20:40,665:INFO:machine: AMD64 +2024-04-25 15:20:40,665:INFO:platform: Windows-10-10.0.22000-SP0 +2024-04-25 15:20:40,720:INFO:Memory: svmem(total=8467492864, available=2503401472, percent=70.4, used=5964091392, free=2503401472) +2024-04-25 15:20:40,721:INFO:Physical Core: 2 +2024-04-25 15:20:40,722:INFO:Logical Core: 4 +2024-04-25 15:20:40,723:INFO:Checking libraries +2024-04-25 15:20:40,723:INFO:System: +2024-04-25 15:20:40,723:INFO: python: 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] +2024-04-25 15:20:40,725:INFO:executable: c:\Users\Jason\Documents\DataB3\.venv\Scripts\python.exe +2024-04-25 15:20:40,725:INFO: machine: Windows-10-10.0.22000-SP0 +2024-04-25 15:20:40,725:INFO:PyCaret required dependencies: +2024-04-25 15:20:41,941:INFO: pip: 24.0 +2024-04-25 15:20:41,942:INFO: setuptools: 65.5.0 +2024-04-25 15:20:41,943:INFO: pycaret: 3.3.0 +2024-04-25 15:20:41,943:INFO: IPython: 8.23.0 +2024-04-25 15:20:41,943:INFO: ipywidgets: 8.1.2 +2024-04-25 15:20:41,944:INFO: tqdm: 4.66.2 +2024-04-25 15:20:41,945:INFO: numpy: 1.24.4 +2024-04-25 15:20:41,946:INFO: pandas: 1.5.3 +2024-04-25 15:20:41,947:INFO: jinja2: 3.1.3 +2024-04-25 15:20:41,948:INFO: scipy: 1.11.4 +2024-04-25 15:20:41,948:INFO: joblib: 1.3.2 +2024-04-25 15:20:41,949:INFO: sklearn: 1.4.1.post1 +2024-04-25 15:20:41,949:INFO: pyod: 1.1.3 +2024-04-25 15:20:41,950:INFO: imblearn: 0.12.2 +2024-04-25 15:20:41,951:INFO: category_encoders: 2.6.3 +2024-04-25 15:20:41,952:INFO: lightgbm: 4.3.0 +2024-04-25 15:20:41,952:INFO: numba: 0.59.1 +2024-04-25 15:20:41,953:INFO: requests: 2.31.0 +2024-04-25 15:20:41,953:INFO: matplotlib: 3.8.3 +2024-04-25 15:20:41,954:INFO: scikitplot: 0.3.7 +2024-04-25 15:20:41,955:INFO: yellowbrick: 1.5 +2024-04-25 15:20:41,955:INFO: plotly: 5.20.0 +2024-04-25 15:20:41,956:INFO: plotly-resampler: Not installed +2024-04-25 15:20:41,956:INFO: kaleido: 0.2.1 +2024-04-25 15:20:41,957:INFO: schemdraw: 0.15 +2024-04-25 15:20:41,957:INFO: statsmodels: 0.14.1 +2024-04-25 15:20:41,958:INFO: sktime: 0.28.0 +2024-04-25 15:20:41,959:INFO: tbats: 1.1.3 +2024-04-25 15:20:41,959:INFO: pmdarima: 2.0.4 +2024-04-25 15:20:41,960:INFO: psutil: 5.9.8 +2024-04-25 15:20:41,960:INFO: markupsafe: 2.1.5 +2024-04-25 15:20:41,961:INFO: pickle5: Not installed +2024-04-25 15:20:41,961:INFO: cloudpickle: 3.0.0 +2024-04-25 15:20:41,962:INFO: deprecation: 2.1.0 +2024-04-25 15:20:41,962:INFO: xxhash: 3.4.1 +2024-04-25 15:20:41,963:INFO: wurlitzer: Not installed +2024-04-25 15:20:41,963:INFO:PyCaret optional dependencies: +2024-04-25 15:20:42,338:INFO: shap: Not installed +2024-04-25 15:20:42,341:INFO: interpret: Not installed +2024-04-25 15:20:42,342:INFO: umap: Not installed +2024-04-25 15:20:42,342:INFO: ydata_profiling: 4.7.0 +2024-04-25 15:20:42,343:INFO: explainerdashboard: Not installed +2024-04-25 15:20:42,343:INFO: autoviz: Not installed +2024-04-25 15:20:42,361:INFO: fairlearn: Not installed +2024-04-25 15:20:42,362:INFO: deepchecks: Not installed +2024-04-25 15:20:42,362:INFO: xgboost: 1.6.2 +2024-04-25 15:20:42,363:INFO: catboost: Not installed +2024-04-25 15:20:42,364:INFO: kmodes: Not installed +2024-04-25 15:20:42,365:INFO: mlxtend: Not installed +2024-04-25 15:20:42,365:INFO: statsforecast: Not installed +2024-04-25 15:20:42,367:INFO: tune_sklearn: Not installed +2024-04-25 15:20:42,367:INFO: ray: Not installed +2024-04-25 15:20:42,368:INFO: hyperopt: Not installed +2024-04-25 15:20:42,368:INFO: optuna: 3.6.1 +2024-04-25 15:20:42,376:INFO: skopt: Not installed +2024-04-25 15:20:42,376:INFO: mlflow: Not installed +2024-04-25 15:20:42,377:INFO: gradio: Not installed +2024-04-25 15:20:42,377:INFO: fastapi: Not installed +2024-04-25 15:20:42,378:INFO: uvicorn: Not installed +2024-04-25 15:20:42,378:INFO: m2cgen: Not installed +2024-04-25 15:20:42,379:INFO: evidently: Not installed +2024-04-25 15:20:42,379:INFO: fugue: Not installed +2024-04-25 15:20:42,380:INFO: streamlit: 1.33.0 +2024-04-25 15:20:42,403:INFO: prophet: 1.1.5 +2024-04-25 15:20:42,403:INFO:None +2024-04-25 15:20:42,404:INFO:Set up data. +2024-04-25 15:20:42,516:INFO:Set up folding strategy. +2024-04-25 15:20:42,517:INFO:Set up train/test split. +2024-04-25 15:20:42,747:INFO:Set up index. +2024-04-25 15:20:42,761:INFO:Assigning column types. +2024-04-25 15:20:42,891:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-04-25 15:20:42,898:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-04-25 15:20:43,171:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-04-25 15:20:43,314:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-04-25 15:20:44,998:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 15:20:46,068:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 15:20:46,072:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 15:20:48,425:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 15:20:48,429:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-04-25 15:20:48,512:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-04-25 15:20:48,601:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-04-25 15:20:49,740:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 15:20:50,422:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 15:20:50,429:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 15:20:50,473:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 15:20:50,475:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-04-25 15:20:50,549:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-04-25 15:20:50,621:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-04-25 15:20:51,595:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 15:20:52,393:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 15:20:52,397:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 15:20:52,438:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 15:20:52,516:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-04-25 15:20:52,590:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-04-25 15:20:53,620:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 15:20:54,350:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 15:20:54,355:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 15:20:54,417:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 15:20:54,423:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-04-25 15:20:54,636:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-04-25 15:20:55,799:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 15:20:56,771:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 15:20:56,776:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 15:20:56,831:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 15:20:57,087:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-04-25 15:20:58,269:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 15:20:59,080:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 15:20:59,086:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 15:20:59,132:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 15:20:59,135:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-04-25 15:21:00,338:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 15:21:01,638:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 15:21:01,645:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 15:21:01,714:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 15:21:02,803:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 15:21:03,602:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 15:21:03,607:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 15:21:03,665:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 15:21:03,668:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-04-25 15:21:04,888:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 15:21:05,706:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 15:21:05,752:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 15:21:06,870:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 15:21:07,711:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 15:21:07,763:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 15:21:07,768:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-04-25 15:21:10,041:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 15:21:10,079:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 15:21:11,899:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 15:21:11,944:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 15:21:12,048:INFO:Preparing preprocessing pipeline... +2024-04-25 15:21:12,049:INFO:Set up date feature engineering. +2024-04-25 15:21:12,049:INFO:Set up simple imputation. +2024-04-25 15:21:12,050:INFO:Set up feature normalization. +2024-04-25 15:21:12,780:INFO:Finished creating preprocessing pipeline. +2024-04-25 15:21:12,849:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\Jason\AppData\Local\Temp\joblib), + steps=[('date_feature_extractor', + TransformerWrapper(include=['Date'], + transformer=ExtractDateTimeFeatures())), + ('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWrapper(transformer=MinMaxScaler()))]) +2024-04-25 15:21:12,850:INFO:Creating final display dataframe. +2024-04-25 15:21:14,357:INFO:Setup _display_container: Description Value +0 Session id 8066 +1 Target Weekly_Sales +2 Target type Regression +3 Original data shape (6435, 8) +4 Transformed data shape (6435, 10) +5 Transformed train set shape (4504, 10) +6 Transformed test set shape (1931, 10) +7 Numeric features 6 +8 Date features 1 +9 Preprocess True +10 Imputation type simple +11 Numeric imputation mean +12 Categorical imputation mode +13 Normalize True +14 Normalize method minmax +15 Fold Generator KFold +16 Fold Number 10 +17 CPU Jobs -1 +18 Use GPU False +19 Log Experiment False +20 Experiment Name reg-default-name +21 USI 9cff +2024-04-25 15:21:16,968:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 15:21:17,027:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 15:21:19,317:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 15:21:19,352:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 15:21:19,440:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour. + warnings.warn( + +2024-04-25 15:21:19,441:INFO:setup() successfully completed in 39.1s............... +2024-04-25 15:22:24,930:INFO:Initializing compare_models() +2024-04-25 15:22:24,930:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-04-25 15:22:24,931:INFO:Checking exceptions +2024-04-25 15:22:24,949:INFO:Preparing display monitor +2024-04-25 15:22:25,182:INFO:Initializing Linear Regression +2024-04-25 15:22:25,183:INFO:Total runtime is 1.6609827677408855e-05 minutes +2024-04-25 15:22:25,218:INFO:SubProcess create_model() called ================================== +2024-04-25 15:22:25,222:INFO:Initializing create_model() +2024-04-25 15:22:25,222:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:22:25,223:INFO:Checking exceptions +2024-04-25 15:22:25,224:INFO:Importing libraries +2024-04-25 15:22:25,225:INFO:Copying training dataset +2024-04-25 15:22:25,317:INFO:Defining folds +2024-04-25 15:22:25,318:INFO:Declaring metric variables +2024-04-25 15:22:25,395:INFO:Importing untrained model +2024-04-25 15:22:25,589:INFO:Linear Regression Imported successfully +2024-04-25 15:22:25,716:INFO:Starting cross validation +2024-04-25 15:22:26,343:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:23:42,012:INFO:Calculating mean and std +2024-04-25 15:23:42,025:INFO:Creating metrics dataframe +2024-04-25 15:23:42,113:INFO:Uploading results into container +2024-04-25 15:23:42,124:INFO:Uploading model into container now +2024-04-25 15:23:42,127:INFO:_master_model_container: 1 +2024-04-25 15:23:42,128:INFO:_display_container: 2 +2024-04-25 15:23:42,129:INFO:LinearRegression(n_jobs=-1) +2024-04-25 15:23:42,130:INFO:create_model() successfully completed...................................... +2024-04-25 15:23:42,871:INFO:SubProcess create_model() end ================================== +2024-04-25 15:23:42,872:INFO:Creating metrics dataframe +2024-04-25 15:23:42,992:INFO:Initializing Lasso Regression +2024-04-25 15:23:42,993:INFO:Total runtime is 1.2968416412671409 minutes +2024-04-25 15:23:43,041:INFO:SubProcess create_model() called ================================== +2024-04-25 15:23:43,044:INFO:Initializing create_model() +2024-04-25 15:23:43,045:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:23:43,046:INFO:Checking exceptions +2024-04-25 15:23:43,051:INFO:Importing libraries +2024-04-25 15:23:43,052:INFO:Copying training dataset +2024-04-25 15:23:43,261:INFO:Defining folds +2024-04-25 15:23:43,261:INFO:Declaring metric variables +2024-04-25 15:23:43,390:INFO:Importing untrained model +2024-04-25 15:23:43,543:INFO:Lasso Regression Imported successfully +2024-04-25 15:23:43,711:INFO:Starting cross validation +2024-04-25 15:23:43,724:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:23:47,318:INFO:Calculating mean and std +2024-04-25 15:23:47,334:INFO:Creating metrics dataframe +2024-04-25 15:23:47,421:INFO:Uploading results into container +2024-04-25 15:23:47,436:INFO:Uploading model into container now +2024-04-25 15:23:47,439:INFO:_master_model_container: 2 +2024-04-25 15:23:47,439:INFO:_display_container: 2 +2024-04-25 15:23:47,444:INFO:Lasso(random_state=8066) +2024-04-25 15:23:47,445:INFO:create_model() successfully completed...................................... +2024-04-25 15:23:48,106:INFO:SubProcess create_model() end ================================== +2024-04-25 15:23:48,107:INFO:Creating metrics dataframe +2024-04-25 15:23:48,214:INFO:Initializing Ridge Regression +2024-04-25 15:23:48,215:INFO:Total runtime is 1.383877189954122 minutes +2024-04-25 15:23:48,252:INFO:SubProcess create_model() called ================================== +2024-04-25 15:23:48,254:INFO:Initializing create_model() +2024-04-25 15:23:48,255:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:23:48,256:INFO:Checking exceptions +2024-04-25 15:23:48,257:INFO:Importing libraries +2024-04-25 15:23:48,259:INFO:Copying training dataset +2024-04-25 15:23:48,618:INFO:Defining folds +2024-04-25 15:23:48,619:INFO:Declaring metric variables +2024-04-25 15:23:48,704:INFO:Importing untrained model +2024-04-25 15:23:48,763:INFO:Ridge Regression Imported successfully +2024-04-25 15:23:48,934:INFO:Starting cross validation +2024-04-25 15:23:48,956:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:23:52,232:INFO:Calculating mean and std +2024-04-25 15:23:52,246:INFO:Creating metrics dataframe +2024-04-25 15:23:52,324:INFO:Uploading results into container +2024-04-25 15:23:52,333:INFO:Uploading model into container now +2024-04-25 15:23:52,337:INFO:_master_model_container: 3 +2024-04-25 15:23:52,338:INFO:_display_container: 2 +2024-04-25 15:23:52,341:INFO:Ridge(random_state=8066) +2024-04-25 15:23:52,342:INFO:create_model() successfully completed...................................... +2024-04-25 15:23:52,828:INFO:SubProcess create_model() end ================================== +2024-04-25 15:23:52,829:INFO:Creating metrics dataframe +2024-04-25 15:23:52,943:INFO:Initializing Elastic Net +2024-04-25 15:23:52,943:INFO:Total runtime is 1.4626704692840578 minutes +2024-04-25 15:23:52,984:INFO:SubProcess create_model() called ================================== +2024-04-25 15:23:52,987:INFO:Initializing create_model() +2024-04-25 15:23:52,988:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:23:52,989:INFO:Checking exceptions +2024-04-25 15:23:52,990:INFO:Importing libraries +2024-04-25 15:23:52,996:INFO:Copying training dataset +2024-04-25 15:23:53,256:INFO:Defining folds +2024-04-25 15:23:53,259:INFO:Declaring metric variables +2024-04-25 15:23:53,306:INFO:Importing untrained model +2024-04-25 15:23:53,395:INFO:Elastic Net Imported successfully +2024-04-25 15:23:53,723:INFO:Starting cross validation +2024-04-25 15:23:53,734:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:23:59,232:INFO:Calculating mean and std +2024-04-25 15:23:59,243:INFO:Creating metrics dataframe +2024-04-25 15:23:59,303:INFO:Uploading results into container +2024-04-25 15:23:59,315:INFO:Uploading model into container now +2024-04-25 15:23:59,318:INFO:_master_model_container: 4 +2024-04-25 15:23:59,319:INFO:_display_container: 2 +2024-04-25 15:23:59,324:INFO:ElasticNet(random_state=8066) +2024-04-25 15:23:59,324:INFO:create_model() successfully completed...................................... +2024-04-25 15:23:59,886:INFO:SubProcess create_model() end ================================== +2024-04-25 15:23:59,889:INFO:Creating metrics dataframe +2024-04-25 15:24:00,085:INFO:Initializing Least Angle Regression +2024-04-25 15:24:00,086:INFO:Total runtime is 1.5817262053489687 minutes +2024-04-25 15:24:00,299:INFO:SubProcess create_model() called ================================== +2024-04-25 15:24:00,302:INFO:Initializing create_model() +2024-04-25 15:24:00,303:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:24:00,303:INFO:Checking exceptions +2024-04-25 15:24:00,304:INFO:Importing libraries +2024-04-25 15:24:00,305:INFO:Copying training dataset +2024-04-25 15:24:00,501:INFO:Defining folds +2024-04-25 15:24:00,502:INFO:Declaring metric variables +2024-04-25 15:24:00,640:INFO:Importing untrained model +2024-04-25 15:24:00,799:INFO:Least Angle Regression Imported successfully +2024-04-25 15:24:01,164:INFO:Starting cross validation +2024-04-25 15:24:01,183:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:24:04,504:INFO:Calculating mean and std +2024-04-25 15:24:04,519:INFO:Creating metrics dataframe +2024-04-25 15:24:04,606:INFO:Uploading results into container +2024-04-25 15:24:04,621:INFO:Uploading model into container now +2024-04-25 15:24:04,627:INFO:_master_model_container: 5 +2024-04-25 15:24:04,628:INFO:_display_container: 2 +2024-04-25 15:24:04,632:INFO:Lars(random_state=8066) +2024-04-25 15:24:04,633:INFO:create_model() successfully completed...................................... +2024-04-25 15:24:05,197:INFO:SubProcess create_model() end ================================== +2024-04-25 15:24:05,198:INFO:Creating metrics dataframe +2024-04-25 15:24:05,322:INFO:Initializing Lasso Least Angle Regression +2024-04-25 15:24:05,323:INFO:Total runtime is 1.6690115054448447 minutes +2024-04-25 15:24:05,370:INFO:SubProcess create_model() called ================================== +2024-04-25 15:24:05,373:INFO:Initializing create_model() +2024-04-25 15:24:05,375:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:24:05,377:INFO:Checking exceptions +2024-04-25 15:24:05,378:INFO:Importing libraries +2024-04-25 15:24:05,379:INFO:Copying training dataset +2024-04-25 15:24:05,619:INFO:Defining folds +2024-04-25 15:24:05,620:INFO:Declaring metric variables +2024-04-25 15:24:05,725:INFO:Importing untrained model +2024-04-25 15:24:05,771:INFO:Lasso Least Angle Regression Imported successfully +2024-04-25 15:24:05,919:INFO:Starting cross validation +2024-04-25 15:24:05,938:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:24:09,199:INFO:Calculating mean and std +2024-04-25 15:24:09,211:INFO:Creating metrics dataframe +2024-04-25 15:24:09,285:INFO:Uploading results into container +2024-04-25 15:24:09,290:INFO:Uploading model into container now +2024-04-25 15:24:09,293:INFO:_master_model_container: 6 +2024-04-25 15:24:09,295:INFO:_display_container: 2 +2024-04-25 15:24:09,299:INFO:LassoLars(random_state=8066) +2024-04-25 15:24:09,300:INFO:create_model() successfully completed...................................... +2024-04-25 15:24:09,761:INFO:SubProcess create_model() end ================================== +2024-04-25 15:24:09,763:INFO:Creating metrics dataframe +2024-04-25 15:24:09,905:INFO:Initializing Orthogonal Matching Pursuit +2024-04-25 15:24:09,905:INFO:Total runtime is 1.7453736941019695 minutes +2024-04-25 15:24:09,940:INFO:SubProcess create_model() called ================================== +2024-04-25 15:24:09,942:INFO:Initializing create_model() +2024-04-25 15:24:09,943:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:24:09,944:INFO:Checking exceptions +2024-04-25 15:24:09,945:INFO:Importing libraries +2024-04-25 15:24:09,945:INFO:Copying training dataset +2024-04-25 15:24:10,287:INFO:Defining folds +2024-04-25 15:24:10,292:INFO:Declaring metric variables +2024-04-25 15:24:10,366:INFO:Importing untrained model +2024-04-25 15:24:10,423:INFO:Orthogonal Matching Pursuit Imported successfully +2024-04-25 15:24:10,636:INFO:Starting cross validation +2024-04-25 15:24:10,658:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:24:14,041:INFO:Calculating mean and std +2024-04-25 15:24:14,056:INFO:Creating metrics dataframe +2024-04-25 15:24:14,222:INFO:Uploading results into container +2024-04-25 15:24:14,283:INFO:Uploading model into container now +2024-04-25 15:24:14,285:INFO:_master_model_container: 7 +2024-04-25 15:24:14,286:INFO:_display_container: 2 +2024-04-25 15:24:14,346:INFO:OrthogonalMatchingPursuit() +2024-04-25 15:24:14,359:INFO:create_model() successfully completed...................................... +2024-04-25 15:24:15,950:INFO:SubProcess create_model() end ================================== +2024-04-25 15:24:15,976:INFO:Creating metrics dataframe +2024-04-25 15:24:16,292:INFO:Initializing Bayesian Ridge +2024-04-25 15:24:16,293:INFO:Total runtime is 1.851841183503469 minutes +2024-04-25 15:24:16,508:INFO:SubProcess create_model() called ================================== +2024-04-25 15:24:16,513:INFO:Initializing create_model() +2024-04-25 15:24:16,514:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:24:16,515:INFO:Checking exceptions +2024-04-25 15:24:16,515:INFO:Importing libraries +2024-04-25 15:24:16,516:INFO:Copying training dataset +2024-04-25 15:24:16,735:INFO:Defining folds +2024-04-25 15:24:16,736:INFO:Declaring metric variables +2024-04-25 15:24:16,811:INFO:Importing untrained model +2024-04-25 15:24:16,866:INFO:Bayesian Ridge Imported successfully +2024-04-25 15:24:17,134:INFO:Starting cross validation +2024-04-25 15:24:17,208:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:24:20,745:INFO:Calculating mean and std +2024-04-25 15:24:20,761:INFO:Creating metrics dataframe +2024-04-25 15:24:20,819:INFO:Uploading results into container +2024-04-25 15:24:20,824:INFO:Uploading model into container now +2024-04-25 15:24:20,830:INFO:_master_model_container: 8 +2024-04-25 15:24:20,831:INFO:_display_container: 2 +2024-04-25 15:24:20,836:INFO:BayesianRidge() +2024-04-25 15:24:20,837:INFO:create_model() successfully completed...................................... +2024-04-25 15:24:21,290:INFO:SubProcess create_model() end ================================== +2024-04-25 15:24:21,291:INFO:Creating metrics dataframe +2024-04-25 15:24:21,405:INFO:Initializing Passive Aggressive Regressor +2024-04-25 15:24:21,406:INFO:Total runtime is 1.9370617707570394 minutes +2024-04-25 15:24:21,439:INFO:SubProcess create_model() called ================================== +2024-04-25 15:24:21,473:INFO:Initializing create_model() +2024-04-25 15:24:21,474:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:24:21,474:INFO:Checking exceptions +2024-04-25 15:24:21,475:INFO:Importing libraries +2024-04-25 15:24:21,475:INFO:Copying training dataset +2024-04-25 15:24:21,677:INFO:Defining folds +2024-04-25 15:24:21,679:INFO:Declaring metric variables +2024-04-25 15:24:21,768:INFO:Importing untrained model +2024-04-25 15:24:21,829:INFO:Passive Aggressive Regressor Imported successfully +2024-04-25 15:24:22,160:INFO:Starting cross validation +2024-04-25 15:24:22,171:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:24:29,516:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 15:24:29,517:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 15:24:29,516:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 15:24:29,621:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 15:24:36,219:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 15:24:36,332:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 15:24:36,340:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 15:24:36,422:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 15:24:40,682:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 15:24:40,769:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 15:24:40,992:INFO:Calculating mean and std +2024-04-25 15:24:41,065:INFO:Creating metrics dataframe +2024-04-25 15:24:41,184:INFO:Uploading results into container +2024-04-25 15:24:41,195:INFO:Uploading model into container now +2024-04-25 15:24:41,210:INFO:_master_model_container: 9 +2024-04-25 15:24:41,211:INFO:_display_container: 2 +2024-04-25 15:24:41,217:INFO:PassiveAggressiveRegressor(random_state=8066) +2024-04-25 15:24:41,218:INFO:create_model() successfully completed...................................... +2024-04-25 15:24:41,705:INFO:SubProcess create_model() end ================================== +2024-04-25 15:24:41,706:INFO:Creating metrics dataframe +2024-04-25 15:24:41,823:INFO:Initializing Huber Regressor +2024-04-25 15:24:41,824:INFO:Total runtime is 2.277361325422923 minutes +2024-04-25 15:24:41,868:INFO:SubProcess create_model() called ================================== +2024-04-25 15:24:41,870:INFO:Initializing create_model() +2024-04-25 15:24:41,871:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:24:41,872:INFO:Checking exceptions +2024-04-25 15:24:41,872:INFO:Importing libraries +2024-04-25 15:24:41,875:INFO:Copying training dataset +2024-04-25 15:24:42,131:INFO:Defining folds +2024-04-25 15:24:42,133:INFO:Declaring metric variables +2024-04-25 15:24:42,218:INFO:Importing untrained model +2024-04-25 15:24:42,272:INFO:Huber Regressor Imported successfully +2024-04-25 15:24:42,482:INFO:Starting cross validation +2024-04-25 15:24:42,582:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:24:46,766:INFO:Calculating mean and std +2024-04-25 15:24:46,788:INFO:Creating metrics dataframe +2024-04-25 15:24:46,919:INFO:Uploading results into container +2024-04-25 15:24:46,931:INFO:Uploading model into container now +2024-04-25 15:24:46,937:INFO:_master_model_container: 10 +2024-04-25 15:24:46,938:INFO:_display_container: 2 +2024-04-25 15:24:46,940:INFO:HuberRegressor() +2024-04-25 15:24:46,941:INFO:create_model() successfully completed...................................... +2024-04-25 15:24:47,431:INFO:SubProcess create_model() end ================================== +2024-04-25 15:24:47,431:INFO:Creating metrics dataframe +2024-04-25 15:24:47,574:INFO:Initializing K Neighbors Regressor +2024-04-25 15:24:47,575:INFO:Total runtime is 2.3732053915659588 minutes +2024-04-25 15:24:47,696:INFO:SubProcess create_model() called ================================== +2024-04-25 15:24:47,697:INFO:Initializing create_model() +2024-04-25 15:24:47,698:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:24:47,700:INFO:Checking exceptions +2024-04-25 15:24:47,703:INFO:Importing libraries +2024-04-25 15:24:47,704:INFO:Copying training dataset +2024-04-25 15:24:48,101:INFO:Defining folds +2024-04-25 15:24:48,104:INFO:Declaring metric variables +2024-04-25 15:24:48,148:INFO:Importing untrained model +2024-04-25 15:24:48,184:INFO:K Neighbors Regressor Imported successfully +2024-04-25 15:24:48,384:INFO:Starting cross validation +2024-04-25 15:24:48,419:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:24:54,079:INFO:Calculating mean and std +2024-04-25 15:24:54,093:INFO:Creating metrics dataframe +2024-04-25 15:24:54,177:INFO:Uploading results into container +2024-04-25 15:24:54,184:INFO:Uploading model into container now +2024-04-25 15:24:54,186:INFO:_master_model_container: 11 +2024-04-25 15:24:54,188:INFO:_display_container: 2 +2024-04-25 15:24:54,193:INFO:KNeighborsRegressor(n_jobs=-1) +2024-04-25 15:24:54,193:INFO:create_model() successfully completed...................................... +2024-04-25 15:24:54,687:INFO:SubProcess create_model() end ================================== +2024-04-25 15:24:54,688:INFO:Creating metrics dataframe +2024-04-25 15:24:54,868:INFO:Initializing Decision Tree Regressor +2024-04-25 15:24:54,869:INFO:Total runtime is 2.4947778224945067 minutes +2024-04-25 15:24:54,916:INFO:SubProcess create_model() called ================================== +2024-04-25 15:24:54,922:INFO:Initializing create_model() +2024-04-25 15:24:54,923:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:24:54,924:INFO:Checking exceptions +2024-04-25 15:24:54,925:INFO:Importing libraries +2024-04-25 15:24:54,926:INFO:Copying training dataset +2024-04-25 15:24:55,255:INFO:Defining folds +2024-04-25 15:24:55,256:INFO:Declaring metric variables +2024-04-25 15:24:55,312:INFO:Importing untrained model +2024-04-25 15:24:55,479:INFO:Decision Tree Regressor Imported successfully +2024-04-25 15:24:55,716:INFO:Starting cross validation +2024-04-25 15:24:55,746:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:25:00,552:INFO:Calculating mean and std +2024-04-25 15:25:00,597:INFO:Creating metrics dataframe +2024-04-25 15:25:00,790:INFO:Uploading results into container +2024-04-25 15:25:00,796:INFO:Uploading model into container now +2024-04-25 15:25:00,806:INFO:_master_model_container: 12 +2024-04-25 15:25:00,806:INFO:_display_container: 2 +2024-04-25 15:25:00,810:INFO:DecisionTreeRegressor(random_state=8066) +2024-04-25 15:25:00,812:INFO:create_model() successfully completed...................................... +2024-04-25 15:25:01,563:INFO:SubProcess create_model() end ================================== +2024-04-25 15:25:01,564:INFO:Creating metrics dataframe +2024-04-25 15:25:01,765:INFO:Initializing Random Forest Regressor +2024-04-25 15:25:01,765:INFO:Total runtime is 2.6097036679585774 minutes +2024-04-25 15:25:01,800:INFO:SubProcess create_model() called ================================== +2024-04-25 15:25:01,802:INFO:Initializing create_model() +2024-04-25 15:25:01,803:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:25:01,804:INFO:Checking exceptions +2024-04-25 15:25:01,804:INFO:Importing libraries +2024-04-25 15:25:01,805:INFO:Copying training dataset +2024-04-25 15:25:01,995:INFO:Defining folds +2024-04-25 15:25:01,996:INFO:Declaring metric variables +2024-04-25 15:25:02,094:INFO:Importing untrained model +2024-04-25 15:25:02,131:INFO:Random Forest Regressor Imported successfully +2024-04-25 15:25:02,508:INFO:Starting cross validation +2024-04-25 15:25:02,518:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:26:44,720:INFO:Calculating mean and std +2024-04-25 15:26:44,767:INFO:Creating metrics dataframe +2024-04-25 15:26:44,874:INFO:Uploading results into container +2024-04-25 15:26:44,884:INFO:Uploading model into container now +2024-04-25 15:26:44,892:INFO:_master_model_container: 13 +2024-04-25 15:26:44,893:INFO:_display_container: 2 +2024-04-25 15:26:44,950:INFO:RandomForestRegressor(n_jobs=-1, random_state=8066) +2024-04-25 15:26:44,951:INFO:create_model() successfully completed...................................... +2024-04-25 15:26:45,601:INFO:SubProcess create_model() end ================================== +2024-04-25 15:26:45,601:INFO:Creating metrics dataframe +2024-04-25 15:26:45,806:INFO:Initializing Extra Trees Regressor +2024-04-25 15:26:45,809:INFO:Total runtime is 4.343774159749349 minutes +2024-04-25 15:26:45,970:INFO:SubProcess create_model() called ================================== +2024-04-25 15:26:46,122:INFO:Initializing create_model() +2024-04-25 15:26:46,123:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:26:46,124:INFO:Checking exceptions +2024-04-25 15:26:46,124:INFO:Importing libraries +2024-04-25 15:26:46,126:INFO:Copying training dataset +2024-04-25 15:26:46,312:INFO:Defining folds +2024-04-25 15:26:46,312:INFO:Declaring metric variables +2024-04-25 15:26:46,410:INFO:Importing untrained model +2024-04-25 15:26:46,703:INFO:Extra Trees Regressor Imported successfully +2024-04-25 15:26:47,319:INFO:Starting cross validation +2024-04-25 15:26:47,329:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:27:54,833:INFO:Calculating mean and std +2024-04-25 15:27:54,852:INFO:Creating metrics dataframe +2024-04-25 15:27:55,006:INFO:Uploading results into container +2024-04-25 15:27:55,033:INFO:Uploading model into container now +2024-04-25 15:27:55,036:INFO:_master_model_container: 14 +2024-04-25 15:27:55,039:INFO:_display_container: 2 +2024-04-25 15:27:55,096:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=8066) +2024-04-25 15:27:55,098:INFO:create_model() successfully completed...................................... +2024-04-25 15:27:55,888:INFO:SubProcess create_model() end ================================== +2024-04-25 15:27:55,888:INFO:Creating metrics dataframe +2024-04-25 15:27:56,101:INFO:Initializing AdaBoost Regressor +2024-04-25 15:27:56,102:INFO:Total runtime is 5.515305499235788 minutes +2024-04-25 15:27:56,226:INFO:SubProcess create_model() called ================================== +2024-04-25 15:27:56,236:INFO:Initializing create_model() +2024-04-25 15:27:56,237:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:27:56,238:INFO:Checking exceptions +2024-04-25 15:27:56,238:INFO:Importing libraries +2024-04-25 15:27:56,239:INFO:Copying training dataset +2024-04-25 15:27:56,477:INFO:Defining folds +2024-04-25 15:27:56,479:INFO:Declaring metric variables +2024-04-25 15:27:56,530:INFO:Importing untrained model +2024-04-25 15:27:56,579:INFO:AdaBoost Regressor Imported successfully +2024-04-25 15:27:56,758:INFO:Starting cross validation +2024-04-25 15:27:56,774:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:28:29,605:INFO:Calculating mean and std +2024-04-25 15:28:29,779:INFO:Creating metrics dataframe +2024-04-25 15:28:30,056:INFO:Uploading results into container +2024-04-25 15:28:30,109:INFO:Uploading model into container now +2024-04-25 15:28:30,121:INFO:_master_model_container: 15 +2024-04-25 15:28:30,122:INFO:_display_container: 2 +2024-04-25 15:28:30,129:INFO:AdaBoostRegressor(random_state=8066) +2024-04-25 15:28:30,130:INFO:create_model() successfully completed...................................... +2024-04-25 15:28:32,613:INFO:SubProcess create_model() end ================================== +2024-04-25 15:28:32,678:INFO:Creating metrics dataframe +2024-04-25 15:28:33,341:INFO:Initializing Gradient Boosting Regressor +2024-04-25 15:28:33,343:INFO:Total runtime is 6.13601042429606 minutes +2024-04-25 15:28:33,517:INFO:SubProcess create_model() called ================================== +2024-04-25 15:28:33,519:INFO:Initializing create_model() +2024-04-25 15:28:33,520:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:28:33,521:INFO:Checking exceptions +2024-04-25 15:28:33,521:INFO:Importing libraries +2024-04-25 15:28:33,522:INFO:Copying training dataset +2024-04-25 15:28:34,503:INFO:Defining folds +2024-04-25 15:28:34,505:INFO:Declaring metric variables +2024-04-25 15:28:34,784:INFO:Importing untrained model +2024-04-25 15:28:35,740:INFO:Gradient Boosting Regressor Imported successfully +2024-04-25 15:28:36,377:INFO:Starting cross validation +2024-04-25 15:28:36,386:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:30:23,205:INFO:Calculating mean and std +2024-04-25 15:30:23,379:INFO:Creating metrics dataframe +2024-04-25 15:30:23,973:INFO:Uploading results into container +2024-04-25 15:30:23,979:INFO:Uploading model into container now +2024-04-25 15:30:24,039:INFO:_master_model_container: 16 +2024-04-25 15:30:24,041:INFO:_display_container: 2 +2024-04-25 15:30:24,250:INFO:GradientBoostingRegressor(random_state=8066) +2024-04-25 15:30:24,250:INFO:create_model() successfully completed...................................... +2024-04-25 15:30:25,629:INFO:SubProcess create_model() end ================================== +2024-04-25 15:30:25,642:INFO:Creating metrics dataframe +2024-04-25 15:30:26,016:INFO:Initializing Extreme Gradient Boosting +2024-04-25 15:30:26,019:INFO:Total runtime is 8.013944113254546 minutes +2024-04-25 15:30:26,100:INFO:SubProcess create_model() called ================================== +2024-04-25 15:30:26,103:INFO:Initializing create_model() +2024-04-25 15:30:26,105:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:30:26,106:INFO:Checking exceptions +2024-04-25 15:30:26,106:INFO:Importing libraries +2024-04-25 15:30:26,107:INFO:Copying training dataset +2024-04-25 15:30:26,610:INFO:Defining folds +2024-04-25 15:30:26,611:INFO:Declaring metric variables +2024-04-25 15:30:26,857:INFO:Importing untrained model +2024-04-25 15:30:27,224:INFO:Extreme Gradient Boosting Imported successfully +2024-04-25 15:30:28,219:INFO:Starting cross validation +2024-04-25 15:30:28,256:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:32:20,541:INFO:Calculating mean and std +2024-04-25 15:32:20,753:INFO:Creating metrics dataframe +2024-04-25 15:32:21,062:INFO:Uploading results into container +2024-04-25 15:32:21,146:INFO:Uploading model into container now +2024-04-25 15:32:21,155:INFO:_master_model_container: 17 +2024-04-25 15:32:21,172:INFO:_display_container: 2 +2024-04-25 15:32:21,327: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, predictor=None, random_state=8066, + reg_alpha=None, reg_lambda=None, ...) +2024-04-25 15:32:21,330:INFO:create_model() successfully completed...................................... +2024-04-25 15:32:26,046:INFO:SubProcess create_model() end ================================== +2024-04-25 15:32:26,047:INFO:Creating metrics dataframe +2024-04-25 15:32:26,659:INFO:Initializing Light Gradient Boosting Machine +2024-04-25 15:32:26,668:INFO:Total runtime is 10.024703939755756 minutes +2024-04-25 15:32:26,899:INFO:SubProcess create_model() called ================================== +2024-04-25 15:32:26,901:INFO:Initializing create_model() +2024-04-25 15:32:26,905:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:32:27,099:INFO:Checking exceptions +2024-04-25 15:32:27,100:INFO:Importing libraries +2024-04-25 15:32:27,101:INFO:Copying training dataset +2024-04-25 15:32:27,761:INFO:Defining folds +2024-04-25 15:32:27,770:INFO:Declaring metric variables +2024-04-25 15:32:28,125:INFO:Importing untrained model +2024-04-25 15:32:28,262:INFO:Light Gradient Boosting Machine Imported successfully +2024-04-25 15:32:29,016:INFO:Starting cross validation +2024-04-25 15:32:29,034:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:33:35,065:INFO:Calculating mean and std +2024-04-25 15:33:35,097:INFO:Creating metrics dataframe +2024-04-25 15:33:35,440:INFO:Uploading results into container +2024-04-25 15:33:35,515:INFO:Uploading model into container now +2024-04-25 15:33:35,645:INFO:_master_model_container: 18 +2024-04-25 15:33:35,647:INFO:_display_container: 2 +2024-04-25 15:33:35,670:INFO:LGBMRegressor(n_jobs=-1, random_state=8066) +2024-04-25 15:33:35,695:INFO:create_model() successfully completed...................................... +2024-04-25 15:33:37,263:INFO:SubProcess create_model() end ================================== +2024-04-25 15:33:37,265:INFO:Creating metrics dataframe +2024-04-25 15:33:37,720:INFO:Initializing Dummy Regressor +2024-04-25 15:33:37,723:INFO:Total runtime is 11.209017113844553 minutes +2024-04-25 15:33:37,918:INFO:SubProcess create_model() called ================================== +2024-04-25 15:33:37,922:INFO:Initializing create_model() +2024-04-25 15:33:37,923:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:33:37,923:INFO:Checking exceptions +2024-04-25 15:33:37,924:INFO:Importing libraries +2024-04-25 15:33:37,924:INFO:Copying training dataset +2024-04-25 15:33:38,617:INFO:Defining folds +2024-04-25 15:33:38,619:INFO:Declaring metric variables +2024-04-25 15:33:38,903:INFO:Importing untrained model +2024-04-25 15:33:39,158:INFO:Dummy Regressor Imported successfully +2024-04-25 15:33:39,958:INFO:Starting cross validation +2024-04-25 15:33:39,981:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:33:50,936:INFO:Calculating mean and std +2024-04-25 15:33:51,101:INFO:Creating metrics dataframe +2024-04-25 15:33:51,360:INFO:Uploading results into container +2024-04-25 15:33:51,377:INFO:Uploading model into container now +2024-04-25 15:33:51,437:INFO:_master_model_container: 19 +2024-04-25 15:33:51,487:INFO:_display_container: 2 +2024-04-25 15:33:51,521:INFO:DummyRegressor() +2024-04-25 15:33:51,521:INFO:create_model() successfully completed...................................... +2024-04-25 15:33:53,307:INFO:SubProcess create_model() end ================================== +2024-04-25 15:33:53,309:INFO:Creating metrics dataframe +2024-04-25 15:33:54,834:INFO:Initializing create_model() +2024-04-25 15:33:54,835:INFO:create_model(self=, estimator=LGBMRegressor(n_jobs=-1, random_state=8066), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:33:54,840:INFO:Checking exceptions +2024-04-25 15:33:55,229:INFO:Importing libraries +2024-04-25 15:33:55,231:INFO:Copying training dataset +2024-04-25 15:33:55,875:INFO:Defining folds +2024-04-25 15:33:55,876:INFO:Declaring metric variables +2024-04-25 15:33:55,890:INFO:Importing untrained model +2024-04-25 15:33:55,891:INFO:Declaring custom model +2024-04-25 15:33:55,968:INFO:Light Gradient Boosting Machine Imported successfully +2024-04-25 15:33:55,991:INFO:Cross validation set to False +2024-04-25 15:33:55,992:INFO:Fitting Model +2024-04-25 15:33:58,890:INFO:[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.447170 seconds. +2024-04-25 15:33:58,912:INFO:You can set `force_col_wise=true` to remove the overhead. +2024-04-25 15:33:58,922:INFO:[LightGBM] [Info] Total Bins 1096 +2024-04-25 15:33:59,011:INFO:[LightGBM] [Info] Number of data points in the train set: 4504, number of used features: 9 +2024-04-25 15:33:59,142:INFO:[LightGBM] [Info] Start training from score 1047274.394195 +2024-04-25 15:34:08,728:INFO:LGBMRegressor(n_jobs=-1, random_state=8066) +2024-04-25 15:34:08,730:INFO:create_model() successfully completed...................................... +2024-04-25 15:34:13,228:INFO:_master_model_container: 19 +2024-04-25 15:34:13,231:INFO:_display_container: 2 +2024-04-25 15:34:13,237:INFO:LGBMRegressor(n_jobs=-1, random_state=8066) +2024-04-25 15:34:13,238:INFO:compare_models() successfully completed...................................... +2024-04-25 15:36:06,615:INFO:Initializing create_model() +2024-04-25 15:36:06,616:INFO:create_model(self=, estimator=xgboost, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 15:36:06,617:INFO:Checking exceptions +2024-04-25 15:36:07,016:INFO:Importing libraries +2024-04-25 15:36:07,016:INFO:Copying training dataset +2024-04-25 15:36:07,509:INFO:Defining folds +2024-04-25 15:36:07,510:INFO:Declaring metric variables +2024-04-25 15:36:07,646:INFO:Importing untrained model +2024-04-25 15:36:07,799:INFO:Extreme Gradient Boosting Imported successfully +2024-04-25 15:36:07,916:INFO:Starting cross validation +2024-04-25 15:36:07,926:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 15:37:29,313:INFO:Calculating mean and std +2024-04-25 15:37:29,324:INFO:Creating metrics dataframe +2024-04-25 15:37:29,681:INFO:Finalizing model +2024-04-25 15:37:49,725:INFO:Uploading results into container +2024-04-25 15:37:49,736:INFO:Uploading model into container now +2024-04-25 15:37:49,921:INFO:_master_model_container: 20 +2024-04-25 15:37:49,924:INFO:_display_container: 3 +2024-04-25 15:37:49,958: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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...) +2024-04-25 15:37:49,959:INFO:create_model() successfully completed...................................... +2024-04-25 15:39:24,589:INFO:Initializing evaluate_model() +2024-04-25 15:39:24,590:INFO:evaluate_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) +2024-04-25 15:39:25,202:INFO:Initializing plot_model() +2024-04-25 15:39:25,203:INFO:plot_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), plot=pipeline, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-04-25 15:39:25,204:INFO:Checking exceptions +2024-04-25 15:39:25,491:INFO:Preloading libraries +2024-04-25 15:39:25,883:INFO:Copying training dataset +2024-04-25 15:39:25,889:INFO:Plot type: pipeline +2024-04-25 15:39:32,043:INFO:Visual Rendered Successfully +2024-04-25 15:39:32,991:INFO:plot_model() successfully completed...................................... +2024-04-25 15:40:06,207:INFO:Initializing plot_model() +2024-04-25 15:40:06,208:INFO:plot_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), plot=learning, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-04-25 15:40:06,209:INFO:Checking exceptions +2024-04-25 15:40:06,240:INFO:Preloading libraries +2024-04-25 15:40:06,337:INFO:Copying training dataset +2024-04-25 15:40:06,339:INFO:Plot type: learning +2024-04-25 15:40:06,916:INFO:Fitting Model +2024-04-25 15:43:10,785:INFO:Visual Rendered Successfully +2024-04-25 15:43:11,345:INFO:plot_model() successfully completed...................................... +2024-04-25 15:43:11,727:INFO:Initializing tune_model() +2024-04-25 15:43:11,728:INFO:tune_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=optuna, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-04-25 15:43:11,729:INFO:Checking exceptions +2024-04-25 15:43:11,732:INFO:Soft dependency imported: optuna: 3.6.1 +2024-04-25 15:43:18,258:INFO:Copying training dataset +2024-04-25 15:43:18,389:INFO:Checking base model +2024-04-25 15:43:18,390:INFO:Base model : Extreme Gradient Boosting +2024-04-25 15:43:18,542:INFO:Declaring metric variables +2024-04-25 15:43:18,754:INFO:Defining Hyperparameters +2024-04-25 15:43:21,769:INFO:Tuning with n_jobs=-1 +2024-04-25 15:43:22,147:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\optuna\samplers\_tpe\sampler.py:319: ExperimentalWarning: ``multivariate`` option is an experimental feature. The interface can change in the future. + warnings.warn( + +2024-04-25 15:43:22,152:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\optuna\samplers\_tpe\sampler.py:338: ExperimentalWarning: ``constant_liar`` option is an experimental feature. The interface can change in the future. + warnings.warn( + +2024-04-25 15:43:22,296:INFO:Initializing optuna.integration.OptunaSearchCV +2024-04-25 15:43:50,914:INFO:Initializing plot_model() +2024-04-25 15:43:50,930:INFO:plot_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), plot=pipeline, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-04-25 15:43:50,932:INFO:Checking exceptions +2024-04-25 15:43:51,227:INFO:Preloading libraries +2024-04-25 15:43:51,514:INFO:Copying training dataset +2024-04-25 15:43:51,515:INFO:Plot type: pipeline +2024-04-25 15:43:56,604:INFO:Visual Rendered Successfully +2024-04-25 15:43:57,739:INFO:plot_model() successfully completed...................................... +2024-04-25 15:48:38,617:INFO:Initializing tune_model() +2024-04-25 15:48:38,618:INFO:tune_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=optuna-integration, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-04-25 15:48:38,619:INFO:Checking exceptions +2024-04-25 15:48:57,814:INFO:Initializing tune_model() +2024-04-25 15:48:57,815:INFO:tune_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=optuna, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-04-25 15:48:57,816:INFO:Checking exceptions +2024-04-25 15:48:57,816:INFO:Soft dependency imported: optuna: 3.6.1 +2024-04-25 15:48:58,198:INFO:Copying training dataset +2024-04-25 15:48:58,554:INFO:Checking base model +2024-04-25 15:48:58,556:INFO:Base model : Extreme Gradient Boosting +2024-04-25 15:48:58,992:INFO:Declaring metric variables +2024-04-25 15:48:59,203:INFO:Defining Hyperparameters +2024-04-25 15:49:03,604:INFO:Tuning with n_jobs=-1 +2024-04-25 15:49:03,610:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\optuna\samplers\_tpe\sampler.py:319: ExperimentalWarning: ``multivariate`` option is an experimental feature. The interface can change in the future. + warnings.warn( + +2024-04-25 15:49:03,611:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\optuna\samplers\_tpe\sampler.py:338: ExperimentalWarning: ``constant_liar`` option is an experimental feature. The interface can change in the future. + warnings.warn( + +2024-04-25 15:49:03,615:INFO:Initializing optuna.integration.OptunaSearchCV +2024-04-25 15:49:04,403:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:2458: ExperimentalWarning: OptunaSearchCV is experimental (supported from v0.17.0). The interface can change in the future. + model_grid = optuna.integration.OptunaSearchCV( # type: ignore + +2024-04-25 15:55:32,066:INFO:best_params: {'actual_estimator__learning_rate': 0.1093036817502172, 'actual_estimator__n_estimators': 206, 'actual_estimator__subsample': 0.814055187993542, 'actual_estimator__max_depth': 6, 'actual_estimator__colsample_bytree': 0.5825468346602922, 'actual_estimator__min_child_weight': 1, 'actual_estimator__reg_alpha': 4.4817368251187986e-08, 'actual_estimator__reg_lambda': 6.402610104211578e-08, 'actual_estimator__scale_pos_weight': 19.172954611372305} +2024-04-25 15:55:32,115:INFO:Hyperparameter search completed +2024-04-25 15:55:32,119:INFO:SubProcess create_model() called ================================== +2024-04-25 15:55:32,159:INFO:Initializing create_model() +2024-04-25 15:55:32,161:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'learning_rate': 0.1093036817502172, 'n_estimators': 206, 'subsample': 0.814055187993542, 'max_depth': 6, 'colsample_bytree': 0.5825468346602922, 'min_child_weight': 1, 'reg_alpha': 4.4817368251187986e-08, 'reg_lambda': 6.402610104211578e-08, 'scale_pos_weight': 19.172954611372305}) +2024-04-25 15:55:32,162:INFO:Checking exceptions +2024-04-25 15:55:32,164:INFO:Importing libraries +2024-04-25 15:55:32,166:INFO:Copying training dataset +2024-04-25 15:55:32,559:INFO:Defining folds +2024-04-25 15:55:32,568:INFO:Declaring metric variables +2024-04-25 15:55:32,768:INFO:Importing untrained model +2024-04-25 15:55:32,773:INFO:Declaring custom model +2024-04-25 15:55:33,095:INFO:Extreme Gradient Boosting Imported successfully +2024-04-25 15:55:33,453:INFO:Starting cross validation +2024-04-25 15:55:33,635:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 16:04:21,739:INFO:Calculating mean and std +2024-04-25 16:04:21,811:INFO:Creating metrics dataframe +2024-04-25 16:04:22,304:INFO:Finalizing model +2024-04-25 16:04:45,371:INFO:Uploading results into container +2024-04-25 16:04:45,492:INFO:Uploading model into container now +2024-04-25 16:04:45,503:INFO:_master_model_container: 21 +2024-04-25 16:04:45,605:INFO:_display_container: 4 +2024-04-25 16:04:45,762:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, + colsample_bytree=0.5825468346602922, 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=0.1093036817502172, + 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=206, n_jobs=-1, + num_parallel_tree=1, predictor='auto', random_state=8066, + reg_alpha=4.4817368251187986e-08, reg_lambda=6.402610104211578e-08, ...) +2024-04-25 16:04:45,763:INFO:create_model() successfully completed...................................... +2024-04-25 16:04:49,591:INFO:SubProcess create_model() end ================================== +2024-04-25 16:04:49,592:INFO:choose_better activated +2024-04-25 16:04:49,939:INFO:SubProcess create_model() called ================================== +2024-04-25 16:04:50,512:INFO:Initializing create_model() +2024-04-25 16:04:50,513:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 16:04:50,514:INFO:Checking exceptions +2024-04-25 16:04:50,689:INFO:Importing libraries +2024-04-25 16:04:50,690:INFO:Copying training dataset +2024-04-25 16:04:50,913:INFO:Defining folds +2024-04-25 16:04:50,914:INFO:Declaring metric variables +2024-04-25 16:04:50,916:INFO:Importing untrained model +2024-04-25 16:04:50,916:INFO:Declaring custom model +2024-04-25 16:04:50,967:INFO:Extreme Gradient Boosting Imported successfully +2024-04-25 16:04:50,970:INFO:Starting cross validation +2024-04-25 16:04:50,997:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 16:06:24,751:INFO:Calculating mean and std +2024-04-25 16:06:24,765:INFO:Creating metrics dataframe +2024-04-25 16:06:24,850:INFO:Finalizing model +2024-04-25 16:06:35,906:INFO:Uploading results into container +2024-04-25 16:06:35,915:INFO:Uploading model into container now +2024-04-25 16:06:35,921:INFO:_master_model_container: 22 +2024-04-25 16:06:35,925:INFO:_display_container: 5 +2024-04-25 16:06:35,988: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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...) +2024-04-25 16:06:35,989:INFO:create_model() successfully completed...................................... +2024-04-25 16:06:38,675:INFO:SubProcess create_model() end ================================== +2024-04-25 16:06:38,752: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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...) result for R2 is 0.9766 +2024-04-25 16:06:38,839:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, + colsample_bytree=0.5825468346602922, 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=0.1093036817502172, + 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=206, n_jobs=-1, + num_parallel_tree=1, predictor='auto', random_state=8066, + reg_alpha=4.4817368251187986e-08, reg_lambda=6.402610104211578e-08, ...) result for R2 is 0.9715 +2024-04-25 16:06:38,904: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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...) is best model +2024-04-25 16:06:38,908:INFO:choose_better completed +2024-04-25 16:06:38,915:INFO:Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one). +2024-04-25 16:06:39,240:INFO:_master_model_container: 22 +2024-04-25 16:06:39,243:INFO:_display_container: 4 +2024-04-25 16:06:39,339: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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...) +2024-04-25 16:06:39,340:INFO:tune_model() successfully completed...................................... +2024-04-25 16:36:17,518:INFO:Initializing predict_model() +2024-04-25 16:36:17,518:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD4E193A0>) +2024-04-25 16:36:17,518:INFO:Checking exceptions +2024-04-25 16:36:17,518:INFO:Preloading libraries +2024-04-25 16:36:17,527:INFO:Set up data. +2024-04-25 16:36:17,542:INFO:Set up index. +2024-04-25 16:36:39,416:INFO:Initializing predict_model() +2024-04-25 16:36:39,416:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD4E253A0>) +2024-04-25 16:36:39,416:INFO:Checking exceptions +2024-04-25 16:36:39,416:INFO:Preloading libraries +2024-04-25 16:36:39,423:INFO:Set up data. +2024-04-25 16:36:39,436:INFO:Set up index. +2024-04-25 16:37:16,752:INFO:Initializing predict_model() +2024-04-25 16:37:16,752:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD76DB880>) +2024-04-25 16:37:16,752:INFO:Checking exceptions +2024-04-25 16:37:16,752:INFO:Preloading libraries +2024-04-25 16:37:16,757:INFO:Set up data. +2024-04-25 16:37:16,769:INFO:Set up index. +2024-04-25 16:38:26,883:INFO:Initializing predict_model() +2024-04-25 16:38:26,883:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7770720>) +2024-04-25 16:38:26,883:INFO:Checking exceptions +2024-04-25 16:38:26,883:INFO:Preloading libraries +2024-04-25 16:38:26,888:INFO:Set up data. +2024-04-25 16:38:26,899:INFO:Set up index. +2024-04-25 16:38:49,523:INFO:Initializing predict_model() +2024-04-25 16:38:49,523:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7CF7F60>) +2024-04-25 16:38:49,523:INFO:Checking exceptions +2024-04-25 16:38:49,524:INFO:Preloading libraries +2024-04-25 16:38:49,528:INFO:Set up data. +2024-04-25 16:38:49,544:INFO:Set up index. +2024-04-25 16:40:08,934:INFO:Initializing predict_model() +2024-04-25 16:40:08,935:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7E696C0>) +2024-04-25 16:40:08,935:INFO:Checking exceptions +2024-04-25 16:40:08,935:INFO:Preloading libraries +2024-04-25 16:40:08,943:INFO:Set up data. +2024-04-25 16:40:08,957:INFO:Set up index. +2024-04-25 16:41:18,472:INFO:Initializing predict_model() +2024-04-25 16:41:18,474:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7EFA5C0>) +2024-04-25 16:41:18,474:INFO:Checking exceptions +2024-04-25 16:41:18,475:INFO:Preloading libraries +2024-04-25 16:41:18,892:INFO:Set up data. +2024-04-25 16:41:19,211:INFO:Set up index. +2024-04-25 16:44:33,221:INFO:Initializing predict_model() +2024-04-25 16:44:33,224:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7FBCCC0>) +2024-04-25 16:44:33,224:INFO:Checking exceptions +2024-04-25 16:44:33,274:INFO:Preloading libraries +2024-04-25 16:44:33,328:INFO:Set up data. +2024-04-25 16:44:33,573:INFO:Set up index. +2024-04-25 16:44:38,522:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\pipeline.py:111: UserWarning: Persisting input arguments took 0.64s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + X, y = pipeline._memory_transform(transformer, X, y) + +2024-04-25 16:44:39,793:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\pipeline.py:111: UserWarning: Persisting input arguments took 0.81s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + X, y = pipeline._memory_transform(transformer, X, y) + +2024-04-25 16:44:41,651:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\pipeline.py:111: UserWarning: Persisting input arguments took 1.18s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + X, y = pipeline._memory_transform(transformer, X, y) + +2024-04-25 16:44:44,019:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\pipeline.py:287: UserWarning: Persisting input arguments took 2.10s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + X, y = self._memory_full_transform( + +2024-04-25 16:46:23,786:INFO:Initializing predict_model() +2024-04-25 16:46:23,787:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7E68A40>) +2024-04-25 16:46:23,788:INFO:Checking exceptions +2024-04-25 16:46:23,788:INFO:Preloading libraries +2024-04-25 16:46:23,808:INFO:Set up data. +2024-04-25 16:46:24,132:INFO:Set up index. +2024-04-25 16:47:44,445:INFO:Initializing predict_model() +2024-04-25 16:47:44,445:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7E6BA60>) +2024-04-25 16:47:44,446:INFO:Checking exceptions +2024-04-25 16:47:44,447:INFO:Preloading libraries +2024-04-25 16:47:44,485:INFO:Set up data. +2024-04-25 16:47:44,647:INFO:Set up index. +2024-04-25 16:48:49,132:INFO:Initializing predict_model() +2024-04-25 16:48:49,152:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7E6A700>) +2024-04-25 16:48:49,152:INFO:Checking exceptions +2024-04-25 16:48:49,153:INFO:Preloading libraries +2024-04-25 16:48:49,185:INFO:Set up data. +2024-04-25 16:48:49,349:INFO:Set up index. +2024-04-25 16:50:02,578:INFO:Initializing predict_model() +2024-04-25 16:50:02,579:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7E6B1A0>) +2024-04-25 16:50:02,580:INFO:Checking exceptions +2024-04-25 16:50:02,580:INFO:Preloading libraries +2024-04-25 16:50:02,605:INFO:Set up data. +2024-04-25 16:50:02,640:INFO:Set up index. +2024-04-25 16:50:30,255:INFO:Initializing predict_model() +2024-04-25 16:50:30,256:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7E69E40>) +2024-04-25 16:50:30,256:INFO:Checking exceptions +2024-04-25 16:50:30,257:INFO:Preloading libraries +2024-04-25 16:50:30,288:INFO:Set up data. +2024-04-25 16:50:30,413:INFO:Set up index. +2024-04-25 16:52:11,639:INFO:Initializing predict_model() +2024-04-25 16:52:11,639:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7E687C0>) +2024-04-25 16:52:11,640:INFO:Checking exceptions +2024-04-25 16:52:11,642:INFO:Preloading libraries +2024-04-25 16:52:11,689:INFO:Set up data. +2024-04-25 16:52:11,740:INFO:Set up index. +2024-04-25 16:52:38,857:INFO:Initializing predict_model() +2024-04-25 16:52:38,858:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7E6A200>) +2024-04-25 16:52:38,864:INFO:Checking exceptions +2024-04-25 16:52:38,865:INFO:Preloading libraries +2024-04-25 16:52:38,890:INFO:Set up data. +2024-04-25 16:52:39,228:INFO:Set up index. +2024-04-25 16:53:05,912:INFO:Initializing predict_model() +2024-04-25 16:53:05,913:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7E6BC40>) +2024-04-25 16:53:05,914:INFO:Checking exceptions +2024-04-25 16:53:05,917:INFO:Preloading libraries +2024-04-25 16:53:05,962:INFO:Set up data. +2024-04-25 16:53:06,370:INFO:Set up index. +2024-04-25 16:54:53,672:INFO:Initializing predict_model() +2024-04-25 16:54:53,674:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7E69EE0>) +2024-04-25 16:54:53,675:INFO:Checking exceptions +2024-04-25 16:54:53,676:INFO:Preloading libraries +2024-04-25 16:54:53,784:INFO:Set up data. +2024-04-25 16:54:54,064:INFO:Set up index. +2024-04-25 16:55:24,881:INFO:Initializing predict_model() +2024-04-25 16:55:24,883:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7E6B880>) +2024-04-25 16:55:24,884:INFO:Checking exceptions +2024-04-25 16:55:24,885:INFO:Preloading libraries +2024-04-25 16:55:25,052:INFO:Set up data. +2024-04-25 16:55:25,251:INFO:Set up index. +2024-04-25 16:55:39,792:INFO:Initializing predict_model() +2024-04-25 16:55:39,793:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7E69EE0>) +2024-04-25 16:55:39,848:INFO:Checking exceptions +2024-04-25 16:55:39,848:INFO:Preloading libraries +2024-04-25 16:55:39,965:INFO:Set up data. +2024-04-25 16:55:40,144:INFO:Set up index. +2024-04-25 17:35:39,583:INFO:Initializing predict_model() +2024-04-25 17:35:39,583:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7E6A020>) +2024-04-25 17:35:39,584:INFO:Checking exceptions +2024-04-25 17:35:39,584:INFO:Preloading libraries +2024-04-25 17:35:39,588:INFO:Set up data. +2024-04-25 17:35:39,597:INFO:Set up index. +2024-04-25 23:07:43,867:INFO:Initializing predict_model() +2024-04-25 23:07:43,869:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8066, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000021DD7E68720>) +2024-04-25 23:07:43,869:INFO:Checking exceptions +2024-04-25 23:07:43,869:INFO:Preloading libraries +2024-04-25 23:07:43,886:INFO:Set up data. +2024-04-25 23:07:43,898:INFO:Set up index. +2024-04-25 23:28:06,112:WARNING:C:\Users\Jason\AppData\Local\Temp\ipykernel_2356\1099768027.py:1: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing. + data['Date'] = pd.to_datetime(data['Date']) + +2024-04-25 23:32:21,100:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-04-25 23:32:21,101:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-04-25 23:32:21,104:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-04-25 23:32:21,105:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-04-25 23:32:37,775:INFO:PyCaret RegressionExperiment +2024-04-25 23:32:37,776:INFO:Logging name: reg-default-name +2024-04-25 23:32:37,777:INFO:ML Usecase: MLUsecase.REGRESSION +2024-04-25 23:32:37,778:INFO:version 3.3.0 +2024-04-25 23:32:37,779:INFO:Initializing setup() +2024-04-25 23:32:37,779:INFO:self.USI: fbc6 +2024-04-25 23:32:37,780:INFO:self._variable_keys: {'y', 'gpu_n_jobs_param', 'html_param', 'fold_shuffle_param', 'idx', 'target_param', 'X_test', 'logging_param', 'exp_id', '_ml_usecase', 'log_plots_param', 'fold_generator', 'transform_target_param', 'gpu_param', 'data', 'exp_name_log', 'X', 'seed', 'memory', 'X_train', 'y_test', 'y_train', 'USI', '_available_plots', 'n_jobs_param', 'fold_groups_param', 'pipeline'} +2024-04-25 23:32:37,781:INFO:Checking environment +2024-04-25 23:32:37,782:INFO:python_version: 3.11.0 +2024-04-25 23:32:37,782:INFO:python_build: ('main', 'Oct 24 2022 18:26:48') +2024-04-25 23:32:37,783:INFO:machine: AMD64 +2024-04-25 23:32:37,786:INFO:platform: Windows-10-10.0.22000-SP0 +2024-04-25 23:32:37,815:INFO:Memory: svmem(total=8467492864, available=2029891584, percent=76.0, used=6437601280, free=2029891584) +2024-04-25 23:32:37,816:INFO:Physical Core: 2 +2024-04-25 23:32:37,817:INFO:Logical Core: 4 +2024-04-25 23:32:37,817:INFO:Checking libraries +2024-04-25 23:32:37,818:INFO:System: +2024-04-25 23:32:37,819:INFO: python: 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] +2024-04-25 23:32:37,996:INFO:executable: c:\Users\Jason\Documents\DataB3\.venv\Scripts\python.exe +2024-04-25 23:32:37,997:INFO: machine: Windows-10-10.0.22000-SP0 +2024-04-25 23:32:37,997:INFO:PyCaret required dependencies: +2024-04-25 23:32:39,790:INFO: pip: 24.0 +2024-04-25 23:32:39,791:INFO: setuptools: 65.5.0 +2024-04-25 23:32:39,792:INFO: pycaret: 3.3.0 +2024-04-25 23:32:39,793:INFO: IPython: 8.23.0 +2024-04-25 23:32:39,794:INFO: ipywidgets: 8.1.2 +2024-04-25 23:32:39,795:INFO: tqdm: 4.66.2 +2024-04-25 23:32:39,795:INFO: numpy: 1.24.4 +2024-04-25 23:32:39,796:INFO: pandas: 1.5.3 +2024-04-25 23:32:39,796:INFO: jinja2: 3.1.3 +2024-04-25 23:32:39,799:INFO: scipy: 1.11.4 +2024-04-25 23:32:39,800:INFO: joblib: 1.3.2 +2024-04-25 23:32:39,801:INFO: sklearn: 1.4.1.post1 +2024-04-25 23:32:39,801:INFO: pyod: 1.1.3 +2024-04-25 23:32:39,803:INFO: imblearn: 0.12.2 +2024-04-25 23:32:39,803:INFO: category_encoders: 2.6.3 +2024-04-25 23:32:39,804:INFO: lightgbm: 4.3.0 +2024-04-25 23:32:39,805:INFO: numba: 0.59.1 +2024-04-25 23:32:39,805:INFO: requests: 2.31.0 +2024-04-25 23:32:39,806:INFO: matplotlib: 3.8.3 +2024-04-25 23:32:39,807:INFO: scikitplot: 0.3.7 +2024-04-25 23:32:39,820:INFO: yellowbrick: 1.5 +2024-04-25 23:32:39,821:INFO: plotly: 5.20.0 +2024-04-25 23:32:39,821:INFO: plotly-resampler: Not installed +2024-04-25 23:32:39,882:INFO: kaleido: 0.2.1 +2024-04-25 23:32:39,887:INFO: schemdraw: 0.15 +2024-04-25 23:32:39,889:INFO: statsmodels: 0.14.1 +2024-04-25 23:32:39,923:INFO: sktime: 0.28.0 +2024-04-25 23:32:39,926:INFO: tbats: 1.1.3 +2024-04-25 23:32:39,927:INFO: pmdarima: 2.0.4 +2024-04-25 23:32:39,928:INFO: psutil: 5.9.8 +2024-04-25 23:32:39,959:INFO: markupsafe: 2.1.5 +2024-04-25 23:32:39,961:INFO: pickle5: Not installed +2024-04-25 23:32:39,962:INFO: cloudpickle: 3.0.0 +2024-04-25 23:32:39,962:INFO: deprecation: 2.1.0 +2024-04-25 23:32:39,963:INFO: xxhash: 3.4.1 +2024-04-25 23:32:39,963:INFO: wurlitzer: Not installed +2024-04-25 23:32:39,964:INFO:PyCaret optional dependencies: +2024-04-25 23:32:42,338:INFO: shap: Not installed +2024-04-25 23:32:42,339:INFO: interpret: Not installed +2024-04-25 23:32:42,352:INFO: umap: Not installed +2024-04-25 23:32:42,353:INFO: ydata_profiling: 4.7.0 +2024-04-25 23:32:42,356:INFO: explainerdashboard: Not installed +2024-04-25 23:32:42,357:INFO: autoviz: Not installed +2024-04-25 23:32:42,360:INFO: fairlearn: Not installed +2024-04-25 23:32:42,478:INFO: deepchecks: Not installed +2024-04-25 23:32:42,479:INFO: xgboost: 1.6.2 +2024-04-25 23:32:42,479:INFO: catboost: Not installed +2024-04-25 23:32:42,480:INFO: kmodes: Not installed +2024-04-25 23:32:42,482:INFO: mlxtend: Not installed +2024-04-25 23:32:42,482:INFO: statsforecast: Not installed +2024-04-25 23:32:42,483:INFO: tune_sklearn: Not installed +2024-04-25 23:32:42,485:INFO: ray: Not installed +2024-04-25 23:32:42,485:INFO: hyperopt: Not installed +2024-04-25 23:32:42,488:INFO: optuna: 3.6.1 +2024-04-25 23:32:42,489:INFO: skopt: Not installed +2024-04-25 23:32:42,489:INFO: mlflow: Not installed +2024-04-25 23:32:42,490:INFO: gradio: Not installed +2024-04-25 23:32:42,490:INFO: fastapi: Not installed +2024-04-25 23:32:42,493:INFO: uvicorn: Not installed +2024-04-25 23:32:42,497:INFO: m2cgen: Not installed +2024-04-25 23:32:42,498:INFO: evidently: Not installed +2024-04-25 23:32:42,704:INFO: fugue: Not installed +2024-04-25 23:32:42,704:INFO: streamlit: 1.33.0 +2024-04-25 23:32:42,717:INFO: prophet: 1.1.5 +2024-04-25 23:32:42,718:INFO:None +2024-04-25 23:32:42,736:INFO:Set up data. +2024-04-25 23:32:43,197:INFO:Set up folding strategy. +2024-04-25 23:32:43,199:INFO:Set up train/test split. +2024-04-25 23:32:43,676:INFO:Set up index. +2024-04-25 23:32:43,681:INFO:Assigning column types. +2024-04-25 23:32:44,226:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-04-25 23:32:44,229:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-04-25 23:32:44,928:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-04-25 23:32:45,534:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-04-25 23:32:50,415:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 23:32:52,914:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 23:32:52,951:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 23:32:56,001:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 23:32:56,005:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-04-25 23:32:56,269:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-04-25 23:32:56,469:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-04-25 23:32:59,858:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 23:33:03,499:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 23:33:03,526:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 23:33:03,614:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 23:33:03,623:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-04-25 23:33:03,823:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-04-25 23:33:04,031:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-04-25 23:33:07,634:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 23:33:12,014:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 23:33:12,032:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 23:33:12,118:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 23:33:12,393:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-04-25 23:33:12,673:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-04-25 23:33:15,627:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 23:33:18,616:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 23:33:18,623:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 23:33:18,760:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 23:33:18,768:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-04-25 23:33:19,168:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-04-25 23:33:23,173:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 23:33:26,876:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 23:33:26,968:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 23:33:27,124:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 23:33:28,470:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-04-25 23:33:46,776:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 23:33:57,514:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 23:33:57,525:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 23:33:58,140:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 23:33:58,145:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-04-25 23:34:16,883:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 23:34:28,775:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 23:34:29,360:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 23:34:30,215:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 23:34:46,372:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 23:34:51,189:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-04-25 23:34:51,347:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 23:34:52,245:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 23:34:52,252:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-04-25 23:35:02,137:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 23:35:07,884:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 23:35:08,171:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 23:35:14,444:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-04-25 23:35:17,523:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 23:35:17,953:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 23:35:17,962:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-04-25 23:35:26,552:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 23:35:26,670:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 23:35:33,110:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 23:35:33,228:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 23:35:33,426:INFO:Preparing preprocessing pipeline... +2024-04-25 23:35:33,427:INFO:Set up date feature engineering. +2024-04-25 23:35:33,427:INFO:Set up simple imputation. +2024-04-25 23:35:33,428:INFO:Set up feature normalization. +2024-04-25 23:35:35,420:INFO:Finished creating preprocessing pipeline. +2024-04-25 23:35:35,515:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\Jason\AppData\Local\Temp\joblib), + steps=[('date_feature_extractor', + TransformerWrapper(include=['Date'], + transformer=ExtractDateTimeFeatures())), + ('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWrapper(transformer=MinMaxScaler()))]) +2024-04-25 23:35:35,516:INFO:Creating final display dataframe. +2024-04-25 23:35:37,983:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\pipeline.py:111: UserWarning: Persisting input arguments took 0.82s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + X, y = pipeline._memory_transform(transformer, X, y) + +2024-04-25 23:35:39,458:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\pipeline.py:111: UserWarning: Persisting input arguments took 0.57s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + X, y = pipeline._memory_transform(transformer, X, y) + +2024-04-25 23:35:41,579:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\pipeline.py:287: UserWarning: Persisting input arguments took 0.77s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + X, y = self._memory_full_transform( + +2024-04-25 23:35:42,191:INFO:Setup _display_container: Description Value +0 Session id 1556 +1 Target Daily_Sales +2 Target type Regression +3 Original data shape (6435, 8) +4 Transformed data shape (6435, 10) +5 Transformed train set shape (4504, 10) +6 Transformed test set shape (1931, 10) +7 Numeric features 6 +8 Date features 1 +9 Preprocess True +10 Imputation type simple +11 Numeric imputation mean +12 Categorical imputation mode +13 Normalize True +14 Normalize method minmax +15 Fold Generator KFold +16 Fold Number 10 +17 CPU Jobs -1 +18 Use GPU False +19 Log Experiment False +20 Experiment Name reg-default-name +21 USI fbc6 +2024-04-25 23:35:49,275:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 23:35:49,373:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 23:35:54,099:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-04-25 23:35:54,158:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-04-25 23:35:54,298:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour. + warnings.warn( + +2024-04-25 23:35:54,300:INFO:setup() successfully completed in 197.17s............... +2024-04-25 23:37:46,252:INFO:Initializing compare_models() +2024-04-25 23:37:46,253:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-04-25 23:37:46,254:INFO:Checking exceptions +2024-04-25 23:37:46,274:INFO:Preparing display monitor +2024-04-25 23:37:47,028:INFO:Initializing Linear Regression +2024-04-25 23:37:47,054:INFO:Total runtime is 1.6558170318603516e-05 minutes +2024-04-25 23:37:47,459:INFO:SubProcess create_model() called ================================== +2024-04-25 23:37:47,463:INFO:Initializing create_model() +2024-04-25 23:37:47,463:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:37:47,464:INFO:Checking exceptions +2024-04-25 23:37:47,465:INFO:Importing libraries +2024-04-25 23:37:47,466:INFO:Copying training dataset +2024-04-25 23:37:47,992:INFO:Defining folds +2024-04-25 23:37:47,994:INFO:Declaring metric variables +2024-04-25 23:37:48,180:INFO:Importing untrained model +2024-04-25 23:37:48,309:INFO:Linear Regression Imported successfully +2024-04-25 23:37:48,544:INFO:Starting cross validation +2024-04-25 23:37:49,004:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:39:08,233:INFO:Calculating mean and std +2024-04-25 23:39:08,251:INFO:Creating metrics dataframe +2024-04-25 23:39:08,573:INFO:Uploading results into container +2024-04-25 23:39:08,689:INFO:Uploading model into container now +2024-04-25 23:39:08,694:INFO:_master_model_container: 1 +2024-04-25 23:39:08,696:INFO:_display_container: 2 +2024-04-25 23:39:08,699:INFO:LinearRegression(n_jobs=-1) +2024-04-25 23:39:08,699:INFO:create_model() successfully completed...................................... +2024-04-25 23:39:09,699:INFO:SubProcess create_model() end ================================== +2024-04-25 23:39:09,700:INFO:Creating metrics dataframe +2024-04-25 23:39:09,862:INFO:Initializing Lasso Regression +2024-04-25 23:39:09,864:INFO:Total runtime is 1.3805968840916951 minutes +2024-04-25 23:39:09,915:INFO:SubProcess create_model() called ================================== +2024-04-25 23:39:09,922:INFO:Initializing create_model() +2024-04-25 23:39:09,925:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:39:09,926:INFO:Checking exceptions +2024-04-25 23:39:09,926:INFO:Importing libraries +2024-04-25 23:39:09,927:INFO:Copying training dataset +2024-04-25 23:39:10,157:INFO:Defining folds +2024-04-25 23:39:10,158:INFO:Declaring metric variables +2024-04-25 23:39:10,296:INFO:Importing untrained model +2024-04-25 23:39:10,410:INFO:Lasso Regression Imported successfully +2024-04-25 23:39:10,666:INFO:Starting cross validation +2024-04-25 23:39:10,681:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:39:14,433:INFO:Calculating mean and std +2024-04-25 23:39:14,445:INFO:Creating metrics dataframe +2024-04-25 23:39:14,528:INFO:Uploading results into container +2024-04-25 23:39:14,533:INFO:Uploading model into container now +2024-04-25 23:39:14,537:INFO:_master_model_container: 2 +2024-04-25 23:39:14,538:INFO:_display_container: 2 +2024-04-25 23:39:14,541:INFO:Lasso(random_state=1556) +2024-04-25 23:39:14,542:INFO:create_model() successfully completed...................................... +2024-04-25 23:39:14,955:INFO:SubProcess create_model() end ================================== +2024-04-25 23:39:14,957:INFO:Creating metrics dataframe +2024-04-25 23:39:15,160:INFO:Initializing Ridge Regression +2024-04-25 23:39:15,161:INFO:Total runtime is 1.4688812454541524 minutes +2024-04-25 23:39:15,318:INFO:SubProcess create_model() called ================================== +2024-04-25 23:39:15,320:INFO:Initializing create_model() +2024-04-25 23:39:15,321:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:39:15,321:INFO:Checking exceptions +2024-04-25 23:39:15,322:INFO:Importing libraries +2024-04-25 23:39:15,323:INFO:Copying training dataset +2024-04-25 23:39:15,544:INFO:Defining folds +2024-04-25 23:39:15,547:INFO:Declaring metric variables +2024-04-25 23:39:15,638:INFO:Importing untrained model +2024-04-25 23:39:15,707:INFO:Ridge Regression Imported successfully +2024-04-25 23:39:15,923:INFO:Starting cross validation +2024-04-25 23:39:15,972:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:39:21,629:INFO:Calculating mean and std +2024-04-25 23:39:21,641:INFO:Creating metrics dataframe +2024-04-25 23:39:21,732:INFO:Uploading results into container +2024-04-25 23:39:21,739:INFO:Uploading model into container now +2024-04-25 23:39:21,745:INFO:_master_model_container: 3 +2024-04-25 23:39:21,747:INFO:_display_container: 2 +2024-04-25 23:39:21,751:INFO:Ridge(random_state=1556) +2024-04-25 23:39:21,752:INFO:create_model() successfully completed...................................... +2024-04-25 23:39:22,185:INFO:SubProcess create_model() end ================================== +2024-04-25 23:39:22,187:INFO:Creating metrics dataframe +2024-04-25 23:39:22,332:INFO:Initializing Elastic Net +2024-04-25 23:39:22,333:INFO:Total runtime is 1.5884198149045308 minutes +2024-04-25 23:39:22,397:INFO:SubProcess create_model() called ================================== +2024-04-25 23:39:22,405:INFO:Initializing create_model() +2024-04-25 23:39:22,406:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:39:22,407:INFO:Checking exceptions +2024-04-25 23:39:22,410:INFO:Importing libraries +2024-04-25 23:39:22,411:INFO:Copying training dataset +2024-04-25 23:39:22,593:INFO:Defining folds +2024-04-25 23:39:22,594:INFO:Declaring metric variables +2024-04-25 23:39:22,636:INFO:Importing untrained model +2024-04-25 23:39:22,743:INFO:Elastic Net Imported successfully +2024-04-25 23:39:22,903:INFO:Starting cross validation +2024-04-25 23:39:22,923:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:39:25,897:INFO:Calculating mean and std +2024-04-25 23:39:25,909:INFO:Creating metrics dataframe +2024-04-25 23:39:25,985:INFO:Uploading results into container +2024-04-25 23:39:25,990:INFO:Uploading model into container now +2024-04-25 23:39:25,994:INFO:_master_model_container: 4 +2024-04-25 23:39:25,995:INFO:_display_container: 2 +2024-04-25 23:39:25,997:INFO:ElasticNet(random_state=1556) +2024-04-25 23:39:25,998:INFO:create_model() successfully completed...................................... +2024-04-25 23:39:26,356:INFO:SubProcess create_model() end ================================== +2024-04-25 23:39:26,357:INFO:Creating metrics dataframe +2024-04-25 23:39:26,449:INFO:Initializing Least Angle Regression +2024-04-25 23:39:26,450:INFO:Total runtime is 1.6570392847061157 minutes +2024-04-25 23:39:26,481:INFO:SubProcess create_model() called ================================== +2024-04-25 23:39:26,501:INFO:Initializing create_model() +2024-04-25 23:39:26,506:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:39:26,506:INFO:Checking exceptions +2024-04-25 23:39:26,507:INFO:Importing libraries +2024-04-25 23:39:26,508:INFO:Copying training dataset +2024-04-25 23:39:26,694:INFO:Defining folds +2024-04-25 23:39:26,696:INFO:Declaring metric variables +2024-04-25 23:39:26,862:INFO:Importing untrained model +2024-04-25 23:39:26,948:INFO:Least Angle Regression Imported successfully +2024-04-25 23:39:27,154:INFO:Starting cross validation +2024-04-25 23:39:27,171:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:39:30,414:INFO:Calculating mean and std +2024-04-25 23:39:30,430:INFO:Creating metrics dataframe +2024-04-25 23:39:30,516:INFO:Uploading results into container +2024-04-25 23:39:30,524:INFO:Uploading model into container now +2024-04-25 23:39:30,529:INFO:_master_model_container: 5 +2024-04-25 23:39:30,530:INFO:_display_container: 2 +2024-04-25 23:39:30,532:INFO:Lars(random_state=1556) +2024-04-25 23:39:30,532:INFO:create_model() successfully completed...................................... +2024-04-25 23:39:30,980:INFO:SubProcess create_model() end ================================== +2024-04-25 23:39:30,981:INFO:Creating metrics dataframe +2024-04-25 23:39:31,072:INFO:Initializing Lasso Least Angle Regression +2024-04-25 23:39:31,073:INFO:Total runtime is 1.7340841054916383 minutes +2024-04-25 23:39:31,195:INFO:SubProcess create_model() called ================================== +2024-04-25 23:39:31,198:INFO:Initializing create_model() +2024-04-25 23:39:31,199:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:39:31,206:INFO:Checking exceptions +2024-04-25 23:39:31,208:INFO:Importing libraries +2024-04-25 23:39:31,209:INFO:Copying training dataset +2024-04-25 23:39:31,333:INFO:Defining folds +2024-04-25 23:39:31,338:INFO:Declaring metric variables +2024-04-25 23:39:31,390:INFO:Importing untrained model +2024-04-25 23:39:31,494:INFO:Lasso Least Angle Regression Imported successfully +2024-04-25 23:39:31,618:INFO:Starting cross validation +2024-04-25 23:39:31,632:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:39:37,276:INFO:Calculating mean and std +2024-04-25 23:39:37,299:INFO:Creating metrics dataframe +2024-04-25 23:39:37,418:INFO:Uploading results into container +2024-04-25 23:39:37,426:INFO:Uploading model into container now +2024-04-25 23:39:37,464:INFO:_master_model_container: 6 +2024-04-25 23:39:37,465:INFO:_display_container: 2 +2024-04-25 23:39:37,468:INFO:LassoLars(random_state=1556) +2024-04-25 23:39:37,471:INFO:create_model() successfully completed...................................... +2024-04-25 23:39:37,920:INFO:SubProcess create_model() end ================================== +2024-04-25 23:39:37,921:INFO:Creating metrics dataframe +2024-04-25 23:39:38,085:INFO:Initializing Orthogonal Matching Pursuit +2024-04-25 23:39:38,087:INFO:Total runtime is 1.8509752273559572 minutes +2024-04-25 23:39:38,294:INFO:SubProcess create_model() called ================================== +2024-04-25 23:39:38,300:INFO:Initializing create_model() +2024-04-25 23:39:38,302:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:39:38,303:INFO:Checking exceptions +2024-04-25 23:39:38,304:INFO:Importing libraries +2024-04-25 23:39:38,305:INFO:Copying training dataset +2024-04-25 23:39:38,782:INFO:Defining folds +2024-04-25 23:39:38,784:INFO:Declaring metric variables +2024-04-25 23:39:38,947:INFO:Importing untrained model +2024-04-25 23:39:39,168:INFO:Orthogonal Matching Pursuit Imported successfully +2024-04-25 23:39:39,532:INFO:Starting cross validation +2024-04-25 23:39:39,543:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:39:44,931:INFO:Calculating mean and std +2024-04-25 23:39:44,954:INFO:Creating metrics dataframe +2024-04-25 23:39:45,155:INFO:Uploading results into container +2024-04-25 23:39:45,168:INFO:Uploading model into container now +2024-04-25 23:39:45,177:INFO:_master_model_container: 7 +2024-04-25 23:39:45,178:INFO:_display_container: 2 +2024-04-25 23:39:45,184:INFO:OrthogonalMatchingPursuit() +2024-04-25 23:39:45,185:INFO:create_model() successfully completed...................................... +2024-04-25 23:39:45,759:INFO:SubProcess create_model() end ================================== +2024-04-25 23:39:45,760:INFO:Creating metrics dataframe +2024-04-25 23:39:45,853:INFO:Initializing Bayesian Ridge +2024-04-25 23:39:45,854:INFO:Total runtime is 1.9804379026095074 minutes +2024-04-25 23:39:45,901:INFO:SubProcess create_model() called ================================== +2024-04-25 23:39:45,906:INFO:Initializing create_model() +2024-04-25 23:39:45,907:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:39:45,908:INFO:Checking exceptions +2024-04-25 23:39:45,909:INFO:Importing libraries +2024-04-25 23:39:45,910:INFO:Copying training dataset +2024-04-25 23:39:46,148:INFO:Defining folds +2024-04-25 23:39:46,150:INFO:Declaring metric variables +2024-04-25 23:39:46,204:INFO:Importing untrained model +2024-04-25 23:39:46,291:INFO:Bayesian Ridge Imported successfully +2024-04-25 23:39:46,563:INFO:Starting cross validation +2024-04-25 23:39:46,577:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:39:49,904:INFO:Calculating mean and std +2024-04-25 23:39:49,912:INFO:Creating metrics dataframe +2024-04-25 23:39:50,046:INFO:Uploading results into container +2024-04-25 23:39:50,073:INFO:Uploading model into container now +2024-04-25 23:39:50,077:INFO:_master_model_container: 8 +2024-04-25 23:39:50,078:INFO:_display_container: 2 +2024-04-25 23:39:50,082:INFO:BayesianRidge() +2024-04-25 23:39:50,082:INFO:create_model() successfully completed...................................... +2024-04-25 23:39:50,492:INFO:SubProcess create_model() end ================================== +2024-04-25 23:39:50,492:INFO:Creating metrics dataframe +2024-04-25 23:39:50,593:INFO:Initializing Passive Aggressive Regressor +2024-04-25 23:39:50,593:INFO:Total runtime is 2.05941432317098 minutes +2024-04-25 23:39:50,617:INFO:SubProcess create_model() called ================================== +2024-04-25 23:39:50,621:INFO:Initializing create_model() +2024-04-25 23:39:50,622:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:39:50,623:INFO:Checking exceptions +2024-04-25 23:39:50,623:INFO:Importing libraries +2024-04-25 23:39:50,624:INFO:Copying training dataset +2024-04-25 23:39:50,721:INFO:Defining folds +2024-04-25 23:39:50,723:INFO:Declaring metric variables +2024-04-25 23:39:50,846:INFO:Importing untrained model +2024-04-25 23:39:50,924:INFO:Passive Aggressive Regressor Imported successfully +2024-04-25 23:39:51,192:INFO:Starting cross validation +2024-04-25 23:39:51,202:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:39:58,996:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 23:39:59,018:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 23:39:59,030:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 23:39:59,185:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 23:40:05,866:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 23:40:05,908:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 23:40:05,950:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 23:40:06,452:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 23:40:10,894:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 23:40:11,005:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-04-25 23:40:11,208:INFO:Calculating mean and std +2024-04-25 23:40:11,218:INFO:Creating metrics dataframe +2024-04-25 23:40:11,313:INFO:Uploading results into container +2024-04-25 23:40:11,316:INFO:Uploading model into container now +2024-04-25 23:40:11,320:INFO:_master_model_container: 9 +2024-04-25 23:40:11,321:INFO:_display_container: 2 +2024-04-25 23:40:11,324:INFO:PassiveAggressiveRegressor(random_state=1556) +2024-04-25 23:40:11,324:INFO:create_model() successfully completed...................................... +2024-04-25 23:40:11,671:INFO:SubProcess create_model() end ================================== +2024-04-25 23:40:11,672:INFO:Creating metrics dataframe +2024-04-25 23:40:11,793:INFO:Initializing Huber Regressor +2024-04-25 23:40:11,794:INFO:Total runtime is 2.412768415609996 minutes +2024-04-25 23:40:11,891:INFO:SubProcess create_model() called ================================== +2024-04-25 23:40:11,893:INFO:Initializing create_model() +2024-04-25 23:40:11,895:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:40:11,896:INFO:Checking exceptions +2024-04-25 23:40:11,897:INFO:Importing libraries +2024-04-25 23:40:11,897:INFO:Copying training dataset +2024-04-25 23:40:12,111:INFO:Defining folds +2024-04-25 23:40:12,113:INFO:Declaring metric variables +2024-04-25 23:40:12,164:INFO:Importing untrained model +2024-04-25 23:40:12,375:INFO:Huber Regressor Imported successfully +2024-04-25 23:40:12,570:INFO:Starting cross validation +2024-04-25 23:40:12,580:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:40:19,382:INFO:Calculating mean and std +2024-04-25 23:40:19,393:INFO:Creating metrics dataframe +2024-04-25 23:40:19,505:INFO:Uploading results into container +2024-04-25 23:40:19,514:INFO:Uploading model into container now +2024-04-25 23:40:19,520:INFO:_master_model_container: 10 +2024-04-25 23:40:19,521:INFO:_display_container: 2 +2024-04-25 23:40:19,523:INFO:HuberRegressor() +2024-04-25 23:40:19,524:INFO:create_model() successfully completed...................................... +2024-04-25 23:40:20,015:INFO:SubProcess create_model() end ================================== +2024-04-25 23:40:20,015:INFO:Creating metrics dataframe +2024-04-25 23:40:20,170:INFO:Initializing K Neighbors Regressor +2024-04-25 23:40:20,171:INFO:Total runtime is 2.5523716410001125 minutes +2024-04-25 23:40:20,246:INFO:SubProcess create_model() called ================================== +2024-04-25 23:40:20,251:INFO:Initializing create_model() +2024-04-25 23:40:20,252:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:40:20,254:INFO:Checking exceptions +2024-04-25 23:40:20,255:INFO:Importing libraries +2024-04-25 23:40:20,255:INFO:Copying training dataset +2024-04-25 23:40:20,450:INFO:Defining folds +2024-04-25 23:40:20,451:INFO:Declaring metric variables +2024-04-25 23:40:20,483:INFO:Importing untrained model +2024-04-25 23:40:20,671:INFO:K Neighbors Regressor Imported successfully +2024-04-25 23:40:20,866:INFO:Starting cross validation +2024-04-25 23:40:20,881:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:40:24,965:INFO:Calculating mean and std +2024-04-25 23:40:24,980:INFO:Creating metrics dataframe +2024-04-25 23:40:25,011:INFO:Uploading results into container +2024-04-25 23:40:25,026:INFO:Uploading model into container now +2024-04-25 23:40:25,061:INFO:_master_model_container: 11 +2024-04-25 23:40:25,062:INFO:_display_container: 2 +2024-04-25 23:40:25,064:INFO:KNeighborsRegressor(n_jobs=-1) +2024-04-25 23:40:25,066:INFO:create_model() successfully completed...................................... +2024-04-25 23:40:25,556:INFO:SubProcess create_model() end ================================== +2024-04-25 23:40:25,557:INFO:Creating metrics dataframe +2024-04-25 23:40:25,659:INFO:Initializing Decision Tree Regressor +2024-04-25 23:40:25,661:INFO:Total runtime is 2.6438710371653245 minutes +2024-04-25 23:40:25,713:INFO:SubProcess create_model() called ================================== +2024-04-25 23:40:25,723:INFO:Initializing create_model() +2024-04-25 23:40:25,724:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:40:25,738:INFO:Checking exceptions +2024-04-25 23:40:25,741:INFO:Importing libraries +2024-04-25 23:40:25,742:INFO:Copying training dataset +2024-04-25 23:40:25,960:INFO:Defining folds +2024-04-25 23:40:25,961:INFO:Declaring metric variables +2024-04-25 23:40:25,994:INFO:Importing untrained model +2024-04-25 23:40:26,183:INFO:Decision Tree Regressor Imported successfully +2024-04-25 23:40:26,342:INFO:Starting cross validation +2024-04-25 23:40:26,352:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:40:32,207:INFO:Calculating mean and std +2024-04-25 23:40:32,246:INFO:Creating metrics dataframe +2024-04-25 23:40:32,376:INFO:Uploading results into container +2024-04-25 23:40:32,391:INFO:Uploading model into container now +2024-04-25 23:40:32,431:INFO:_master_model_container: 12 +2024-04-25 23:40:32,432:INFO:_display_container: 2 +2024-04-25 23:40:32,439:INFO:DecisionTreeRegressor(random_state=1556) +2024-04-25 23:40:32,442:INFO:create_model() successfully completed...................................... +2024-04-25 23:40:33,217:INFO:SubProcess create_model() end ================================== +2024-04-25 23:40:33,218:INFO:Creating metrics dataframe +2024-04-25 23:40:33,527:INFO:Initializing Random Forest Regressor +2024-04-25 23:40:33,528:INFO:Total runtime is 2.7749989032745366 minutes +2024-04-25 23:40:33,616:INFO:SubProcess create_model() called ================================== +2024-04-25 23:40:33,619:INFO:Initializing create_model() +2024-04-25 23:40:33,620:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:40:33,620:INFO:Checking exceptions +2024-04-25 23:40:33,621:INFO:Importing libraries +2024-04-25 23:40:33,622:INFO:Copying training dataset +2024-04-25 23:40:33,830:INFO:Defining folds +2024-04-25 23:40:33,831:INFO:Declaring metric variables +2024-04-25 23:40:33,872:INFO:Importing untrained model +2024-04-25 23:40:34,040:INFO:Random Forest Regressor Imported successfully +2024-04-25 23:40:34,301:INFO:Starting cross validation +2024-04-25 23:40:34,315:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:42:26,548:INFO:Calculating mean and std +2024-04-25 23:42:26,574:INFO:Creating metrics dataframe +2024-04-25 23:42:26,733:INFO:Uploading results into container +2024-04-25 23:42:26,759:INFO:Uploading model into container now +2024-04-25 23:42:26,773:INFO:_master_model_container: 13 +2024-04-25 23:42:26,776:INFO:_display_container: 2 +2024-04-25 23:42:26,779:INFO:RandomForestRegressor(n_jobs=-1, random_state=1556) +2024-04-25 23:42:26,781:INFO:create_model() successfully completed...................................... +2024-04-25 23:42:27,401:INFO:SubProcess create_model() end ================================== +2024-04-25 23:42:27,403:INFO:Creating metrics dataframe +2024-04-25 23:42:27,555:INFO:Initializing Extra Trees Regressor +2024-04-25 23:42:27,556:INFO:Total runtime is 4.675461876392365 minutes +2024-04-25 23:42:27,599:INFO:SubProcess create_model() called ================================== +2024-04-25 23:42:27,601:INFO:Initializing create_model() +2024-04-25 23:42:27,602:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:42:27,603:INFO:Checking exceptions +2024-04-25 23:42:27,603:INFO:Importing libraries +2024-04-25 23:42:27,604:INFO:Copying training dataset +2024-04-25 23:42:27,906:INFO:Defining folds +2024-04-25 23:42:27,907:INFO:Declaring metric variables +2024-04-25 23:42:27,959:INFO:Importing untrained model +2024-04-25 23:42:28,089:INFO:Extra Trees Regressor Imported successfully +2024-04-25 23:42:28,319:INFO:Starting cross validation +2024-04-25 23:42:28,329:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:43:38,011:INFO:Calculating mean and std +2024-04-25 23:43:38,033:INFO:Creating metrics dataframe +2024-04-25 23:43:38,186:INFO:Uploading results into container +2024-04-25 23:43:38,192:INFO:Uploading model into container now +2024-04-25 23:43:38,201:INFO:_master_model_container: 14 +2024-04-25 23:43:38,206:INFO:_display_container: 2 +2024-04-25 23:43:38,219:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=1556) +2024-04-25 23:43:38,223:INFO:create_model() successfully completed...................................... +2024-04-25 23:43:38,782:INFO:SubProcess create_model() end ================================== +2024-04-25 23:43:38,785:INFO:Creating metrics dataframe +2024-04-25 23:43:38,956:INFO:Initializing AdaBoost Regressor +2024-04-25 23:43:38,957:INFO:Total runtime is 5.865484905242921 minutes +2024-04-25 23:43:39,011:INFO:SubProcess create_model() called ================================== +2024-04-25 23:43:39,014:INFO:Initializing create_model() +2024-04-25 23:43:39,015:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:43:39,016:INFO:Checking exceptions +2024-04-25 23:43:39,016:INFO:Importing libraries +2024-04-25 23:43:39,017:INFO:Copying training dataset +2024-04-25 23:43:39,388:INFO:Defining folds +2024-04-25 23:43:39,388:INFO:Declaring metric variables +2024-04-25 23:43:39,651:INFO:Importing untrained model +2024-04-25 23:43:39,830:INFO:AdaBoost Regressor Imported successfully +2024-04-25 23:43:39,982:INFO:Starting cross validation +2024-04-25 23:43:39,997:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:43:57,544:INFO:Calculating mean and std +2024-04-25 23:43:57,558:INFO:Creating metrics dataframe +2024-04-25 23:43:57,607:INFO:Uploading results into container +2024-04-25 23:43:57,611:INFO:Uploading model into container now +2024-04-25 23:43:57,616:INFO:_master_model_container: 15 +2024-04-25 23:43:57,617:INFO:_display_container: 2 +2024-04-25 23:43:57,619:INFO:AdaBoostRegressor(random_state=1556) +2024-04-25 23:43:57,622:INFO:create_model() successfully completed...................................... +2024-04-25 23:43:58,206:INFO:SubProcess create_model() end ================================== +2024-04-25 23:43:58,207:INFO:Creating metrics dataframe +2024-04-25 23:43:58,373:INFO:Initializing Gradient Boosting Regressor +2024-04-25 23:43:58,374:INFO:Total runtime is 6.1890998562177035 minutes +2024-04-25 23:43:58,452:INFO:SubProcess create_model() called ================================== +2024-04-25 23:43:58,454:INFO:Initializing create_model() +2024-04-25 23:43:58,455:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:43:58,455:INFO:Checking exceptions +2024-04-25 23:43:58,467:INFO:Importing libraries +2024-04-25 23:43:58,468:INFO:Copying training dataset +2024-04-25 23:43:58,741:INFO:Defining folds +2024-04-25 23:43:58,742:INFO:Declaring metric variables +2024-04-25 23:43:58,841:INFO:Importing untrained model +2024-04-25 23:43:58,944:INFO:Gradient Boosting Regressor Imported successfully +2024-04-25 23:43:59,087:INFO:Starting cross validation +2024-04-25 23:43:59,101:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:44:34,066:INFO:Calculating mean and std +2024-04-25 23:44:34,078:INFO:Creating metrics dataframe +2024-04-25 23:44:34,249:INFO:Uploading results into container +2024-04-25 23:44:34,259:INFO:Uploading model into container now +2024-04-25 23:44:34,296:INFO:_master_model_container: 16 +2024-04-25 23:44:34,324:INFO:_display_container: 2 +2024-04-25 23:44:34,328:INFO:GradientBoostingRegressor(random_state=1556) +2024-04-25 23:44:34,328:INFO:create_model() successfully completed...................................... +2024-04-25 23:44:34,782:INFO:SubProcess create_model() end ================================== +2024-04-25 23:44:34,783:INFO:Creating metrics dataframe +2024-04-25 23:44:34,939:INFO:Initializing Extreme Gradient Boosting +2024-04-25 23:44:34,940:INFO:Total runtime is 6.798531937599184 minutes +2024-04-25 23:44:34,972:INFO:SubProcess create_model() called ================================== +2024-04-25 23:44:34,976:INFO:Initializing create_model() +2024-04-25 23:44:34,976:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:44:34,977:INFO:Checking exceptions +2024-04-25 23:44:34,978:INFO:Importing libraries +2024-04-25 23:44:34,979:INFO:Copying training dataset +2024-04-25 23:44:35,151:INFO:Defining folds +2024-04-25 23:44:35,152:INFO:Declaring metric variables +2024-04-25 23:44:35,299:INFO:Importing untrained model +2024-04-25 23:44:35,692:INFO:Extreme Gradient Boosting Imported successfully +2024-04-25 23:44:35,872:INFO:Starting cross validation +2024-04-25 23:44:35,883:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:45:10,370:INFO:Calculating mean and std +2024-04-25 23:45:10,385:INFO:Creating metrics dataframe +2024-04-25 23:45:10,520:INFO:Uploading results into container +2024-04-25 23:45:10,595:INFO:Uploading model into container now +2024-04-25 23:45:10,603:INFO:_master_model_container: 17 +2024-04-25 23:45:10,604:INFO:_display_container: 2 +2024-04-25 23:45:10,618: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, predictor=None, random_state=1556, + reg_alpha=None, reg_lambda=None, ...) +2024-04-25 23:45:10,619:INFO:create_model() successfully completed...................................... +2024-04-25 23:45:11,342:INFO:SubProcess create_model() end ================================== +2024-04-25 23:45:11,343:INFO:Creating metrics dataframe +2024-04-25 23:45:11,586:INFO:Initializing Light Gradient Boosting Machine +2024-04-25 23:45:11,587:INFO:Total runtime is 7.409321578343711 minutes +2024-04-25 23:45:11,702:INFO:SubProcess create_model() called ================================== +2024-04-25 23:45:11,704:INFO:Initializing create_model() +2024-04-25 23:45:11,704:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:45:11,705:INFO:Checking exceptions +2024-04-25 23:45:11,706:INFO:Importing libraries +2024-04-25 23:45:11,706:INFO:Copying training dataset +2024-04-25 23:45:12,087:INFO:Defining folds +2024-04-25 23:45:12,088:INFO:Declaring metric variables +2024-04-25 23:45:12,218:INFO:Importing untrained model +2024-04-25 23:45:12,266:INFO:Light Gradient Boosting Machine Imported successfully +2024-04-25 23:45:12,501:INFO:Starting cross validation +2024-04-25 23:45:12,515:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:45:32,165:INFO:Calculating mean and std +2024-04-25 23:45:32,180:INFO:Creating metrics dataframe +2024-04-25 23:45:32,303:INFO:Uploading results into container +2024-04-25 23:45:32,313:INFO:Uploading model into container now +2024-04-25 23:45:32,317:INFO:_master_model_container: 18 +2024-04-25 23:45:32,318:INFO:_display_container: 2 +2024-04-25 23:45:32,356:INFO:LGBMRegressor(n_jobs=-1, random_state=1556) +2024-04-25 23:45:32,357:INFO:create_model() successfully completed...................................... +2024-04-25 23:45:32,875:INFO:SubProcess create_model() end ================================== +2024-04-25 23:45:32,876:INFO:Creating metrics dataframe +2024-04-25 23:45:33,074:INFO:Initializing Dummy Regressor +2024-04-25 23:45:33,075:INFO:Total runtime is 7.76745456059774 minutes +2024-04-25 23:45:33,149:INFO:SubProcess create_model() called ================================== +2024-04-25 23:45:33,158:INFO:Initializing create_model() +2024-04-25 23:45:33,159:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:45:33,160:INFO:Checking exceptions +2024-04-25 23:45:33,161:INFO:Importing libraries +2024-04-25 23:45:33,161:INFO:Copying training dataset +2024-04-25 23:45:33,327:INFO:Defining folds +2024-04-25 23:45:33,328:INFO:Declaring metric variables +2024-04-25 23:45:33,576:INFO:Importing untrained model +2024-04-25 23:45:33,751:INFO:Dummy Regressor Imported successfully +2024-04-25 23:45:34,025:INFO:Starting cross validation +2024-04-25 23:45:34,118:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:45:37,057:INFO:Calculating mean and std +2024-04-25 23:45:37,081:INFO:Creating metrics dataframe +2024-04-25 23:45:37,234:INFO:Uploading results into container +2024-04-25 23:45:37,238:INFO:Uploading model into container now +2024-04-25 23:45:37,242:INFO:_master_model_container: 19 +2024-04-25 23:45:37,244:INFO:_display_container: 2 +2024-04-25 23:45:37,252:INFO:DummyRegressor() +2024-04-25 23:45:37,253:INFO:create_model() successfully completed...................................... +2024-04-25 23:45:37,833:INFO:SubProcess create_model() end ================================== +2024-04-25 23:45:37,834:INFO:Creating metrics dataframe +2024-04-25 23:45:38,341:INFO:Initializing create_model() +2024-04-25 23:45:38,342:INFO:create_model(self=, estimator=LGBMRegressor(n_jobs=-1, random_state=1556), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:45:38,342:INFO:Checking exceptions +2024-04-25 23:45:38,356:INFO:Importing libraries +2024-04-25 23:45:38,357:INFO:Copying training dataset +2024-04-25 23:45:38,737:INFO:Defining folds +2024-04-25 23:45:38,739:INFO:Declaring metric variables +2024-04-25 23:45:38,740:INFO:Importing untrained model +2024-04-25 23:45:38,741:INFO:Declaring custom model +2024-04-25 23:45:38,757:INFO:Light Gradient Boosting Machine Imported successfully +2024-04-25 23:45:38,771:INFO:Cross validation set to False +2024-04-25 23:45:38,773:INFO:Fitting Model +2024-04-25 23:45:39,404:INFO:[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004175 seconds. +2024-04-25 23:45:39,405:INFO:You can set `force_col_wise=true` to remove the overhead. +2024-04-25 23:45:39,406:INFO:[LightGBM] [Info] Total Bins 1098 +2024-04-25 23:45:39,407:INFO:[LightGBM] [Info] Number of data points in the train set: 4504, number of used features: 9 +2024-04-25 23:45:39,410:INFO:[LightGBM] [Info] Start training from score 1044720.346647 +2024-04-25 23:45:40,745:INFO:LGBMRegressor(n_jobs=-1, random_state=1556) +2024-04-25 23:45:40,746:INFO:create_model() successfully completed...................................... +2024-04-25 23:45:42,034:INFO:_master_model_container: 19 +2024-04-25 23:45:42,035:INFO:_display_container: 2 +2024-04-25 23:45:42,040:INFO:LGBMRegressor(n_jobs=-1, random_state=1556) +2024-04-25 23:45:42,041:INFO:compare_models() successfully completed...................................... +2024-04-25 23:47:12,002:INFO:Initializing create_model() +2024-04-25 23:47:12,003:INFO:create_model(self=, estimator=xgboost, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:47:12,005:INFO:Checking exceptions +2024-04-25 23:47:12,182:INFO:Importing libraries +2024-04-25 23:47:12,183:INFO:Copying training dataset +2024-04-25 23:47:12,491:INFO:Defining folds +2024-04-25 23:47:12,494:INFO:Declaring metric variables +2024-04-25 23:47:12,720:INFO:Importing untrained model +2024-04-25 23:47:12,873:INFO:Extreme Gradient Boosting Imported successfully +2024-04-25 23:47:13,377:INFO:Starting cross validation +2024-04-25 23:47:13,622:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:47:49,028:INFO:Calculating mean and std +2024-04-25 23:47:49,043:INFO:Creating metrics dataframe +2024-04-25 23:47:49,195:INFO:Finalizing model +2024-04-25 23:47:53,958:INFO:Uploading results into container +2024-04-25 23:47:53,965:INFO:Uploading model into container now +2024-04-25 23:47:54,186:INFO:_master_model_container: 20 +2024-04-25 23:47:54,186:INFO:_display_container: 3 +2024-04-25 23:47:54,241: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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...) +2024-04-25 23:47:54,241:INFO:create_model() successfully completed...................................... +2024-04-25 23:49:37,664:INFO:Initializing evaluate_model() +2024-04-25 23:49:37,664:INFO:evaluate_model(self=, estimator=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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) +2024-04-25 23:49:37,909:INFO:Initializing plot_model() +2024-04-25 23:49:37,909:INFO:plot_model(self=, estimator=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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...), plot=pipeline, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-04-25 23:49:37,909:INFO:Checking exceptions +2024-04-25 23:49:37,917:INFO:Preloading libraries +2024-04-25 23:49:37,928:INFO:Copying training dataset +2024-04-25 23:49:37,929:INFO:Plot type: pipeline +2024-04-25 23:49:39,218:INFO:Visual Rendered Successfully +2024-04-25 23:49:39,337:INFO:plot_model() successfully completed...................................... +2024-04-25 23:49:43,288:INFO:Initializing tune_model() +2024-04-25 23:49:43,289:INFO:tune_model(self=, estimator=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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=optuna, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-04-25 23:49:43,289:INFO:Checking exceptions +2024-04-25 23:49:43,289:INFO:Soft dependency imported: optuna: 3.6.1 +2024-04-25 23:49:44,664:INFO:Copying training dataset +2024-04-25 23:49:44,680:INFO:Checking base model +2024-04-25 23:49:44,680:INFO:Base model : Extreme Gradient Boosting +2024-04-25 23:49:44,697:INFO:Declaring metric variables +2024-04-25 23:49:44,762:INFO:Defining Hyperparameters +2024-04-25 23:49:45,191:INFO:Tuning with n_jobs=-1 +2024-04-25 23:49:45,320:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\optuna\samplers\_tpe\sampler.py:319: ExperimentalWarning: ``multivariate`` option is an experimental feature. The interface can change in the future. + warnings.warn( + +2024-04-25 23:49:45,321:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\optuna\samplers\_tpe\sampler.py:338: ExperimentalWarning: ``constant_liar`` option is an experimental feature. The interface can change in the future. + warnings.warn( + +2024-04-25 23:49:45,323:INFO:Initializing optuna.integration.OptunaSearchCV +2024-04-25 23:49:45,513:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:2458: ExperimentalWarning: OptunaSearchCV is experimental (supported from v0.17.0). The interface can change in the future. + model_grid = optuna.integration.OptunaSearchCV( # type: ignore + +2024-04-25 23:50:52,312:INFO:best_params: {'actual_estimator__learning_rate': 0.0902335860660403, 'actual_estimator__n_estimators': 13, 'actual_estimator__subsample': 0.7058681693658853, 'actual_estimator__max_depth': 8, 'actual_estimator__colsample_bytree': 0.5753866136690098, 'actual_estimator__min_child_weight': 2, 'actual_estimator__reg_alpha': 0.004462146786822356, 'actual_estimator__reg_lambda': 0.13666316387812516, 'actual_estimator__scale_pos_weight': 39.52125918691066} +2024-04-25 23:50:52,326:INFO:Hyperparameter search completed +2024-04-25 23:50:52,326:INFO:SubProcess create_model() called ================================== +2024-04-25 23:50:52,331:INFO:Initializing create_model() +2024-04-25 23:50:52,331:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'learning_rate': 0.0902335860660403, 'n_estimators': 13, 'subsample': 0.7058681693658853, 'max_depth': 8, 'colsample_bytree': 0.5753866136690098, 'min_child_weight': 2, 'reg_alpha': 0.004462146786822356, 'reg_lambda': 0.13666316387812516, 'scale_pos_weight': 39.52125918691066}) +2024-04-25 23:50:52,331:INFO:Checking exceptions +2024-04-25 23:50:52,331:INFO:Importing libraries +2024-04-25 23:50:52,332:INFO:Copying training dataset +2024-04-25 23:50:52,361:INFO:Defining folds +2024-04-25 23:50:52,361:INFO:Declaring metric variables +2024-04-25 23:50:52,372:INFO:Importing untrained model +2024-04-25 23:50:52,373:INFO:Declaring custom model +2024-04-25 23:50:52,390:INFO:Extreme Gradient Boosting Imported successfully +2024-04-25 23:50:52,423:INFO:Starting cross validation +2024-04-25 23:50:52,428:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:50:53,917:INFO:Calculating mean and std +2024-04-25 23:50:53,922:INFO:Creating metrics dataframe +2024-04-25 23:50:53,951:INFO:Finalizing model +2024-04-25 23:50:54,212:INFO:Uploading results into container +2024-04-25 23:50:54,215:INFO:Uploading model into container now +2024-04-25 23:50:54,216:INFO:_master_model_container: 21 +2024-04-25 23:50:54,216:INFO:_display_container: 4 +2024-04-25 23:50:54,225:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, + colsample_bytree=0.5753866136690098, 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=0.0902335860660403, + max_bin=256, max_cat_to_onehot=4, max_delta_step=0, max_depth=8, + max_leaves=0, min_child_weight=2, missing=nan, + monotone_constraints='()', n_estimators=13, n_jobs=-1, + num_parallel_tree=1, predictor='auto', random_state=1556, + reg_alpha=0.004462146786822356, reg_lambda=0.13666316387812516, ...) +2024-04-25 23:50:54,226:INFO:create_model() successfully completed...................................... +2024-04-25 23:50:54,491:INFO:SubProcess create_model() end ================================== +2024-04-25 23:50:54,492:INFO:choose_better activated +2024-04-25 23:50:54,502:INFO:SubProcess create_model() called ================================== +2024-04-25 23:50:54,512:INFO:Initializing create_model() +2024-04-25 23:50:54,513:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:50:54,513:INFO:Checking exceptions +2024-04-25 23:50:54,518:INFO:Importing libraries +2024-04-25 23:50:54,519:INFO:Copying training dataset +2024-04-25 23:50:54,537:INFO:Defining folds +2024-04-25 23:50:54,537:INFO:Declaring metric variables +2024-04-25 23:50:54,538:INFO:Importing untrained model +2024-04-25 23:50:54,538:INFO:Declaring custom model +2024-04-25 23:50:54,555:INFO:Extreme Gradient Boosting Imported successfully +2024-04-25 23:50:54,556:INFO:Starting cross validation +2024-04-25 23:50:54,559:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:51:01,304:INFO:Calculating mean and std +2024-04-25 23:51:01,305:INFO:Creating metrics dataframe +2024-04-25 23:51:01,308:INFO:Finalizing model +2024-04-25 23:51:02,272:INFO:Uploading results into container +2024-04-25 23:51:02,274:INFO:Uploading model into container now +2024-04-25 23:51:02,275:INFO:_master_model_container: 22 +2024-04-25 23:51:02,275:INFO:_display_container: 5 +2024-04-25 23:51:02,284: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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...) +2024-04-25 23:51:02,284:INFO:create_model() successfully completed...................................... +2024-04-25 23:51:02,466:INFO:SubProcess create_model() end ================================== +2024-04-25 23:51:02,470: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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...) result for R2 is 0.9732 +2024-04-25 23:51:02,474:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, + colsample_bytree=0.5753866136690098, 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=0.0902335860660403, + max_bin=256, max_cat_to_onehot=4, max_delta_step=0, max_depth=8, + max_leaves=0, min_child_weight=2, missing=nan, + monotone_constraints='()', n_estimators=13, n_jobs=-1, + num_parallel_tree=1, predictor='auto', random_state=1556, + reg_alpha=0.004462146786822356, reg_lambda=0.13666316387812516, ...) result for R2 is 0.4652 +2024-04-25 23:51:02,477: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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...) is best model +2024-04-25 23:51:02,477:INFO:choose_better completed +2024-04-25 23:51:02,478:INFO:Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one). +2024-04-25 23:51:02,503:INFO:_master_model_container: 22 +2024-04-25 23:51:02,504:INFO:_display_container: 4 +2024-04-25 23:51:02,516: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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...) +2024-04-25 23:51:02,516:INFO:tune_model() successfully completed...................................... +2024-04-25 23:51:35,574:INFO:Initializing tune_model() +2024-04-25 23:51:35,574:INFO:tune_model(self=, estimator=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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-04-25 23:51:35,574:INFO:Checking exceptions +2024-04-25 23:51:35,635:INFO:Copying training dataset +2024-04-25 23:51:35,649:INFO:Checking base model +2024-04-25 23:51:35,649:INFO:Base model : Extreme Gradient Boosting +2024-04-25 23:51:35,679:INFO:Declaring metric variables +2024-04-25 23:51:35,694:INFO:Defining Hyperparameters +2024-04-25 23:51:36,015:INFO:Tuning with n_jobs=-1 +2024-04-25 23:51:36,015:INFO:Initializing RandomizedSearchCV +2024-04-25 23:52:54,921:INFO:best_params: {'actual_estimator__subsample': 1, 'actual_estimator__scale_pos_weight': 9.3, 'actual_estimator__reg_lambda': 0.0005, 'actual_estimator__reg_alpha': 0.5, 'actual_estimator__n_estimators': 170, 'actual_estimator__min_child_weight': 1, 'actual_estimator__max_depth': 7, 'actual_estimator__learning_rate': 0.1, 'actual_estimator__colsample_bytree': 0.7} +2024-04-25 23:52:54,928:INFO:Hyperparameter search completed +2024-04-25 23:52:54,929:INFO:SubProcess create_model() called ================================== +2024-04-25 23:52:54,934:INFO:Initializing create_model() +2024-04-25 23:52:54,934:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'subsample': 1, 'scale_pos_weight': 9.3, 'reg_lambda': 0.0005, 'reg_alpha': 0.5, 'n_estimators': 170, 'min_child_weight': 1, 'max_depth': 7, 'learning_rate': 0.1, 'colsample_bytree': 0.7}) +2024-04-25 23:52:54,934:INFO:Checking exceptions +2024-04-25 23:52:54,935:INFO:Importing libraries +2024-04-25 23:52:54,936:INFO:Copying training dataset +2024-04-25 23:52:54,963:INFO:Defining folds +2024-04-25 23:52:54,964:INFO:Declaring metric variables +2024-04-25 23:52:54,973:INFO:Importing untrained model +2024-04-25 23:52:54,973:INFO:Declaring custom model +2024-04-25 23:52:54,983:INFO:Extreme Gradient Boosting Imported successfully +2024-04-25 23:52:55,003:INFO:Starting cross validation +2024-04-25 23:52:55,007:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:53:05,883:INFO:Calculating mean and std +2024-04-25 23:53:05,886:INFO:Creating metrics dataframe +2024-04-25 23:53:05,905:INFO:Finalizing model +2024-04-25 23:53:08,135:INFO:Uploading results into container +2024-04-25 23:53:08,137:INFO:Uploading model into container now +2024-04-25 23:53:08,139:INFO:_master_model_container: 23 +2024-04-25 23:53:08,140:INFO:_display_container: 5 +2024-04-25 23:53:08,156:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.7, + 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=0.1, max_bin=256, max_cat_to_onehot=4, + max_delta_step=0, max_depth=7, max_leaves=0, min_child_weight=1, + missing=nan, monotone_constraints='()', n_estimators=170, + n_jobs=-1, num_parallel_tree=1, predictor='auto', + random_state=1556, reg_alpha=0.5, reg_lambda=0.0005, ...) +2024-04-25 23:53:08,156:INFO:create_model() successfully completed...................................... +2024-04-25 23:53:08,408:INFO:SubProcess create_model() end ================================== +2024-04-25 23:53:08,408:INFO:choose_better activated +2024-04-25 23:53:08,426:INFO:SubProcess create_model() called ================================== +2024-04-25 23:53:08,440:INFO:Initializing create_model() +2024-04-25 23:53:08,441:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-04-25 23:53:08,443:INFO:Checking exceptions +2024-04-25 23:53:08,456:INFO:Importing libraries +2024-04-25 23:53:08,456:INFO:Copying training dataset +2024-04-25 23:53:08,538:INFO:Defining folds +2024-04-25 23:53:08,540:INFO:Declaring metric variables +2024-04-25 23:53:08,541:INFO:Importing untrained model +2024-04-25 23:53:08,542:INFO:Declaring custom model +2024-04-25 23:53:08,563:INFO:Extreme Gradient Boosting Imported successfully +2024-04-25 23:53:08,564:INFO:Starting cross validation +2024-04-25 23:53:08,567:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-04-25 23:53:16,023:INFO:Calculating mean and std +2024-04-25 23:53:16,024:INFO:Creating metrics dataframe +2024-04-25 23:53:16,029:INFO:Finalizing model +2024-04-25 23:53:17,109:INFO:Uploading results into container +2024-04-25 23:53:17,111:INFO:Uploading model into container now +2024-04-25 23:53:17,114:INFO:_master_model_container: 24 +2024-04-25 23:53:17,114:INFO:_display_container: 6 +2024-04-25 23:53:17,124: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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...) +2024-04-25 23:53:17,124:INFO:create_model() successfully completed...................................... +2024-04-25 23:53:17,323:INFO:SubProcess create_model() end ================================== +2024-04-25 23:53:17,326: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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...) result for R2 is 0.9732 +2024-04-25 23:53:17,330:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.7, + 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=0.1, max_bin=256, max_cat_to_onehot=4, + max_delta_step=0, max_depth=7, max_leaves=0, min_child_weight=1, + missing=nan, monotone_constraints='()', n_estimators=170, + n_jobs=-1, num_parallel_tree=1, predictor='auto', + random_state=1556, reg_alpha=0.5, reg_lambda=0.0005, ...) result for R2 is 0.9711 +2024-04-25 23:53:17,338: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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...) is best model +2024-04-25 23:53:17,339:INFO:choose_better completed +2024-04-25 23:53:17,341:INFO:Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one). +2024-04-25 23:53:17,367:INFO:_master_model_container: 24 +2024-04-25 23:53:17,367:INFO:_display_container: 5 +2024-04-25 23:53:17,383: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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...) +2024-04-25 23:53:17,383:INFO:tune_model() successfully completed...................................... +2024-04-25 23:54:00,195:INFO:Initializing predict_model() +2024-04-25 23:54:00,195:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BE02498FE0>) +2024-04-25 23:54:00,195:INFO:Checking exceptions +2024-04-25 23:54:00,195:INFO:Preloading libraries +2024-04-25 23:54:00,202:INFO:Set up data. +2024-04-25 23:54:00,210:INFO:Set up index. +2024-04-26 00:37:04,203:INFO:Initializing predict_model() +2024-04-26 00:37:04,203:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BE024D4180>) +2024-04-26 00:37:04,203:INFO:Checking exceptions +2024-04-26 00:37:04,204:INFO:Preloading libraries +2024-04-26 00:37:04,209:INFO:Set up data. +2024-04-26 00:37:04,218:INFO:Set up index. +2024-04-26 00:37:13,059:INFO:Initializing predict_model() +2024-04-26 00:37:13,059:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=1556, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BE02A791C0>) +2024-04-26 00:37:13,059:INFO:Checking exceptions +2024-04-26 00:37:13,059:INFO:Preloading libraries +2024-04-26 00:37:13,064:INFO:Set up data. +2024-04-26 00:37:13,072:INFO:Set up index. +2024-05-01 14:53:52,432:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 14:53:52,462:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 14:53:52,464:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 14:53:52,465:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 14:53:59,470:INFO:PyCaret RegressionExperiment +2024-05-01 14:53:59,471:INFO:Logging name: reg-default-name +2024-05-01 14:53:59,472:INFO:ML Usecase: MLUsecase.REGRESSION +2024-05-01 14:53:59,473:INFO:version 3.3.0 +2024-05-01 14:53:59,473:INFO:Initializing setup() +2024-05-01 14:53:59,474:INFO:self.USI: 96d5 +2024-05-01 14:53:59,474:INFO:self._variable_keys: {'X_test', 'fold_shuffle_param', 'exp_name_log', '_ml_usecase', 'X', 'n_jobs_param', 'log_plots_param', 'y_test', 'logging_param', 'html_param', 'y', 'gpu_n_jobs_param', 'seed', 'USI', 'transform_target_param', 'target_param', 'data', 'X_train', 'memory', 'exp_id', 'fold_groups_param', 'fold_generator', 'pipeline', 'idx', 'y_train', '_available_plots', 'gpu_param'} +2024-05-01 14:53:59,475:INFO:Checking environment +2024-05-01 14:53:59,476:INFO:python_version: 3.11.0 +2024-05-01 14:53:59,476:INFO:python_build: ('main', 'Oct 24 2022 18:26:48') +2024-05-01 14:53:59,477:INFO:machine: AMD64 +2024-05-01 14:53:59,478:INFO:platform: Windows-10-10.0.22000-SP0 +2024-05-01 14:53:59,528:INFO:Memory: svmem(total=8467492864, available=1379897344, percent=83.7, used=7087595520, free=1379897344) +2024-05-01 14:53:59,534:INFO:Physical Core: 2 +2024-05-01 14:53:59,756:INFO:Logical Core: 4 +2024-05-01 14:53:59,759:INFO:Checking libraries +2024-05-01 14:53:59,760:INFO:System: +2024-05-01 14:53:59,761:INFO: python: 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] +2024-05-01 14:53:59,761:INFO:executable: c:\Users\Jason\Documents\DataB3\.venv\Scripts\python.exe +2024-05-01 14:53:59,762:INFO: machine: Windows-10-10.0.22000-SP0 +2024-05-01 14:53:59,762:INFO:PyCaret required dependencies: +2024-05-01 14:54:03,785:INFO: pip: 24.0 +2024-05-01 14:54:03,786:INFO: setuptools: 65.5.0 +2024-05-01 14:54:03,786:INFO: pycaret: 3.3.0 +2024-05-01 14:54:03,787:INFO: IPython: 8.23.0 +2024-05-01 14:54:03,787:INFO: ipywidgets: 8.1.2 +2024-05-01 14:54:03,788:INFO: tqdm: 4.66.2 +2024-05-01 14:54:03,789:INFO: numpy: 1.24.4 +2024-05-01 14:54:03,789:INFO: pandas: 1.5.3 +2024-05-01 14:54:03,790:INFO: jinja2: 3.1.3 +2024-05-01 14:54:03,790:INFO: scipy: 1.11.4 +2024-05-01 14:54:03,791:INFO: joblib: 1.3.2 +2024-05-01 14:54:03,791:INFO: sklearn: 1.4.1.post1 +2024-05-01 14:54:03,793:INFO: pyod: 1.1.3 +2024-05-01 14:54:03,793:INFO: imblearn: 0.12.2 +2024-05-01 14:54:03,794:INFO: category_encoders: 2.6.3 +2024-05-01 14:54:03,815:INFO: lightgbm: 4.3.0 +2024-05-01 14:54:03,816:INFO: numba: 0.59.1 +2024-05-01 14:54:03,818:INFO: requests: 2.31.0 +2024-05-01 14:54:03,821:INFO: matplotlib: 3.8.3 +2024-05-01 14:54:03,822:INFO: scikitplot: 0.3.7 +2024-05-01 14:54:03,823:INFO: yellowbrick: 1.5 +2024-05-01 14:54:03,825:INFO: plotly: 5.20.0 +2024-05-01 14:54:03,827:INFO: plotly-resampler: Not installed +2024-05-01 14:54:03,827:INFO: kaleido: 0.2.1 +2024-05-01 14:54:03,828:INFO: schemdraw: 0.15 +2024-05-01 14:54:03,829:INFO: statsmodels: 0.14.1 +2024-05-01 14:54:03,829:INFO: sktime: 0.28.0 +2024-05-01 14:54:03,830:INFO: tbats: 1.1.3 +2024-05-01 14:54:03,830:INFO: pmdarima: 2.0.4 +2024-05-01 14:54:03,831:INFO: psutil: 5.9.8 +2024-05-01 14:54:03,831:INFO: markupsafe: 2.1.5 +2024-05-01 14:54:03,832:INFO: pickle5: Not installed +2024-05-01 14:54:03,832:INFO: cloudpickle: 3.0.0 +2024-05-01 14:54:03,832:INFO: deprecation: 2.1.0 +2024-05-01 14:54:03,833:INFO: xxhash: 3.4.1 +2024-05-01 14:54:03,833:INFO: wurlitzer: Not installed +2024-05-01 14:54:03,834:INFO:PyCaret optional dependencies: +2024-05-01 14:54:04,194:INFO: shap: Not installed +2024-05-01 14:54:04,195:INFO: interpret: Not installed +2024-05-01 14:54:04,196:INFO: umap: Not installed +2024-05-01 14:54:04,196:INFO: ydata_profiling: 4.7.0 +2024-05-01 14:54:04,219:INFO: explainerdashboard: Not installed +2024-05-01 14:54:04,220:INFO: autoviz: Not installed +2024-05-01 14:54:04,223:INFO: fairlearn: Not installed +2024-05-01 14:54:04,223:INFO: deepchecks: Not installed +2024-05-01 14:54:04,224:INFO: xgboost: 1.6.2 +2024-05-01 14:54:04,225:INFO: catboost: Not installed +2024-05-01 14:54:04,225:INFO: kmodes: Not installed +2024-05-01 14:54:04,226:INFO: mlxtend: Not installed +2024-05-01 14:54:04,229:INFO: statsforecast: Not installed +2024-05-01 14:54:04,233:INFO: tune_sklearn: Not installed +2024-05-01 14:54:04,233:INFO: ray: Not installed +2024-05-01 14:54:04,234:INFO: hyperopt: Not installed +2024-05-01 14:54:04,235:INFO: optuna: 3.6.1 +2024-05-01 14:54:04,235:INFO: skopt: Not installed +2024-05-01 14:54:04,236:INFO: mlflow: Not installed +2024-05-01 14:54:04,236:INFO: gradio: Not installed +2024-05-01 14:54:04,239:INFO: fastapi: Not installed +2024-05-01 14:54:04,240:INFO: uvicorn: Not installed +2024-05-01 14:54:04,241:INFO: m2cgen: Not installed +2024-05-01 14:54:04,242:INFO: evidently: Not installed +2024-05-01 14:54:04,243:INFO: fugue: Not installed +2024-05-01 14:54:04,244:INFO: streamlit: 1.33.0 +2024-05-01 14:54:04,245:INFO: prophet: 1.1.5 +2024-05-01 14:54:04,245:INFO:None +2024-05-01 14:54:04,246:INFO:Set up data. +2024-05-01 14:54:04,405:INFO:Set up folding strategy. +2024-05-01 14:54:04,406:INFO:Set up train/test split. +2024-05-01 14:54:04,553:INFO:Set up index. +2024-05-01 14:54:04,556:INFO:Assigning column types. +2024-05-01 14:54:04,617:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-05-01 14:54:04,619:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 14:54:04,769:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 14:54:04,874:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 14:54:06,345:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 14:54:10,478:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 14:54:10,516:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 14:54:12,452:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 14:54:12,457:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 14:54:12,557:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 14:54:12,663:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 14:54:14,209:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 14:54:15,480:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 14:54:15,498:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 14:54:15,575:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 14:54:15,580:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-05-01 14:54:15,762:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 14:54:15,909:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 14:54:17,640:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 14:54:20,942:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 14:54:20,955:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 14:54:21,019:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 14:54:21,316:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 14:54:21,815:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 14:54:25,711:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 14:54:29,341:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 14:54:29,347:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 14:54:29,413:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 14:54:29,421:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-05-01 14:54:30,376:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 14:54:34,463:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 14:54:36,005:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 14:54:36,011:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 14:54:36,082:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 14:54:36,361:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 14:54:39,243:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 14:54:41,791:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 14:54:41,799:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 14:54:42,045:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 14:54:42,051:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-05-01 14:54:56,569:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 14:55:05,382:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 14:55:05,602:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 14:55:05,842:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 14:55:12,803:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 14:55:14,402:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 14:55:14,407:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 14:55:14,482:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 14:55:14,504:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-05-01 14:55:17,557:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 14:55:19,517:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 14:55:19,613:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 14:55:21,687:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 14:55:23,033:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 14:55:23,431:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 14:55:23,451:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-05-01 14:55:30,120:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 14:55:30,202:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 14:55:34,590:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 14:55:34,653:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 14:55:34,794:INFO:Preparing preprocessing pipeline... +2024-05-01 14:55:34,795:INFO:Set up date feature engineering. +2024-05-01 14:55:34,795:INFO:Set up simple imputation. +2024-05-01 14:55:34,796:INFO:Set up feature normalization. +2024-05-01 14:55:36,606:INFO:Finished creating preprocessing pipeline. +2024-05-01 14:55:36,841:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\Jason\AppData\Local\Temp\joblib), + steps=[('date_feature_extractor', + TransformerWrapper(include=['Date'], + transformer=ExtractDateTimeFeatures())), + ('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWrapper(transformer=MinMaxScaler()))]) +2024-05-01 14:55:36,842:INFO:Creating final display dataframe. +2024-05-01 14:55:39,786:INFO:Setup _display_container: Description Value +0 Session id 2822 +1 Target Daily_Sales +2 Target type Regression +3 Original data shape (6435, 8) +4 Transformed data shape (6435, 10) +5 Transformed train set shape (4504, 10) +6 Transformed test set shape (1931, 10) +7 Numeric features 6 +8 Date features 1 +9 Preprocess True +10 Imputation type simple +11 Numeric imputation mean +12 Categorical imputation mode +13 Normalize True +14 Normalize method minmax +15 Fold Generator KFold +16 Fold Number 10 +17 CPU Jobs -1 +18 Use GPU False +19 Log Experiment False +20 Experiment Name reg-default-name +21 USI 96d5 +2024-05-01 14:55:47,623:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 14:55:47,772:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 14:55:51,986:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 14:55:52,270:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 14:55:52,511:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour. + warnings.warn( + +2024-05-01 14:55:52,522:INFO:setup() successfully completed in 114.69s............... +2024-05-01 14:55:52,632:INFO:Initializing compare_models() +2024-05-01 14:55:52,633:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-05-01 14:55:52,634:INFO:Checking exceptions +2024-05-01 14:55:52,655:INFO:Preparing display monitor +2024-05-01 14:55:54,451:INFO:Initializing Linear Regression +2024-05-01 14:55:54,455:INFO:Total runtime is 6.6681702931722e-05 minutes +2024-05-01 14:55:54,505:INFO:SubProcess create_model() called ================================== +2024-05-01 14:55:54,507:INFO:Initializing create_model() +2024-05-01 14:55:54,510:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 14:55:54,511:INFO:Checking exceptions +2024-05-01 14:55:54,513:INFO:Importing libraries +2024-05-01 14:55:54,514:INFO:Copying training dataset +2024-05-01 14:55:54,768:INFO:Defining folds +2024-05-01 14:55:54,769:INFO:Declaring metric variables +2024-05-01 14:55:54,815:INFO:Importing untrained model +2024-05-01 14:55:54,860:INFO:Linear Regression Imported successfully +2024-05-01 14:55:55,084:INFO:Starting cross validation +2024-05-01 14:55:55,380:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 14:58:07,778:INFO:Calculating mean and std +2024-05-01 14:58:07,811:INFO:Creating metrics dataframe +2024-05-01 14:58:07,879:INFO:Uploading results into container +2024-05-01 14:58:07,884:INFO:Uploading model into container now +2024-05-01 14:58:07,892:INFO:_master_model_container: 1 +2024-05-01 14:58:07,893:INFO:_display_container: 2 +2024-05-01 14:58:07,896:INFO:LinearRegression(n_jobs=-1) +2024-05-01 14:58:07,897:INFO:create_model() successfully completed...................................... +2024-05-01 14:58:08,950:INFO:SubProcess create_model() end ================================== +2024-05-01 14:58:08,951:INFO:Creating metrics dataframe +2024-05-01 14:58:09,123:INFO:Initializing Lasso Regression +2024-05-01 14:58:09,123:INFO:Total runtime is 2.244528567790985 minutes +2024-05-01 14:58:09,198:INFO:SubProcess create_model() called ================================== +2024-05-01 14:58:09,201:INFO:Initializing create_model() +2024-05-01 14:58:09,201:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 14:58:09,202:INFO:Checking exceptions +2024-05-01 14:58:09,203:INFO:Importing libraries +2024-05-01 14:58:09,204:INFO:Copying training dataset +2024-05-01 14:58:09,411:INFO:Defining folds +2024-05-01 14:58:09,412:INFO:Declaring metric variables +2024-05-01 14:58:09,491:INFO:Importing untrained model +2024-05-01 14:58:09,566:INFO:Lasso Regression Imported successfully +2024-05-01 14:58:09,711:INFO:Starting cross validation +2024-05-01 14:58:09,723:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 14:58:14,363:INFO:Calculating mean and std +2024-05-01 14:58:14,374:INFO:Creating metrics dataframe +2024-05-01 14:58:14,512:INFO:Uploading results into container +2024-05-01 14:58:14,518:INFO:Uploading model into container now +2024-05-01 14:58:14,522:INFO:_master_model_container: 2 +2024-05-01 14:58:14,523:INFO:_display_container: 2 +2024-05-01 14:58:14,526:INFO:Lasso(random_state=2822) +2024-05-01 14:58:14,527:INFO:create_model() successfully completed...................................... +2024-05-01 14:58:15,307:INFO:SubProcess create_model() end ================================== +2024-05-01 14:58:15,308:INFO:Creating metrics dataframe +2024-05-01 14:58:15,514:INFO:Initializing Ridge Regression +2024-05-01 14:58:15,515:INFO:Total runtime is 2.3510465145111086 minutes +2024-05-01 14:58:15,612:INFO:SubProcess create_model() called ================================== +2024-05-01 14:58:15,615:INFO:Initializing create_model() +2024-05-01 14:58:15,615:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 14:58:15,616:INFO:Checking exceptions +2024-05-01 14:58:15,617:INFO:Importing libraries +2024-05-01 14:58:15,618:INFO:Copying training dataset +2024-05-01 14:58:15,760:INFO:Defining folds +2024-05-01 14:58:15,766:INFO:Declaring metric variables +2024-05-01 14:58:15,890:INFO:Importing untrained model +2024-05-01 14:58:15,949:INFO:Ridge Regression Imported successfully +2024-05-01 14:58:16,184:INFO:Starting cross validation +2024-05-01 14:58:16,213:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 14:58:25,035:INFO:Calculating mean and std +2024-05-01 14:58:25,156:INFO:Creating metrics dataframe +2024-05-01 14:58:25,505:INFO:Uploading results into container +2024-05-01 14:58:25,517:INFO:Uploading model into container now +2024-05-01 14:58:25,522:INFO:_master_model_container: 3 +2024-05-01 14:58:25,523:INFO:_display_container: 2 +2024-05-01 14:58:25,527:INFO:Ridge(random_state=2822) +2024-05-01 14:58:25,528:INFO:create_model() successfully completed...................................... +2024-05-01 14:58:28,497:INFO:SubProcess create_model() end ================================== +2024-05-01 14:58:28,498:INFO:Creating metrics dataframe +2024-05-01 14:58:29,037:INFO:Initializing Elastic Net +2024-05-01 14:58:29,038:INFO:Total runtime is 2.576454365253449 minutes +2024-05-01 14:58:29,259:INFO:SubProcess create_model() called ================================== +2024-05-01 14:58:29,261:INFO:Initializing create_model() +2024-05-01 14:58:29,262:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 14:58:29,263:INFO:Checking exceptions +2024-05-01 14:58:29,263:INFO:Importing libraries +2024-05-01 14:58:29,264:INFO:Copying training dataset +2024-05-01 14:58:29,791:INFO:Defining folds +2024-05-01 14:58:29,792:INFO:Declaring metric variables +2024-05-01 14:58:30,005:INFO:Importing untrained model +2024-05-01 14:58:30,225:INFO:Elastic Net Imported successfully +2024-05-01 14:58:30,622:INFO:Starting cross validation +2024-05-01 14:58:30,634:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 14:58:36,180:INFO:Calculating mean and std +2024-05-01 14:58:36,193:INFO:Creating metrics dataframe +2024-05-01 14:58:36,235:INFO:Uploading results into container +2024-05-01 14:58:36,279:INFO:Uploading model into container now +2024-05-01 14:58:36,283:INFO:_master_model_container: 4 +2024-05-01 14:58:36,287:INFO:_display_container: 2 +2024-05-01 14:58:36,289:INFO:ElasticNet(random_state=2822) +2024-05-01 14:58:36,290:INFO:create_model() successfully completed...................................... +2024-05-01 14:58:36,856:INFO:SubProcess create_model() end ================================== +2024-05-01 14:58:36,858:INFO:Creating metrics dataframe +2024-05-01 14:58:36,968:INFO:Initializing Least Angle Regression +2024-05-01 14:58:36,972:INFO:Total runtime is 2.7086818893750513 minutes +2024-05-01 14:58:37,011:INFO:SubProcess create_model() called ================================== +2024-05-01 14:58:37,014:INFO:Initializing create_model() +2024-05-01 14:58:37,015:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 14:58:37,016:INFO:Checking exceptions +2024-05-01 14:58:37,019:INFO:Importing libraries +2024-05-01 14:58:37,020:INFO:Copying training dataset +2024-05-01 14:58:37,103:INFO:Defining folds +2024-05-01 14:58:37,104:INFO:Declaring metric variables +2024-05-01 14:58:37,161:INFO:Importing untrained model +2024-05-01 14:58:37,210:INFO:Least Angle Regression Imported successfully +2024-05-01 14:58:37,315:INFO:Starting cross validation +2024-05-01 14:58:37,324:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 14:58:45,305:INFO:Calculating mean and std +2024-05-01 14:58:45,318:INFO:Creating metrics dataframe +2024-05-01 14:58:45,401:INFO:Uploading results into container +2024-05-01 14:58:45,407:INFO:Uploading model into container now +2024-05-01 14:58:45,411:INFO:_master_model_container: 5 +2024-05-01 14:58:45,413:INFO:_display_container: 2 +2024-05-01 14:58:45,417:INFO:Lars(random_state=2822) +2024-05-01 14:58:45,417:INFO:create_model() successfully completed...................................... +2024-05-01 14:58:46,076:INFO:SubProcess create_model() end ================================== +2024-05-01 14:58:46,077:INFO:Creating metrics dataframe +2024-05-01 14:58:46,258:INFO:Initializing Lasso Least Angle Regression +2024-05-01 14:58:46,260:INFO:Total runtime is 2.8634718497594203 minutes +2024-05-01 14:58:46,326:INFO:SubProcess create_model() called ================================== +2024-05-01 14:58:46,329:INFO:Initializing create_model() +2024-05-01 14:58:46,329:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 14:58:46,330:INFO:Checking exceptions +2024-05-01 14:58:46,331:INFO:Importing libraries +2024-05-01 14:58:46,332:INFO:Copying training dataset +2024-05-01 14:58:46,692:INFO:Defining folds +2024-05-01 14:58:46,693:INFO:Declaring metric variables +2024-05-01 14:58:46,839:INFO:Importing untrained model +2024-05-01 14:58:46,970:INFO:Lasso Least Angle Regression Imported successfully +2024-05-01 14:58:47,273:INFO:Starting cross validation +2024-05-01 14:58:47,407:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 14:58:55,858:INFO:Calculating mean and std +2024-05-01 14:58:55,872:INFO:Creating metrics dataframe +2024-05-01 14:58:56,036:INFO:Uploading results into container +2024-05-01 14:58:56,041:INFO:Uploading model into container now +2024-05-01 14:58:56,276:INFO:_master_model_container: 6 +2024-05-01 14:58:56,277:INFO:_display_container: 2 +2024-05-01 14:58:56,280:INFO:LassoLars(random_state=2822) +2024-05-01 14:58:56,281:INFO:create_model() successfully completed...................................... +2024-05-01 14:58:59,466:INFO:SubProcess create_model() end ================================== +2024-05-01 14:58:59,468:INFO:Creating metrics dataframe +2024-05-01 14:58:59,886:INFO:Initializing Orthogonal Matching Pursuit +2024-05-01 14:58:59,887:INFO:Total runtime is 3.090594879786174 minutes +2024-05-01 14:58:59,970:INFO:SubProcess create_model() called ================================== +2024-05-01 14:58:59,972:INFO:Initializing create_model() +2024-05-01 14:58:59,973:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 14:58:59,992:INFO:Checking exceptions +2024-05-01 14:58:59,996:INFO:Importing libraries +2024-05-01 14:58:59,997:INFO:Copying training dataset +2024-05-01 14:59:00,232:INFO:Defining folds +2024-05-01 14:59:00,272:INFO:Declaring metric variables +2024-05-01 14:59:00,327:INFO:Importing untrained model +2024-05-01 14:59:00,587:INFO:Orthogonal Matching Pursuit Imported successfully +2024-05-01 14:59:00,789:INFO:Starting cross validation +2024-05-01 14:59:00,796:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 14:59:06,460:INFO:Calculating mean and std +2024-05-01 14:59:06,475:INFO:Creating metrics dataframe +2024-05-01 14:59:06,529:INFO:Uploading results into container +2024-05-01 14:59:06,536:INFO:Uploading model into container now +2024-05-01 14:59:06,541:INFO:_master_model_container: 7 +2024-05-01 14:59:06,541:INFO:_display_container: 2 +2024-05-01 14:59:06,543:INFO:OrthogonalMatchingPursuit() +2024-05-01 14:59:06,544:INFO:create_model() successfully completed...................................... +2024-05-01 14:59:08,489:INFO:SubProcess create_model() end ================================== +2024-05-01 14:59:08,490:INFO:Creating metrics dataframe +2024-05-01 14:59:08,636:INFO:Initializing Bayesian Ridge +2024-05-01 14:59:08,637:INFO:Total runtime is 3.23642647266388 minutes +2024-05-01 14:59:08,683:INFO:SubProcess create_model() called ================================== +2024-05-01 14:59:08,685:INFO:Initializing create_model() +2024-05-01 14:59:08,686:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 14:59:08,687:INFO:Checking exceptions +2024-05-01 14:59:08,687:INFO:Importing libraries +2024-05-01 14:59:08,688:INFO:Copying training dataset +2024-05-01 14:59:08,770:INFO:Defining folds +2024-05-01 14:59:08,771:INFO:Declaring metric variables +2024-05-01 14:59:08,835:INFO:Importing untrained model +2024-05-01 14:59:08,902:INFO:Bayesian Ridge Imported successfully +2024-05-01 14:59:09,019:INFO:Starting cross validation +2024-05-01 14:59:09,053:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 14:59:12,348:INFO:Calculating mean and std +2024-05-01 14:59:12,363:INFO:Creating metrics dataframe +2024-05-01 14:59:12,413:INFO:Uploading results into container +2024-05-01 14:59:12,424:INFO:Uploading model into container now +2024-05-01 14:59:12,427:INFO:_master_model_container: 8 +2024-05-01 14:59:12,428:INFO:_display_container: 2 +2024-05-01 14:59:12,430:INFO:BayesianRidge() +2024-05-01 14:59:12,431:INFO:create_model() successfully completed...................................... +2024-05-01 14:59:12,967:INFO:SubProcess create_model() end ================================== +2024-05-01 14:59:12,969:INFO:Creating metrics dataframe +2024-05-01 14:59:13,102:INFO:Initializing Passive Aggressive Regressor +2024-05-01 14:59:13,104:INFO:Total runtime is 3.3108908375104273 minutes +2024-05-01 14:59:13,144:INFO:SubProcess create_model() called ================================== +2024-05-01 14:59:13,146:INFO:Initializing create_model() +2024-05-01 14:59:13,148:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 14:59:13,149:INFO:Checking exceptions +2024-05-01 14:59:13,150:INFO:Importing libraries +2024-05-01 14:59:13,150:INFO:Copying training dataset +2024-05-01 14:59:13,213:INFO:Defining folds +2024-05-01 14:59:13,215:INFO:Declaring metric variables +2024-05-01 14:59:13,271:INFO:Importing untrained model +2024-05-01 14:59:13,315:INFO:Passive Aggressive Regressor Imported successfully +2024-05-01 14:59:13,449:INFO:Starting cross validation +2024-05-01 14:59:13,466:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 14:59:23,255:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 14:59:23,262:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 14:59:23,263:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 14:59:23,284:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 14:59:41,831:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 14:59:42,009:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 14:59:42,679:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 14:59:43,834:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 14:59:54,910:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 14:59:55,499:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 14:59:56,056:INFO:Calculating mean and std +2024-05-01 14:59:56,082:INFO:Creating metrics dataframe +2024-05-01 14:59:56,263:INFO:Uploading results into container +2024-05-01 14:59:56,279:INFO:Uploading model into container now +2024-05-01 14:59:56,292:INFO:_master_model_container: 9 +2024-05-01 14:59:56,293:INFO:_display_container: 2 +2024-05-01 14:59:56,297:INFO:PassiveAggressiveRegressor(random_state=2822) +2024-05-01 14:59:56,298:INFO:create_model() successfully completed...................................... +2024-05-01 14:59:57,569:INFO:SubProcess create_model() end ================================== +2024-05-01 14:59:57,570:INFO:Creating metrics dataframe +2024-05-01 14:59:58,293:INFO:Initializing Huber Regressor +2024-05-01 14:59:58,294:INFO:Total runtime is 4.064026602109274 minutes +2024-05-01 14:59:58,375:INFO:SubProcess create_model() called ================================== +2024-05-01 14:59:58,380:INFO:Initializing create_model() +2024-05-01 14:59:58,381:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 14:59:58,395:INFO:Checking exceptions +2024-05-01 14:59:58,396:INFO:Importing libraries +2024-05-01 14:59:58,397:INFO:Copying training dataset +2024-05-01 14:59:58,711:INFO:Defining folds +2024-05-01 14:59:58,713:INFO:Declaring metric variables +2024-05-01 14:59:58,785:INFO:Importing untrained model +2024-05-01 14:59:58,945:INFO:Huber Regressor Imported successfully +2024-05-01 14:59:59,302:INFO:Starting cross validation +2024-05-01 14:59:59,377:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:00:18,061:INFO:Calculating mean and std +2024-05-01 15:00:18,072:INFO:Creating metrics dataframe +2024-05-01 15:00:18,355:INFO:Uploading results into container +2024-05-01 15:00:18,513:INFO:Uploading model into container now +2024-05-01 15:00:18,515:INFO:_master_model_container: 10 +2024-05-01 15:00:18,516:INFO:_display_container: 2 +2024-05-01 15:00:18,519:INFO:HuberRegressor() +2024-05-01 15:00:18,519:INFO:create_model() successfully completed...................................... +2024-05-01 15:00:21,256:INFO:SubProcess create_model() end ================================== +2024-05-01 15:00:21,257:INFO:Creating metrics dataframe +2024-05-01 15:00:21,956:INFO:Initializing K Neighbors Regressor +2024-05-01 15:00:21,957:INFO:Total runtime is 4.458428009351095 minutes +2024-05-01 15:00:22,172:INFO:SubProcess create_model() called ================================== +2024-05-01 15:00:22,175:INFO:Initializing create_model() +2024-05-01 15:00:22,175:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:00:22,177:INFO:Checking exceptions +2024-05-01 15:00:22,178:INFO:Importing libraries +2024-05-01 15:00:22,179:INFO:Copying training dataset +2024-05-01 15:00:22,639:INFO:Defining folds +2024-05-01 15:00:22,642:INFO:Declaring metric variables +2024-05-01 15:00:22,861:INFO:Importing untrained model +2024-05-01 15:00:23,078:INFO:K Neighbors Regressor Imported successfully +2024-05-01 15:00:23,391:INFO:Starting cross validation +2024-05-01 15:00:23,401:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:00:50,203:INFO:Calculating mean and std +2024-05-01 15:00:50,213:INFO:Creating metrics dataframe +2024-05-01 15:00:50,453:INFO:Uploading results into container +2024-05-01 15:00:50,458:INFO:Uploading model into container now +2024-05-01 15:00:50,461:INFO:_master_model_container: 11 +2024-05-01 15:00:50,462:INFO:_display_container: 2 +2024-05-01 15:00:50,464:INFO:KNeighborsRegressor(n_jobs=-1) +2024-05-01 15:00:50,465:INFO:create_model() successfully completed...................................... +2024-05-01 15:00:54,061:INFO:SubProcess create_model() end ================================== +2024-05-01 15:00:54,062:INFO:Creating metrics dataframe +2024-05-01 15:00:54,378:INFO:Initializing Decision Tree Regressor +2024-05-01 15:00:54,379:INFO:Total runtime is 4.998787554105123 minutes +2024-05-01 15:00:54,513:INFO:SubProcess create_model() called ================================== +2024-05-01 15:00:54,515:INFO:Initializing create_model() +2024-05-01 15:00:54,516:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:00:54,516:INFO:Checking exceptions +2024-05-01 15:00:54,518:INFO:Importing libraries +2024-05-01 15:00:54,519:INFO:Copying training dataset +2024-05-01 15:00:54,782:INFO:Defining folds +2024-05-01 15:00:54,788:INFO:Declaring metric variables +2024-05-01 15:00:55,292:INFO:Importing untrained model +2024-05-01 15:00:55,495:INFO:Decision Tree Regressor Imported successfully +2024-05-01 15:00:56,515:INFO:Starting cross validation +2024-05-01 15:00:56,523:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:01:14,094:INFO:Calculating mean and std +2024-05-01 15:01:14,112:INFO:Creating metrics dataframe +2024-05-01 15:01:14,257:INFO:Uploading results into container +2024-05-01 15:01:14,269:INFO:Uploading model into container now +2024-05-01 15:01:14,275:INFO:_master_model_container: 12 +2024-05-01 15:01:14,288:INFO:_display_container: 2 +2024-05-01 15:01:14,297:INFO:DecisionTreeRegressor(random_state=2822) +2024-05-01 15:01:14,309:INFO:create_model() successfully completed...................................... +2024-05-01 15:01:15,907:INFO:SubProcess create_model() end ================================== +2024-05-01 15:01:15,910:INFO:Creating metrics dataframe +2024-05-01 15:01:16,257:INFO:Initializing Random Forest Regressor +2024-05-01 15:01:16,258:INFO:Total runtime is 5.363449354966481 minutes +2024-05-01 15:01:16,415:INFO:SubProcess create_model() called ================================== +2024-05-01 15:01:16,418:INFO:Initializing create_model() +2024-05-01 15:01:16,424:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:01:16,426:INFO:Checking exceptions +2024-05-01 15:01:16,435:INFO:Importing libraries +2024-05-01 15:01:16,437:INFO:Copying training dataset +2024-05-01 15:01:16,676:INFO:Defining folds +2024-05-01 15:01:16,678:INFO:Declaring metric variables +2024-05-01 15:01:16,788:INFO:Importing untrained model +2024-05-01 15:01:17,026:INFO:Random Forest Regressor Imported successfully +2024-05-01 15:01:17,254:INFO:Starting cross validation +2024-05-01 15:01:17,271:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:03:18,467:INFO:Calculating mean and std +2024-05-01 15:03:18,477:INFO:Creating metrics dataframe +2024-05-01 15:03:18,507:INFO:Uploading results into container +2024-05-01 15:03:18,512:INFO:Uploading model into container now +2024-05-01 15:03:18,515:INFO:_master_model_container: 13 +2024-05-01 15:03:18,515:INFO:_display_container: 2 +2024-05-01 15:03:18,519:INFO:RandomForestRegressor(n_jobs=-1, random_state=2822) +2024-05-01 15:03:18,521:INFO:create_model() successfully completed...................................... +2024-05-01 15:03:18,954:INFO:SubProcess create_model() end ================================== +2024-05-01 15:03:18,955:INFO:Creating metrics dataframe +2024-05-01 15:03:19,118:INFO:Initializing Extra Trees Regressor +2024-05-01 15:03:19,119:INFO:Total runtime is 7.4111259778340655 minutes +2024-05-01 15:03:19,156:INFO:SubProcess create_model() called ================================== +2024-05-01 15:03:19,160:INFO:Initializing create_model() +2024-05-01 15:03:19,165:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:03:19,167:INFO:Checking exceptions +2024-05-01 15:03:19,168:INFO:Importing libraries +2024-05-01 15:03:19,168:INFO:Copying training dataset +2024-05-01 15:03:19,234:INFO:Defining folds +2024-05-01 15:03:19,236:INFO:Declaring metric variables +2024-05-01 15:03:19,281:INFO:Importing untrained model +2024-05-01 15:03:19,391:INFO:Extra Trees Regressor Imported successfully +2024-05-01 15:03:19,487:INFO:Starting cross validation +2024-05-01 15:03:19,506:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:04:03,357:INFO:Calculating mean and std +2024-05-01 15:04:03,367:INFO:Creating metrics dataframe +2024-05-01 15:04:03,442:INFO:Uploading results into container +2024-05-01 15:04:03,454:INFO:Uploading model into container now +2024-05-01 15:04:03,460:INFO:_master_model_container: 14 +2024-05-01 15:04:03,461:INFO:_display_container: 2 +2024-05-01 15:04:03,466:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=2822) +2024-05-01 15:04:03,468:INFO:create_model() successfully completed...................................... +2024-05-01 15:04:04,099:INFO:SubProcess create_model() end ================================== +2024-05-01 15:04:04,102:INFO:Creating metrics dataframe +2024-05-01 15:04:04,398:INFO:Initializing AdaBoost Regressor +2024-05-01 15:04:04,400:INFO:Total runtime is 8.165802868207296 minutes +2024-05-01 15:04:04,476:INFO:SubProcess create_model() called ================================== +2024-05-01 15:04:04,479:INFO:Initializing create_model() +2024-05-01 15:04:04,483:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:04:04,484:INFO:Checking exceptions +2024-05-01 15:04:04,485:INFO:Importing libraries +2024-05-01 15:04:04,486:INFO:Copying training dataset +2024-05-01 15:04:04,627:INFO:Defining folds +2024-05-01 15:04:04,628:INFO:Declaring metric variables +2024-05-01 15:04:04,709:INFO:Importing untrained model +2024-05-01 15:04:04,776:INFO:AdaBoost Regressor Imported successfully +2024-05-01 15:04:04,937:INFO:Starting cross validation +2024-05-01 15:04:04,962:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:04:31,473:INFO:Calculating mean and std +2024-05-01 15:04:31,483:INFO:Creating metrics dataframe +2024-05-01 15:04:31,585:INFO:Uploading results into container +2024-05-01 15:04:31,590:INFO:Uploading model into container now +2024-05-01 15:04:31,593:INFO:_master_model_container: 15 +2024-05-01 15:04:31,594:INFO:_display_container: 2 +2024-05-01 15:04:31,596:INFO:AdaBoostRegressor(random_state=2822) +2024-05-01 15:04:31,597:INFO:create_model() successfully completed...................................... +2024-05-01 15:04:32,586:INFO:SubProcess create_model() end ================================== +2024-05-01 15:04:32,587:INFO:Creating metrics dataframe +2024-05-01 15:04:32,819:INFO:Initializing Gradient Boosting Regressor +2024-05-01 15:04:32,820:INFO:Total runtime is 8.639481663703918 minutes +2024-05-01 15:04:32,878:INFO:SubProcess create_model() called ================================== +2024-05-01 15:04:32,880:INFO:Initializing create_model() +2024-05-01 15:04:32,881:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:04:32,882:INFO:Checking exceptions +2024-05-01 15:04:32,883:INFO:Importing libraries +2024-05-01 15:04:32,883:INFO:Copying training dataset +2024-05-01 15:04:33,028:INFO:Defining folds +2024-05-01 15:04:33,030:INFO:Declaring metric variables +2024-05-01 15:04:33,062:INFO:Importing untrained model +2024-05-01 15:04:33,126:INFO:Gradient Boosting Regressor Imported successfully +2024-05-01 15:04:33,330:INFO:Starting cross validation +2024-05-01 15:04:33,338:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:05:12,545:INFO:Calculating mean and std +2024-05-01 15:05:12,556:INFO:Creating metrics dataframe +2024-05-01 15:05:12,586:INFO:Uploading results into container +2024-05-01 15:05:12,590:INFO:Uploading model into container now +2024-05-01 15:05:12,593:INFO:_master_model_container: 16 +2024-05-01 15:05:12,594:INFO:_display_container: 2 +2024-05-01 15:05:12,597:INFO:GradientBoostingRegressor(random_state=2822) +2024-05-01 15:05:12,599:INFO:create_model() successfully completed...................................... +2024-05-01 15:05:12,943:INFO:SubProcess create_model() end ================================== +2024-05-01 15:05:12,944:INFO:Creating metrics dataframe +2024-05-01 15:05:13,099:INFO:Initializing Extreme Gradient Boosting +2024-05-01 15:05:13,100:INFO:Total runtime is 9.310817774136861 minutes +2024-05-01 15:05:13,135:INFO:SubProcess create_model() called ================================== +2024-05-01 15:05:13,137:INFO:Initializing create_model() +2024-05-01 15:05:13,138:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:05:13,139:INFO:Checking exceptions +2024-05-01 15:05:13,140:INFO:Importing libraries +2024-05-01 15:05:13,141:INFO:Copying training dataset +2024-05-01 15:05:13,194:INFO:Defining folds +2024-05-01 15:05:13,194:INFO:Declaring metric variables +2024-05-01 15:05:13,229:INFO:Importing untrained model +2024-05-01 15:05:13,270:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 15:05:13,332:INFO:Starting cross validation +2024-05-01 15:05:13,341:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:05:37,986:INFO:Calculating mean and std +2024-05-01 15:05:37,996:INFO:Creating metrics dataframe +2024-05-01 15:05:38,025:INFO:Uploading results into container +2024-05-01 15:05:38,029:INFO:Uploading model into container now +2024-05-01 15:05:38,032:INFO:_master_model_container: 17 +2024-05-01 15:05:38,032:INFO:_display_container: 2 +2024-05-01 15:05:38,043: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, predictor=None, random_state=2822, + reg_alpha=None, reg_lambda=None, ...) +2024-05-01 15:05:38,045:INFO:create_model() successfully completed...................................... +2024-05-01 15:05:38,397:INFO:SubProcess create_model() end ================================== +2024-05-01 15:05:38,398:INFO:Creating metrics dataframe +2024-05-01 15:05:38,554:INFO:Initializing Light Gradient Boosting Machine +2024-05-01 15:05:38,556:INFO:Total runtime is 9.735066290696462 minutes +2024-05-01 15:05:38,592:INFO:SubProcess create_model() called ================================== +2024-05-01 15:05:38,595:INFO:Initializing create_model() +2024-05-01 15:05:38,595:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:05:38,596:INFO:Checking exceptions +2024-05-01 15:05:38,597:INFO:Importing libraries +2024-05-01 15:05:38,597:INFO:Copying training dataset +2024-05-01 15:05:38,651:INFO:Defining folds +2024-05-01 15:05:38,651:INFO:Declaring metric variables +2024-05-01 15:05:38,685:INFO:Importing untrained model +2024-05-01 15:05:38,719:INFO:Light Gradient Boosting Machine Imported successfully +2024-05-01 15:05:38,781:INFO:Starting cross validation +2024-05-01 15:05:38,789:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:05:49,113:INFO:Calculating mean and std +2024-05-01 15:05:49,124:INFO:Creating metrics dataframe +2024-05-01 15:05:49,171:INFO:Uploading results into container +2024-05-01 15:05:49,175:INFO:Uploading model into container now +2024-05-01 15:05:49,178:INFO:_master_model_container: 18 +2024-05-01 15:05:49,178:INFO:_display_container: 2 +2024-05-01 15:05:49,181:INFO:LGBMRegressor(n_jobs=-1, random_state=2822) +2024-05-01 15:05:49,182:INFO:create_model() successfully completed...................................... +2024-05-01 15:05:49,563:INFO:SubProcess create_model() end ================================== +2024-05-01 15:05:49,564:INFO:Creating metrics dataframe +2024-05-01 15:05:49,714:INFO:Initializing Dummy Regressor +2024-05-01 15:05:49,716:INFO:Total runtime is 9.92106040318807 minutes +2024-05-01 15:05:49,752:INFO:SubProcess create_model() called ================================== +2024-05-01 15:05:49,754:INFO:Initializing create_model() +2024-05-01 15:05:49,755:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:05:49,755:INFO:Checking exceptions +2024-05-01 15:05:49,756:INFO:Importing libraries +2024-05-01 15:05:49,757:INFO:Copying training dataset +2024-05-01 15:05:49,818:INFO:Defining folds +2024-05-01 15:05:49,818:INFO:Declaring metric variables +2024-05-01 15:05:49,854:INFO:Importing untrained model +2024-05-01 15:05:49,891:INFO:Dummy Regressor Imported successfully +2024-05-01 15:05:49,970:INFO:Starting cross validation +2024-05-01 15:05:49,988:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:05:54,218:INFO:Calculating mean and std +2024-05-01 15:05:54,227:INFO:Creating metrics dataframe +2024-05-01 15:05:54,260:INFO:Uploading results into container +2024-05-01 15:05:54,264:INFO:Uploading model into container now +2024-05-01 15:05:54,268:INFO:_master_model_container: 19 +2024-05-01 15:05:54,269:INFO:_display_container: 2 +2024-05-01 15:05:54,276:INFO:DummyRegressor() +2024-05-01 15:05:54,276:INFO:create_model() successfully completed...................................... +2024-05-01 15:05:54,957:INFO:SubProcess create_model() end ================================== +2024-05-01 15:05:54,958:INFO:Creating metrics dataframe +2024-05-01 15:05:55,241:INFO:Initializing create_model() +2024-05-01 15:05:55,241:INFO:create_model(self=, estimator=LGBMRegressor(n_jobs=-1, random_state=2822), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:05:55,242:INFO:Checking exceptions +2024-05-01 15:05:55,262:INFO:Importing libraries +2024-05-01 15:05:55,263:INFO:Copying training dataset +2024-05-01 15:05:55,326:INFO:Defining folds +2024-05-01 15:05:55,327:INFO:Declaring metric variables +2024-05-01 15:05:55,330:INFO:Importing untrained model +2024-05-01 15:05:55,331:INFO:Declaring custom model +2024-05-01 15:05:55,342:INFO:Light Gradient Boosting Machine Imported successfully +2024-05-01 15:05:55,350:INFO:Cross validation set to False +2024-05-01 15:05:55,350:INFO:Fitting Model +2024-05-01 15:05:55,665:INFO:[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003449 seconds. +2024-05-01 15:05:55,666:INFO:You can set `force_col_wise=true` to remove the overhead. +2024-05-01 15:05:55,667:INFO:[LightGBM] [Info] Total Bins 1098 +2024-05-01 15:05:55,668:INFO:[LightGBM] [Info] Number of data points in the train set: 4504, number of used features: 9 +2024-05-01 15:05:55,671:INFO:[LightGBM] [Info] Start training from score 1047483.650762 +2024-05-01 15:05:56,642:INFO:LGBMRegressor(n_jobs=-1, random_state=2822) +2024-05-01 15:05:56,643:INFO:create_model() successfully completed...................................... +2024-05-01 15:05:58,179:INFO:_master_model_container: 19 +2024-05-01 15:05:58,179:INFO:_display_container: 2 +2024-05-01 15:05:58,185:INFO:LGBMRegressor(n_jobs=-1, random_state=2822) +2024-05-01 15:05:58,186:INFO:compare_models() successfully completed...................................... +2024-05-01 15:05:58,575:INFO:Initializing create_model() +2024-05-01 15:05:58,576:INFO:create_model(self=, estimator=xgboost, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:05:58,577:INFO:Checking exceptions +2024-05-01 15:05:58,862:INFO:Importing libraries +2024-05-01 15:05:58,876:INFO:Copying training dataset +2024-05-01 15:05:59,124:INFO:Defining folds +2024-05-01 15:05:59,126:INFO:Declaring metric variables +2024-05-01 15:05:59,277:INFO:Importing untrained model +2024-05-01 15:05:59,451:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 15:05:59,577:INFO:Starting cross validation +2024-05-01 15:05:59,586:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:06:37,495:INFO:Calculating mean and std +2024-05-01 15:06:37,506:INFO:Creating metrics dataframe +2024-05-01 15:06:37,576:INFO:Finalizing model +2024-05-01 15:06:42,892:INFO:Uploading results into container +2024-05-01 15:06:42,899:INFO:Uploading model into container now +2024-05-01 15:06:43,053:INFO:_master_model_container: 20 +2024-05-01 15:06:43,055:INFO:_display_container: 3 +2024-05-01 15:06:43,085: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, predictor='auto', + random_state=2822, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 15:06:43,086:INFO:create_model() successfully completed...................................... +2024-05-01 15:06:44,056:INFO:Initializing evaluate_model() +2024-05-01 15:06:44,057:INFO:evaluate_model(self=, estimator=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, predictor='auto', + random_state=2822, reg_alpha=0, reg_lambda=1, ...), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) +2024-05-01 15:06:44,502:INFO:Initializing plot_model() +2024-05-01 15:06:44,503:INFO:plot_model(self=, estimator=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, predictor='auto', + random_state=2822, reg_alpha=0, reg_lambda=1, ...), plot=pipeline, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-05-01 15:06:44,503:INFO:Checking exceptions +2024-05-01 15:06:44,716:INFO:Preloading libraries +2024-05-01 15:06:44,930:INFO:Copying training dataset +2024-05-01 15:06:44,931:INFO:Plot type: pipeline +2024-05-01 15:06:49,995:INFO:Visual Rendered Successfully +2024-05-01 15:06:50,631:INFO:plot_model() successfully completed...................................... +2024-05-01 15:06:50,937:INFO:Initializing tune_model() +2024-05-01 15:06:50,937:INFO:tune_model(self=, estimator=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, predictor='auto', + random_state=2822, reg_alpha=0, reg_lambda=1, ...), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=optuna, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-05-01 15:06:50,938:INFO:Checking exceptions +2024-05-01 15:06:50,939:INFO:Soft dependency imported: optuna: 3.6.1 +2024-05-01 15:06:59,568:INFO:Copying training dataset +2024-05-01 15:06:59,608:INFO:Checking base model +2024-05-01 15:06:59,609:INFO:Base model : Extreme Gradient Boosting +2024-05-01 15:06:59,720:INFO:Declaring metric variables +2024-05-01 15:06:59,778:INFO:Defining Hyperparameters +2024-05-01 15:07:00,684:INFO:Tuning with n_jobs=-1 +2024-05-01 15:07:00,893:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\optuna\samplers\_tpe\sampler.py:319: ExperimentalWarning: ``multivariate`` option is an experimental feature. The interface can change in the future. + warnings.warn( + +2024-05-01 15:07:00,893:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\optuna\samplers\_tpe\sampler.py:338: ExperimentalWarning: ``constant_liar`` option is an experimental feature. The interface can change in the future. + warnings.warn( + +2024-05-01 15:07:00,897:INFO:Initializing optuna.integration.OptunaSearchCV +2024-05-01 15:07:01,734:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:2458: ExperimentalWarning: OptunaSearchCV is experimental (supported from v0.17.0). The interface can change in the future. + model_grid = optuna.integration.OptunaSearchCV( # type: ignore + +2024-05-01 15:14:29,406:INFO:best_params: {'actual_estimator__learning_rate': 0.02103998446883122, 'actual_estimator__n_estimators': 292, 'actual_estimator__subsample': 0.5816346515297148, 'actual_estimator__max_depth': 6, 'actual_estimator__colsample_bytree': 0.8703102008721908, 'actual_estimator__min_child_weight': 2, 'actual_estimator__reg_alpha': 9.202237091158391e-06, 'actual_estimator__reg_lambda': 3.489938591019268e-07, 'actual_estimator__scale_pos_weight': 28.951306294169118} +2024-05-01 15:14:29,437:INFO:Hyperparameter search completed +2024-05-01 15:14:29,438:INFO:SubProcess create_model() called ================================== +2024-05-01 15:14:29,481:INFO:Initializing create_model() +2024-05-01 15:14:29,482:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=2822, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'learning_rate': 0.02103998446883122, 'n_estimators': 292, 'subsample': 0.5816346515297148, 'max_depth': 6, 'colsample_bytree': 0.8703102008721908, 'min_child_weight': 2, 'reg_alpha': 9.202237091158391e-06, 'reg_lambda': 3.489938591019268e-07, 'scale_pos_weight': 28.951306294169118}) +2024-05-01 15:14:29,483:INFO:Checking exceptions +2024-05-01 15:14:29,483:INFO:Importing libraries +2024-05-01 15:14:29,484:INFO:Copying training dataset +2024-05-01 15:14:29,536:INFO:Defining folds +2024-05-01 15:14:29,537:INFO:Declaring metric variables +2024-05-01 15:14:29,574:INFO:Importing untrained model +2024-05-01 15:14:29,575:INFO:Declaring custom model +2024-05-01 15:14:29,621:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 15:14:29,689:INFO:Starting cross validation +2024-05-01 15:14:29,697:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:16:40,147:INFO:Calculating mean and std +2024-05-01 15:16:40,162:INFO:Creating metrics dataframe +2024-05-01 15:16:40,266:INFO:Finalizing model +2024-05-01 15:17:05,682:INFO:Uploading results into container +2024-05-01 15:17:05,689:INFO:Uploading model into container now +2024-05-01 15:17:05,696:INFO:_master_model_container: 21 +2024-05-01 15:17:05,697:INFO:_display_container: 4 +2024-05-01 15:17:05,751:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, + colsample_bytree=0.8703102008721908, 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=0.02103998446883122, + max_bin=256, max_cat_to_onehot=4, max_delta_step=0, max_depth=6, + max_leaves=0, min_child_weight=2, missing=nan, + monotone_constraints='()', n_estimators=292, n_jobs=-1, + num_parallel_tree=1, predictor='auto', random_state=2822, + reg_alpha=9.202237091158391e-06, reg_lambda=3.489938591019268e-07, ...) +2024-05-01 15:17:05,752:INFO:create_model() successfully completed...................................... +2024-05-01 15:17:06,215:INFO:SubProcess create_model() end ================================== +2024-05-01 15:17:06,216:INFO:choose_better activated +2024-05-01 15:17:06,256:INFO:SubProcess create_model() called ================================== +2024-05-01 15:17:06,286:INFO:Initializing create_model() +2024-05-01 15:17:06,287:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=2822, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:17:06,288:INFO:Checking exceptions +2024-05-01 15:17:06,303:INFO:Importing libraries +2024-05-01 15:17:06,303:INFO:Copying training dataset +2024-05-01 15:17:06,346:INFO:Defining folds +2024-05-01 15:17:06,348:INFO:Declaring metric variables +2024-05-01 15:17:06,349:INFO:Importing untrained model +2024-05-01 15:17:06,350:INFO:Declaring custom model +2024-05-01 15:17:06,378:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 15:17:06,381:INFO:Starting cross validation +2024-05-01 15:17:06,390:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:17:27,815:INFO:Calculating mean and std +2024-05-01 15:17:27,826:INFO:Creating metrics dataframe +2024-05-01 15:17:27,847:INFO:Finalizing model +2024-05-01 15:17:31,225:INFO:Uploading results into container +2024-05-01 15:17:31,229:INFO:Uploading model into container now +2024-05-01 15:17:31,232:INFO:_master_model_container: 22 +2024-05-01 15:17:31,232:INFO:_display_container: 5 +2024-05-01 15:17:31,258: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, predictor='auto', + random_state=2822, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 15:17:31,259:INFO:create_model() successfully completed...................................... +2024-05-01 15:17:31,680:INFO:SubProcess create_model() end ================================== +2024-05-01 15:17:31,704: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, predictor='auto', + random_state=2822, reg_alpha=0, reg_lambda=1, ...) result for R2 is 0.9734 +2024-05-01 15:17:31,729:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, + colsample_bytree=0.8703102008721908, 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=0.02103998446883122, + max_bin=256, max_cat_to_onehot=4, max_delta_step=0, max_depth=6, + max_leaves=0, min_child_weight=2, missing=nan, + monotone_constraints='()', n_estimators=292, n_jobs=-1, + num_parallel_tree=1, predictor='auto', random_state=2822, + reg_alpha=9.202237091158391e-06, reg_lambda=3.489938591019268e-07, ...) result for R2 is 0.9595 +2024-05-01 15:17:31,755: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, predictor='auto', + random_state=2822, reg_alpha=0, reg_lambda=1, ...) is best model +2024-05-01 15:17:31,756:INFO:choose_better completed +2024-05-01 15:17:31,759:INFO:Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one). +2024-05-01 15:17:31,892:INFO:_master_model_container: 22 +2024-05-01 15:17:31,893:INFO:_display_container: 4 +2024-05-01 15:17:31,926: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, predictor='auto', + random_state=2822, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 15:17:31,927:INFO:tune_model() successfully completed...................................... +2024-05-01 15:17:34,406:INFO:Initializing predict_model() +2024-05-01 15:17:34,406:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=2822, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000002941599A660>) +2024-05-01 15:17:34,407:INFO:Checking exceptions +2024-05-01 15:17:34,408:INFO:Preloading libraries +2024-05-01 15:17:34,457:INFO:Set up data. +2024-05-01 15:17:34,490:INFO:Set up index. +2024-05-01 15:19:18,183:INFO:Initializing predict_model() +2024-05-01 15:19:18,252:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=2822, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000029415834180>) +2024-05-01 15:19:18,254:INFO:Checking exceptions +2024-05-01 15:19:18,254:INFO:Preloading libraries +2024-05-01 15:19:18,271:INFO:Set up data. +2024-05-01 15:19:18,338:INFO:Set up index. +2024-05-01 15:27:30,614:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 15:27:30,615:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 15:27:30,615:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 15:27:30,615:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 15:27:33,643:INFO:PyCaret RegressionExperiment +2024-05-01 15:27:33,643:INFO:Logging name: reg-default-name +2024-05-01 15:27:33,643:INFO:ML Usecase: MLUsecase.REGRESSION +2024-05-01 15:27:33,644:INFO:version 3.3.0 +2024-05-01 15:27:33,644:INFO:Initializing setup() +2024-05-01 15:27:33,644:INFO:self.USI: 96ec +2024-05-01 15:27:33,644:INFO:self._variable_keys: {'exp_id', 'idx', 'exp_name_log', 'seed', 'html_param', 'fold_generator', 'logging_param', 'USI', 'y_test', 'pipeline', 'n_jobs_param', 'log_plots_param', 'memory', 'y', 'target_param', 'fold_shuffle_param', 'X', 'y_train', 'fold_groups_param', 'data', 'transform_target_param', 'gpu_param', '_available_plots', '_ml_usecase', 'gpu_n_jobs_param', 'X_test', 'X_train'} +2024-05-01 15:27:33,644:INFO:Checking environment +2024-05-01 15:27:33,644:INFO:python_version: 3.11.0 +2024-05-01 15:27:33,645:INFO:python_build: ('main', 'Oct 24 2022 18:26:48') +2024-05-01 15:27:33,645:INFO:machine: AMD64 +2024-05-01 15:27:33,645:INFO:platform: Windows-10-10.0.22000-SP0 +2024-05-01 15:27:33,652:INFO:Memory: svmem(total=8467492864, available=1913880576, percent=77.4, used=6553612288, free=1913880576) +2024-05-01 15:27:33,652:INFO:Physical Core: 2 +2024-05-01 15:27:33,653:INFO:Logical Core: 4 +2024-05-01 15:27:33,653:INFO:Checking libraries +2024-05-01 15:27:33,653:INFO:System: +2024-05-01 15:27:33,653:INFO: python: 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] +2024-05-01 15:27:33,653:INFO:executable: c:\Users\Jason\Documents\DataB3\.venv\Scripts\python.exe +2024-05-01 15:27:33,653:INFO: machine: Windows-10-10.0.22000-SP0 +2024-05-01 15:27:33,653:INFO:PyCaret required dependencies: +2024-05-01 15:27:33,723:INFO: pip: 24.0 +2024-05-01 15:27:33,724:INFO: setuptools: 65.5.0 +2024-05-01 15:27:33,724:INFO: pycaret: 3.3.0 +2024-05-01 15:27:33,724:INFO: IPython: 8.23.0 +2024-05-01 15:27:33,724:INFO: ipywidgets: 8.1.2 +2024-05-01 15:27:33,724:INFO: tqdm: 4.66.2 +2024-05-01 15:27:33,724:INFO: numpy: 1.24.4 +2024-05-01 15:27:33,725:INFO: pandas: 1.5.3 +2024-05-01 15:27:33,725:INFO: jinja2: 3.1.3 +2024-05-01 15:27:33,725:INFO: scipy: 1.11.4 +2024-05-01 15:27:33,726:INFO: joblib: 1.3.2 +2024-05-01 15:27:33,726:INFO: sklearn: 1.4.1.post1 +2024-05-01 15:27:33,729:INFO: pyod: 1.1.3 +2024-05-01 15:27:33,729:INFO: imblearn: 0.12.2 +2024-05-01 15:27:33,729:INFO: category_encoders: 2.6.3 +2024-05-01 15:27:33,730:INFO: lightgbm: 4.3.0 +2024-05-01 15:27:33,730:INFO: numba: 0.59.1 +2024-05-01 15:27:33,730:INFO: requests: 2.31.0 +2024-05-01 15:27:33,730:INFO: matplotlib: 3.8.3 +2024-05-01 15:27:33,730:INFO: scikitplot: 0.3.7 +2024-05-01 15:27:33,730:INFO: yellowbrick: 1.5 +2024-05-01 15:27:33,730:INFO: plotly: 5.20.0 +2024-05-01 15:27:33,730:INFO: plotly-resampler: Not installed +2024-05-01 15:27:33,731:INFO: kaleido: 0.2.1 +2024-05-01 15:27:33,731:INFO: schemdraw: 0.15 +2024-05-01 15:27:33,731:INFO: statsmodels: 0.14.1 +2024-05-01 15:27:33,732:INFO: sktime: 0.28.0 +2024-05-01 15:27:33,732:INFO: tbats: 1.1.3 +2024-05-01 15:27:33,732:INFO: pmdarima: 2.0.4 +2024-05-01 15:27:33,733:INFO: psutil: 5.9.8 +2024-05-01 15:27:33,733:INFO: markupsafe: 2.1.5 +2024-05-01 15:27:33,733:INFO: pickle5: Not installed +2024-05-01 15:27:33,733:INFO: cloudpickle: 3.0.0 +2024-05-01 15:27:33,733:INFO: deprecation: 2.1.0 +2024-05-01 15:27:33,734:INFO: xxhash: 3.4.1 +2024-05-01 15:27:33,734:INFO: wurlitzer: Not installed +2024-05-01 15:27:33,734:INFO:PyCaret optional dependencies: +2024-05-01 15:27:33,761:INFO: shap: Not installed +2024-05-01 15:27:33,762:INFO: interpret: Not installed +2024-05-01 15:27:33,762:INFO: umap: Not installed +2024-05-01 15:27:33,762:INFO: ydata_profiling: 4.7.0 +2024-05-01 15:27:33,762:INFO: explainerdashboard: Not installed +2024-05-01 15:27:33,762:INFO: autoviz: Not installed +2024-05-01 15:27:33,762:INFO: fairlearn: Not installed +2024-05-01 15:27:33,762:INFO: deepchecks: Not installed +2024-05-01 15:27:33,762:INFO: xgboost: 1.6.2 +2024-05-01 15:27:33,762:INFO: catboost: Not installed +2024-05-01 15:27:33,762:INFO: kmodes: Not installed +2024-05-01 15:27:33,762:INFO: mlxtend: Not installed +2024-05-01 15:27:33,762:INFO: statsforecast: Not installed +2024-05-01 15:27:33,763:INFO: tune_sklearn: Not installed +2024-05-01 15:27:33,763:INFO: ray: Not installed +2024-05-01 15:27:33,763:INFO: hyperopt: Not installed +2024-05-01 15:27:33,763:INFO: optuna: 3.6.1 +2024-05-01 15:27:33,763:INFO: skopt: Not installed +2024-05-01 15:27:33,763:INFO: mlflow: Not installed +2024-05-01 15:27:33,763:INFO: gradio: Not installed +2024-05-01 15:27:33,763:INFO: fastapi: Not installed +2024-05-01 15:27:33,763:INFO: uvicorn: Not installed +2024-05-01 15:27:33,763:INFO: m2cgen: Not installed +2024-05-01 15:27:33,763:INFO: evidently: Not installed +2024-05-01 15:27:33,763:INFO: fugue: Not installed +2024-05-01 15:27:33,763:INFO: streamlit: 1.33.0 +2024-05-01 15:27:33,763:INFO: prophet: 1.1.5 +2024-05-01 15:27:33,764:INFO:None +2024-05-01 15:27:33,764:INFO:Set up data. +2024-05-01 15:27:33,776:INFO:Set up folding strategy. +2024-05-01 15:27:33,776:INFO:Set up train/test split. +2024-05-01 15:27:33,853:INFO:Set up index. +2024-05-01 15:27:33,853:INFO:Assigning column types. +2024-05-01 15:27:33,863:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-05-01 15:27:33,864:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 15:27:33,878:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 15:27:33,891:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 15:27:34,044:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:27:34,196:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:27:34,197:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:27:34,414:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:27:34,415:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 15:27:34,435:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 15:27:34,448:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 15:27:34,630:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:27:34,780:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:27:34,781:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:27:34,788:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:27:34,789:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-05-01 15:27:34,806:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 15:27:34,820:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 15:27:35,012:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:27:35,137:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:27:35,139:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:27:35,146:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:27:35,161:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 15:27:35,184:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 15:27:35,373:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:27:35,543:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:27:35,544:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:27:35,550:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:27:35,551:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-05-01 15:27:35,581:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 15:27:35,749:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:27:35,885:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:27:35,886:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:27:35,894:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:27:35,920:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 15:27:36,087:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:27:36,227:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:27:36,229:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:27:36,241:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:27:36,242:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-05-01 15:27:36,510:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:27:36,654:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:27:36,655:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:27:36,663:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:27:36,864:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:27:37,012:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:27:37,013:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:27:37,019:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:27:37,020:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-05-01 15:27:37,229:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:27:37,392:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:27:37,402:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:27:37,638:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:27:37,785:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:27:37,795:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:27:37,795:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-05-01 15:27:38,144:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:27:38,155:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:27:38,578:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:27:38,586:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:27:38,588:INFO:Preparing preprocessing pipeline... +2024-05-01 15:27:38,588:INFO:Set up simple imputation. +2024-05-01 15:27:38,619:INFO:Set up encoding of categorical features. +2024-05-01 15:27:38,619:INFO:Set up feature normalization. +2024-05-01 15:27:38,777:INFO:Finished creating preprocessing pipeline. +2024-05-01 15:27:38,794:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\Jason\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['Date'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['Date'], + transformer=TargetEncoder(cols=['Date'], + handle_missing='return_nan'))), + ('normalize', TransformerWrapper(transformer=MinMaxScaler()))]) +2024-05-01 15:27:38,794:INFO:Creating final display dataframe. +2024-05-01 15:27:39,144:INFO:Setup _display_container: Description Value +0 Session id 2361 +1 Target Daily_Sales +2 Target type Regression +3 Original data shape (6435, 8) +4 Transformed data shape (6435, 8) +5 Transformed train set shape (4504, 8) +6 Transformed test set shape (1931, 8) +7 Numeric features 6 +8 Categorical features 1 +9 Preprocess True +10 Imputation type simple +11 Numeric imputation mean +12 Categorical imputation mode +13 Maximum one-hot encoding 25 +14 Encoding method None +15 Normalize True +16 Normalize method minmax +17 Fold Generator KFold +18 Fold Number 10 +19 CPU Jobs -1 +20 Use GPU False +21 Log Experiment False +22 Experiment Name reg-default-name +23 USI 96ec +2024-05-01 15:27:39,633:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:27:39,641:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:27:39,984:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:27:39,991:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:27:39,992:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour. + warnings.warn( + +2024-05-01 15:27:39,993:INFO:setup() successfully completed in 6.38s............... +2024-05-01 15:27:56,834:INFO:Initializing compare_models() +2024-05-01 15:27:56,834:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-05-01 15:27:56,834:INFO:Checking exceptions +2024-05-01 15:27:56,839:INFO:Preparing display monitor +2024-05-01 15:27:56,928:INFO:Initializing Linear Regression +2024-05-01 15:27:56,928:INFO:Total runtime is 0.0 minutes +2024-05-01 15:27:56,948:INFO:SubProcess create_model() called ================================== +2024-05-01 15:27:56,948:INFO:Initializing create_model() +2024-05-01 15:27:56,949:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:27:56,949:INFO:Checking exceptions +2024-05-01 15:27:56,949:INFO:Importing libraries +2024-05-01 15:27:56,949:INFO:Copying training dataset +2024-05-01 15:27:56,966:INFO:Defining folds +2024-05-01 15:27:56,967:INFO:Declaring metric variables +2024-05-01 15:27:56,977:INFO:Importing untrained model +2024-05-01 15:27:56,988:INFO:Linear Regression Imported successfully +2024-05-01 15:27:57,012:INFO:Starting cross validation +2024-05-01 15:27:57,035:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:28:10,809:INFO:Calculating mean and std +2024-05-01 15:28:10,812:INFO:Creating metrics dataframe +2024-05-01 15:28:10,825:INFO:Uploading results into container +2024-05-01 15:28:10,827:INFO:Uploading model into container now +2024-05-01 15:28:10,836:INFO:_master_model_container: 1 +2024-05-01 15:28:10,836:INFO:_display_container: 2 +2024-05-01 15:28:10,837:INFO:LinearRegression(n_jobs=-1) +2024-05-01 15:28:10,837:INFO:create_model() successfully completed...................................... +2024-05-01 15:28:11,004:INFO:SubProcess create_model() end ================================== +2024-05-01 15:28:11,004:INFO:Creating metrics dataframe +2024-05-01 15:28:11,021:INFO:Initializing Lasso Regression +2024-05-01 15:28:11,021:INFO:Total runtime is 0.23488185405731202 minutes +2024-05-01 15:28:11,030:INFO:SubProcess create_model() called ================================== +2024-05-01 15:28:11,031:INFO:Initializing create_model() +2024-05-01 15:28:11,031:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:28:11,031:INFO:Checking exceptions +2024-05-01 15:28:11,031:INFO:Importing libraries +2024-05-01 15:28:11,031:INFO:Copying training dataset +2024-05-01 15:28:11,041:INFO:Defining folds +2024-05-01 15:28:11,041:INFO:Declaring metric variables +2024-05-01 15:28:11,050:INFO:Importing untrained model +2024-05-01 15:28:11,061:INFO:Lasso Regression Imported successfully +2024-05-01 15:28:11,084:INFO:Starting cross validation +2024-05-01 15:28:11,091:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:28:11,762:INFO:Calculating mean and std +2024-05-01 15:28:11,764:INFO:Creating metrics dataframe +2024-05-01 15:28:11,771:INFO:Uploading results into container +2024-05-01 15:28:11,773:INFO:Uploading model into container now +2024-05-01 15:28:11,775:INFO:_master_model_container: 2 +2024-05-01 15:28:11,776:INFO:_display_container: 2 +2024-05-01 15:28:11,777:INFO:Lasso(random_state=2361) +2024-05-01 15:28:11,777:INFO:create_model() successfully completed...................................... +2024-05-01 15:28:11,911:INFO:SubProcess create_model() end ================================== +2024-05-01 15:28:11,911:INFO:Creating metrics dataframe +2024-05-01 15:28:11,934:INFO:Initializing Ridge Regression +2024-05-01 15:28:11,935:INFO:Total runtime is 0.25010106166203816 minutes +2024-05-01 15:28:11,945:INFO:SubProcess create_model() called ================================== +2024-05-01 15:28:11,946:INFO:Initializing create_model() +2024-05-01 15:28:11,946:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:28:11,946:INFO:Checking exceptions +2024-05-01 15:28:11,946:INFO:Importing libraries +2024-05-01 15:28:11,946:INFO:Copying training dataset +2024-05-01 15:28:11,958:INFO:Defining folds +2024-05-01 15:28:11,959:INFO:Declaring metric variables +2024-05-01 15:28:11,971:INFO:Importing untrained model +2024-05-01 15:28:11,985:INFO:Ridge Regression Imported successfully +2024-05-01 15:28:11,999:INFO:Starting cross validation +2024-05-01 15:28:12,004:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:28:12,696:INFO:Calculating mean and std +2024-05-01 15:28:12,699:INFO:Creating metrics dataframe +2024-05-01 15:28:12,710:INFO:Uploading results into container +2024-05-01 15:28:12,712:INFO:Uploading model into container now +2024-05-01 15:28:12,714:INFO:_master_model_container: 3 +2024-05-01 15:28:12,715:INFO:_display_container: 2 +2024-05-01 15:28:12,716:INFO:Ridge(random_state=2361) +2024-05-01 15:28:12,717:INFO:create_model() successfully completed...................................... +2024-05-01 15:28:12,863:INFO:SubProcess create_model() end ================================== +2024-05-01 15:28:12,863:INFO:Creating metrics dataframe +2024-05-01 15:28:12,895:INFO:Initializing Elastic Net +2024-05-01 15:28:12,895:INFO:Total runtime is 0.2661028504371643 minutes +2024-05-01 15:28:12,907:INFO:SubProcess create_model() called ================================== +2024-05-01 15:28:12,908:INFO:Initializing create_model() +2024-05-01 15:28:12,909:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:28:12,910:INFO:Checking exceptions +2024-05-01 15:28:12,910:INFO:Importing libraries +2024-05-01 15:28:12,910:INFO:Copying training dataset +2024-05-01 15:28:12,925:INFO:Defining folds +2024-05-01 15:28:12,925:INFO:Declaring metric variables +2024-05-01 15:28:12,939:INFO:Importing untrained model +2024-05-01 15:28:12,952:INFO:Elastic Net Imported successfully +2024-05-01 15:28:12,975:INFO:Starting cross validation +2024-05-01 15:28:12,982:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:28:13,930:INFO:Calculating mean and std +2024-05-01 15:28:13,933:INFO:Creating metrics dataframe +2024-05-01 15:28:13,945:INFO:Uploading results into container +2024-05-01 15:28:13,949:INFO:Uploading model into container now +2024-05-01 15:28:13,950:INFO:_master_model_container: 4 +2024-05-01 15:28:13,950:INFO:_display_container: 2 +2024-05-01 15:28:13,951:INFO:ElasticNet(random_state=2361) +2024-05-01 15:28:13,951:INFO:create_model() successfully completed...................................... +2024-05-01 15:28:14,106:INFO:SubProcess create_model() end ================================== +2024-05-01 15:28:14,106:INFO:Creating metrics dataframe +2024-05-01 15:28:14,130:INFO:Initializing Least Angle Regression +2024-05-01 15:28:14,131:INFO:Total runtime is 0.28670042753219604 minutes +2024-05-01 15:28:14,137:INFO:SubProcess create_model() called ================================== +2024-05-01 15:28:14,138:INFO:Initializing create_model() +2024-05-01 15:28:14,138:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:28:14,138:INFO:Checking exceptions +2024-05-01 15:28:14,139:INFO:Importing libraries +2024-05-01 15:28:14,139:INFO:Copying training dataset +2024-05-01 15:28:14,154:INFO:Defining folds +2024-05-01 15:28:14,154:INFO:Declaring metric variables +2024-05-01 15:28:14,173:INFO:Importing untrained model +2024-05-01 15:28:14,183:INFO:Least Angle Regression Imported successfully +2024-05-01 15:28:14,202:INFO:Starting cross validation +2024-05-01 15:28:14,209:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:28:14,967:INFO:Calculating mean and std +2024-05-01 15:28:14,970:INFO:Creating metrics dataframe +2024-05-01 15:28:14,976:INFO:Uploading results into container +2024-05-01 15:28:14,977:INFO:Uploading model into container now +2024-05-01 15:28:14,977:INFO:_master_model_container: 5 +2024-05-01 15:28:14,978:INFO:_display_container: 2 +2024-05-01 15:28:14,979:INFO:Lars(random_state=2361) +2024-05-01 15:28:14,979:INFO:create_model() successfully completed...................................... +2024-05-01 15:28:15,114:INFO:SubProcess create_model() end ================================== +2024-05-01 15:28:15,114:INFO:Creating metrics dataframe +2024-05-01 15:28:15,138:INFO:Initializing Lasso Least Angle Regression +2024-05-01 15:28:15,138:INFO:Total runtime is 0.303484853108724 minutes +2024-05-01 15:28:15,146:INFO:SubProcess create_model() called ================================== +2024-05-01 15:28:15,148:INFO:Initializing create_model() +2024-05-01 15:28:15,148:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:28:15,148:INFO:Checking exceptions +2024-05-01 15:28:15,149:INFO:Importing libraries +2024-05-01 15:28:15,149:INFO:Copying training dataset +2024-05-01 15:28:15,159:INFO:Defining folds +2024-05-01 15:28:15,160:INFO:Declaring metric variables +2024-05-01 15:28:15,174:INFO:Importing untrained model +2024-05-01 15:28:15,186:INFO:Lasso Least Angle Regression Imported successfully +2024-05-01 15:28:15,214:INFO:Starting cross validation +2024-05-01 15:28:15,219:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:28:17,407:INFO:Calculating mean and std +2024-05-01 15:28:17,409:INFO:Creating metrics dataframe +2024-05-01 15:28:17,415:INFO:Uploading results into container +2024-05-01 15:28:17,416:INFO:Uploading model into container now +2024-05-01 15:28:17,422:INFO:_master_model_container: 6 +2024-05-01 15:28:17,423:INFO:_display_container: 2 +2024-05-01 15:28:17,424:INFO:LassoLars(random_state=2361) +2024-05-01 15:28:17,424:INFO:create_model() successfully completed...................................... +2024-05-01 15:28:17,621:INFO:SubProcess create_model() end ================================== +2024-05-01 15:28:17,622:INFO:Creating metrics dataframe +2024-05-01 15:28:17,651:INFO:Initializing Orthogonal Matching Pursuit +2024-05-01 15:28:17,651:INFO:Total runtime is 0.3453792254130046 minutes +2024-05-01 15:28:17,663:INFO:SubProcess create_model() called ================================== +2024-05-01 15:28:17,664:INFO:Initializing create_model() +2024-05-01 15:28:17,664:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:28:17,665:INFO:Checking exceptions +2024-05-01 15:28:17,665:INFO:Importing libraries +2024-05-01 15:28:17,665:INFO:Copying training dataset +2024-05-01 15:28:17,677:INFO:Defining folds +2024-05-01 15:28:17,678:INFO:Declaring metric variables +2024-05-01 15:28:17,695:INFO:Importing untrained model +2024-05-01 15:28:17,713:INFO:Orthogonal Matching Pursuit Imported successfully +2024-05-01 15:28:17,747:INFO:Starting cross validation +2024-05-01 15:28:17,750:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:28:18,494:INFO:Calculating mean and std +2024-05-01 15:28:18,496:INFO:Creating metrics dataframe +2024-05-01 15:28:18,502:INFO:Uploading results into container +2024-05-01 15:28:18,506:INFO:Uploading model into container now +2024-05-01 15:28:18,507:INFO:_master_model_container: 7 +2024-05-01 15:28:18,507:INFO:_display_container: 2 +2024-05-01 15:28:18,508:INFO:OrthogonalMatchingPursuit() +2024-05-01 15:28:18,508:INFO:create_model() successfully completed...................................... +2024-05-01 15:28:18,666:INFO:SubProcess create_model() end ================================== +2024-05-01 15:28:18,666:INFO:Creating metrics dataframe +2024-05-01 15:28:18,684:INFO:Initializing Bayesian Ridge +2024-05-01 15:28:18,685:INFO:Total runtime is 0.3626133839289348 minutes +2024-05-01 15:28:18,693:INFO:SubProcess create_model() called ================================== +2024-05-01 15:28:18,693:INFO:Initializing create_model() +2024-05-01 15:28:18,693:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:28:18,693:INFO:Checking exceptions +2024-05-01 15:28:18,694:INFO:Importing libraries +2024-05-01 15:28:18,694:INFO:Copying training dataset +2024-05-01 15:28:18,703:INFO:Defining folds +2024-05-01 15:28:18,703:INFO:Declaring metric variables +2024-05-01 15:28:18,717:INFO:Importing untrained model +2024-05-01 15:28:18,733:INFO:Bayesian Ridge Imported successfully +2024-05-01 15:28:18,763:INFO:Starting cross validation +2024-05-01 15:28:18,771:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:28:19,526:INFO:Calculating mean and std +2024-05-01 15:28:19,535:INFO:Creating metrics dataframe +2024-05-01 15:28:19,550:INFO:Uploading results into container +2024-05-01 15:28:19,553:INFO:Uploading model into container now +2024-05-01 15:28:19,554:INFO:_master_model_container: 8 +2024-05-01 15:28:19,554:INFO:_display_container: 2 +2024-05-01 15:28:19,554:INFO:BayesianRidge() +2024-05-01 15:28:19,554:INFO:create_model() successfully completed...................................... +2024-05-01 15:28:19,702:INFO:SubProcess create_model() end ================================== +2024-05-01 15:28:19,702:INFO:Creating metrics dataframe +2024-05-01 15:28:19,721:INFO:Initializing Passive Aggressive Regressor +2024-05-01 15:28:19,721:INFO:Total runtime is 0.3798805117607117 minutes +2024-05-01 15:28:19,729:INFO:SubProcess create_model() called ================================== +2024-05-01 15:28:19,730:INFO:Initializing create_model() +2024-05-01 15:28:19,731:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:28:19,731:INFO:Checking exceptions +2024-05-01 15:28:19,732:INFO:Importing libraries +2024-05-01 15:28:19,732:INFO:Copying training dataset +2024-05-01 15:28:19,739:INFO:Defining folds +2024-05-01 15:28:19,740:INFO:Declaring metric variables +2024-05-01 15:28:19,752:INFO:Importing untrained model +2024-05-01 15:28:19,763:INFO:Passive Aggressive Regressor Imported successfully +2024-05-01 15:28:19,800:INFO:Starting cross validation +2024-05-01 15:28:19,805:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:28:21,215:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:28:21,215:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:28:21,222:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:28:21,223:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:28:22,539:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:28:22,571:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:28:22,575:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:28:22,603:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:28:23,433:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:28:23,434:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:28:23,487:INFO:Calculating mean and std +2024-05-01 15:28:23,490:INFO:Creating metrics dataframe +2024-05-01 15:28:23,503:INFO:Uploading results into container +2024-05-01 15:28:23,504:INFO:Uploading model into container now +2024-05-01 15:28:23,505:INFO:_master_model_container: 9 +2024-05-01 15:28:23,505:INFO:_display_container: 2 +2024-05-01 15:28:23,505:INFO:PassiveAggressiveRegressor(random_state=2361) +2024-05-01 15:28:23,506:INFO:create_model() successfully completed...................................... +2024-05-01 15:28:23,642:INFO:SubProcess create_model() end ================================== +2024-05-01 15:28:23,643:INFO:Creating metrics dataframe +2024-05-01 15:28:23,666:INFO:Initializing Huber Regressor +2024-05-01 15:28:23,667:INFO:Total runtime is 0.44563616911570236 minutes +2024-05-01 15:28:23,675:INFO:SubProcess create_model() called ================================== +2024-05-01 15:28:23,676:INFO:Initializing create_model() +2024-05-01 15:28:23,676:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:28:23,676:INFO:Checking exceptions +2024-05-01 15:28:23,676:INFO:Importing libraries +2024-05-01 15:28:23,676:INFO:Copying training dataset +2024-05-01 15:28:23,687:INFO:Defining folds +2024-05-01 15:28:23,687:INFO:Declaring metric variables +2024-05-01 15:28:23,703:INFO:Importing untrained model +2024-05-01 15:28:23,718:INFO:Huber Regressor Imported successfully +2024-05-01 15:28:23,761:INFO:Starting cross validation +2024-05-01 15:28:23,765:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:28:24,702:INFO:Calculating mean and std +2024-05-01 15:28:24,704:INFO:Creating metrics dataframe +2024-05-01 15:28:24,717:INFO:Uploading results into container +2024-05-01 15:28:24,718:INFO:Uploading model into container now +2024-05-01 15:28:24,720:INFO:_master_model_container: 10 +2024-05-01 15:28:24,720:INFO:_display_container: 2 +2024-05-01 15:28:24,722:INFO:HuberRegressor() +2024-05-01 15:28:24,722:INFO:create_model() successfully completed...................................... +2024-05-01 15:28:24,874:INFO:SubProcess create_model() end ================================== +2024-05-01 15:28:24,875:INFO:Creating metrics dataframe +2024-05-01 15:28:24,897:INFO:Initializing K Neighbors Regressor +2024-05-01 15:28:24,897:INFO:Total runtime is 0.46614207426706955 minutes +2024-05-01 15:28:24,903:INFO:SubProcess create_model() called ================================== +2024-05-01 15:28:24,904:INFO:Initializing create_model() +2024-05-01 15:28:24,904:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:28:24,904:INFO:Checking exceptions +2024-05-01 15:28:24,904:INFO:Importing libraries +2024-05-01 15:28:24,904:INFO:Copying training dataset +2024-05-01 15:28:24,919:INFO:Defining folds +2024-05-01 15:28:24,920:INFO:Declaring metric variables +2024-05-01 15:28:24,944:INFO:Importing untrained model +2024-05-01 15:28:24,998:INFO:K Neighbors Regressor Imported successfully +2024-05-01 15:28:25,015:INFO:Starting cross validation +2024-05-01 15:28:25,024:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:28:26,443:INFO:Calculating mean and std +2024-05-01 15:28:26,446:INFO:Creating metrics dataframe +2024-05-01 15:28:26,459:INFO:Uploading results into container +2024-05-01 15:28:26,460:INFO:Uploading model into container now +2024-05-01 15:28:26,461:INFO:_master_model_container: 11 +2024-05-01 15:28:26,461:INFO:_display_container: 2 +2024-05-01 15:28:26,461:INFO:KNeighborsRegressor(n_jobs=-1) +2024-05-01 15:28:26,461:INFO:create_model() successfully completed...................................... +2024-05-01 15:28:26,600:INFO:SubProcess create_model() end ================================== +2024-05-01 15:28:26,600:INFO:Creating metrics dataframe +2024-05-01 15:28:26,620:INFO:Initializing Decision Tree Regressor +2024-05-01 15:28:26,621:INFO:Total runtime is 0.4948744336764018 minutes +2024-05-01 15:28:26,630:INFO:SubProcess create_model() called ================================== +2024-05-01 15:28:26,630:INFO:Initializing create_model() +2024-05-01 15:28:26,630:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:28:26,631:INFO:Checking exceptions +2024-05-01 15:28:26,631:INFO:Importing libraries +2024-05-01 15:28:26,631:INFO:Copying training dataset +2024-05-01 15:28:26,642:INFO:Defining folds +2024-05-01 15:28:26,645:INFO:Declaring metric variables +2024-05-01 15:28:26,659:INFO:Importing untrained model +2024-05-01 15:28:26,674:INFO:Decision Tree Regressor Imported successfully +2024-05-01 15:28:26,715:INFO:Starting cross validation +2024-05-01 15:28:26,719:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:28:27,899:INFO:Calculating mean and std +2024-05-01 15:28:27,901:INFO:Creating metrics dataframe +2024-05-01 15:28:27,917:INFO:Uploading results into container +2024-05-01 15:28:27,920:INFO:Uploading model into container now +2024-05-01 15:28:27,920:INFO:_master_model_container: 12 +2024-05-01 15:28:27,920:INFO:_display_container: 2 +2024-05-01 15:28:27,921:INFO:DecisionTreeRegressor(random_state=2361) +2024-05-01 15:28:27,921:INFO:create_model() successfully completed...................................... +2024-05-01 15:28:28,056:INFO:SubProcess create_model() end ================================== +2024-05-01 15:28:28,057:INFO:Creating metrics dataframe +2024-05-01 15:28:28,082:INFO:Initializing Random Forest Regressor +2024-05-01 15:28:28,083:INFO:Total runtime is 0.5192351539929708 minutes +2024-05-01 15:28:28,096:INFO:SubProcess create_model() called ================================== +2024-05-01 15:28:28,096:INFO:Initializing create_model() +2024-05-01 15:28:28,097:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:28:28,097:INFO:Checking exceptions +2024-05-01 15:28:28,097:INFO:Importing libraries +2024-05-01 15:28:28,098:INFO:Copying training dataset +2024-05-01 15:28:28,110:INFO:Defining folds +2024-05-01 15:28:28,110:INFO:Declaring metric variables +2024-05-01 15:28:28,127:INFO:Importing untrained model +2024-05-01 15:28:28,155:INFO:Random Forest Regressor Imported successfully +2024-05-01 15:28:28,181:INFO:Starting cross validation +2024-05-01 15:28:28,188:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:28:54,662:INFO:Calculating mean and std +2024-05-01 15:28:54,668:INFO:Creating metrics dataframe +2024-05-01 15:28:54,690:INFO:Uploading results into container +2024-05-01 15:28:54,692:INFO:Uploading model into container now +2024-05-01 15:28:54,693:INFO:_master_model_container: 13 +2024-05-01 15:28:54,694:INFO:_display_container: 2 +2024-05-01 15:28:54,695:INFO:RandomForestRegressor(n_jobs=-1, random_state=2361) +2024-05-01 15:28:54,695:INFO:create_model() successfully completed...................................... +2024-05-01 15:28:54,976:INFO:SubProcess create_model() end ================================== +2024-05-01 15:28:54,976:INFO:Creating metrics dataframe +2024-05-01 15:28:55,044:INFO:Initializing Extra Trees Regressor +2024-05-01 15:28:55,044:INFO:Total runtime is 0.9685878276824951 minutes +2024-05-01 15:28:55,060:INFO:SubProcess create_model() called ================================== +2024-05-01 15:28:55,062:INFO:Initializing create_model() +2024-05-01 15:28:55,063:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:28:55,065:INFO:Checking exceptions +2024-05-01 15:28:55,065:INFO:Importing libraries +2024-05-01 15:28:55,065:INFO:Copying training dataset +2024-05-01 15:28:55,087:INFO:Defining folds +2024-05-01 15:28:55,088:INFO:Declaring metric variables +2024-05-01 15:28:55,109:INFO:Importing untrained model +2024-05-01 15:28:55,138:INFO:Extra Trees Regressor Imported successfully +2024-05-01 15:28:55,194:INFO:Starting cross validation +2024-05-01 15:28:55,198:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:29:09,295:INFO:Calculating mean and std +2024-05-01 15:29:09,299:INFO:Creating metrics dataframe +2024-05-01 15:29:09,319:INFO:Uploading results into container +2024-05-01 15:29:09,321:INFO:Uploading model into container now +2024-05-01 15:29:09,323:INFO:_master_model_container: 14 +2024-05-01 15:29:09,324:INFO:_display_container: 2 +2024-05-01 15:29:09,325:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=2361) +2024-05-01 15:29:09,325:INFO:create_model() successfully completed...................................... +2024-05-01 15:29:09,533:INFO:SubProcess create_model() end ================================== +2024-05-01 15:29:09,534:INFO:Creating metrics dataframe +2024-05-01 15:29:09,585:INFO:Initializing AdaBoost Regressor +2024-05-01 15:29:09,585:INFO:Total runtime is 1.210941485563914 minutes +2024-05-01 15:29:09,602:INFO:SubProcess create_model() called ================================== +2024-05-01 15:29:09,603:INFO:Initializing create_model() +2024-05-01 15:29:09,603:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:29:09,603:INFO:Checking exceptions +2024-05-01 15:29:09,604:INFO:Importing libraries +2024-05-01 15:29:09,604:INFO:Copying training dataset +2024-05-01 15:29:09,623:INFO:Defining folds +2024-05-01 15:29:09,624:INFO:Declaring metric variables +2024-05-01 15:29:09,643:INFO:Importing untrained model +2024-05-01 15:29:09,666:INFO:AdaBoost Regressor Imported successfully +2024-05-01 15:29:09,703:INFO:Starting cross validation +2024-05-01 15:29:09,707:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:29:13,746:INFO:Calculating mean and std +2024-05-01 15:29:13,755:INFO:Creating metrics dataframe +2024-05-01 15:29:13,771:INFO:Uploading results into container +2024-05-01 15:29:13,773:INFO:Uploading model into container now +2024-05-01 15:29:13,775:INFO:_master_model_container: 15 +2024-05-01 15:29:13,775:INFO:_display_container: 2 +2024-05-01 15:29:13,776:INFO:AdaBoostRegressor(random_state=2361) +2024-05-01 15:29:13,777:INFO:create_model() successfully completed...................................... +2024-05-01 15:29:13,983:INFO:SubProcess create_model() end ================================== +2024-05-01 15:29:13,984:INFO:Creating metrics dataframe +2024-05-01 15:29:14,043:INFO:Initializing Gradient Boosting Regressor +2024-05-01 15:29:14,044:INFO:Total runtime is 1.2852558294932048 minutes +2024-05-01 15:29:14,059:INFO:SubProcess create_model() called ================================== +2024-05-01 15:29:14,060:INFO:Initializing create_model() +2024-05-01 15:29:14,060:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:29:14,061:INFO:Checking exceptions +2024-05-01 15:29:14,061:INFO:Importing libraries +2024-05-01 15:29:14,062:INFO:Copying training dataset +2024-05-01 15:29:14,081:INFO:Defining folds +2024-05-01 15:29:14,082:INFO:Declaring metric variables +2024-05-01 15:29:14,098:INFO:Importing untrained model +2024-05-01 15:29:14,127:INFO:Gradient Boosting Regressor Imported successfully +2024-05-01 15:29:14,173:INFO:Starting cross validation +2024-05-01 15:29:14,178:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:29:21,693:INFO:Calculating mean and std +2024-05-01 15:29:21,699:INFO:Creating metrics dataframe +2024-05-01 15:29:21,715:INFO:Uploading results into container +2024-05-01 15:29:21,717:INFO:Uploading model into container now +2024-05-01 15:29:21,720:INFO:_master_model_container: 16 +2024-05-01 15:29:21,720:INFO:_display_container: 2 +2024-05-01 15:29:21,722:INFO:GradientBoostingRegressor(random_state=2361) +2024-05-01 15:29:21,722:INFO:create_model() successfully completed...................................... +2024-05-01 15:29:21,953:INFO:SubProcess create_model() end ================================== +2024-05-01 15:29:21,954:INFO:Creating metrics dataframe +2024-05-01 15:29:22,016:INFO:Initializing Extreme Gradient Boosting +2024-05-01 15:29:22,016:INFO:Total runtime is 1.4181324442227683 minutes +2024-05-01 15:29:22,029:INFO:SubProcess create_model() called ================================== +2024-05-01 15:29:22,031:INFO:Initializing create_model() +2024-05-01 15:29:22,031:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:29:22,031:INFO:Checking exceptions +2024-05-01 15:29:22,032:INFO:Importing libraries +2024-05-01 15:29:22,032:INFO:Copying training dataset +2024-05-01 15:29:22,059:INFO:Defining folds +2024-05-01 15:29:22,060:INFO:Declaring metric variables +2024-05-01 15:29:22,078:INFO:Importing untrained model +2024-05-01 15:29:22,183:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 15:29:22,221:INFO:Starting cross validation +2024-05-01 15:29:22,225:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:29:30,142:INFO:Calculating mean and std +2024-05-01 15:29:30,146:INFO:Creating metrics dataframe +2024-05-01 15:29:30,167:INFO:Uploading results into container +2024-05-01 15:29:30,169:INFO:Uploading model into container now +2024-05-01 15:29:30,172:INFO:_master_model_container: 17 +2024-05-01 15:29:30,172:INFO:_display_container: 2 +2024-05-01 15:29:30,176: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, predictor=None, random_state=2361, + reg_alpha=None, reg_lambda=None, ...) +2024-05-01 15:29:30,177:INFO:create_model() successfully completed...................................... +2024-05-01 15:29:30,386:INFO:SubProcess create_model() end ================================== +2024-05-01 15:29:30,387:INFO:Creating metrics dataframe +2024-05-01 15:29:30,453:INFO:Initializing Light Gradient Boosting Machine +2024-05-01 15:29:30,454:INFO:Total runtime is 1.5587518334388735 minutes +2024-05-01 15:29:30,471:INFO:SubProcess create_model() called ================================== +2024-05-01 15:29:30,472:INFO:Initializing create_model() +2024-05-01 15:29:30,473:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:29:30,473:INFO:Checking exceptions +2024-05-01 15:29:30,473:INFO:Importing libraries +2024-05-01 15:29:30,474:INFO:Copying training dataset +2024-05-01 15:29:30,492:INFO:Defining folds +2024-05-01 15:29:30,495:INFO:Declaring metric variables +2024-05-01 15:29:30,510:INFO:Importing untrained model +2024-05-01 15:29:30,537:INFO:Light Gradient Boosting Machine Imported successfully +2024-05-01 15:29:30,577:INFO:Starting cross validation +2024-05-01 15:29:30,587:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:29:33,685:INFO:Calculating mean and std +2024-05-01 15:29:33,690:INFO:Creating metrics dataframe +2024-05-01 15:29:33,708:INFO:Uploading results into container +2024-05-01 15:29:33,710:INFO:Uploading model into container now +2024-05-01 15:29:33,711:INFO:_master_model_container: 18 +2024-05-01 15:29:33,712:INFO:_display_container: 2 +2024-05-01 15:29:33,714:INFO:LGBMRegressor(n_jobs=-1, random_state=2361) +2024-05-01 15:29:33,714:INFO:create_model() successfully completed...................................... +2024-05-01 15:29:33,905:INFO:SubProcess create_model() end ================================== +2024-05-01 15:29:33,905:INFO:Creating metrics dataframe +2024-05-01 15:29:33,957:INFO:Initializing Dummy Regressor +2024-05-01 15:29:33,958:INFO:Total runtime is 1.617164242267609 minutes +2024-05-01 15:29:33,967:INFO:SubProcess create_model() called ================================== +2024-05-01 15:29:33,968:INFO:Initializing create_model() +2024-05-01 15:29:33,969:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:29:33,969:INFO:Checking exceptions +2024-05-01 15:29:33,970:INFO:Importing libraries +2024-05-01 15:29:33,970:INFO:Copying training dataset +2024-05-01 15:29:33,984:INFO:Defining folds +2024-05-01 15:29:33,984:INFO:Declaring metric variables +2024-05-01 15:29:34,004:INFO:Importing untrained model +2024-05-01 15:29:34,023:INFO:Dummy Regressor Imported successfully +2024-05-01 15:29:34,059:INFO:Starting cross validation +2024-05-01 15:29:34,064:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:29:34,968:INFO:Calculating mean and std +2024-05-01 15:29:34,977:INFO:Creating metrics dataframe +2024-05-01 15:29:34,994:INFO:Uploading results into container +2024-05-01 15:29:34,997:INFO:Uploading model into container now +2024-05-01 15:29:34,998:INFO:_master_model_container: 19 +2024-05-01 15:29:34,998:INFO:_display_container: 2 +2024-05-01 15:29:34,999:INFO:DummyRegressor() +2024-05-01 15:29:35,000:INFO:create_model() successfully completed...................................... +2024-05-01 15:29:35,221:INFO:SubProcess create_model() end ================================== +2024-05-01 15:29:35,222:INFO:Creating metrics dataframe +2024-05-01 15:29:35,391:INFO:Initializing create_model() +2024-05-01 15:29:35,391:INFO:create_model(self=, estimator=RandomForestRegressor(n_jobs=-1, random_state=2361), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:29:35,391:INFO:Checking exceptions +2024-05-01 15:29:35,400:INFO:Importing libraries +2024-05-01 15:29:35,400:INFO:Copying training dataset +2024-05-01 15:29:35,468:INFO:Defining folds +2024-05-01 15:29:35,469:INFO:Declaring metric variables +2024-05-01 15:29:35,469:INFO:Importing untrained model +2024-05-01 15:29:35,469:INFO:Declaring custom model +2024-05-01 15:29:35,477:INFO:Random Forest Regressor Imported successfully +2024-05-01 15:29:35,482:INFO:Cross validation set to False +2024-05-01 15:29:35,482:INFO:Fitting Model +2024-05-01 15:29:38,480:INFO:RandomForestRegressor(n_jobs=-1, random_state=2361) +2024-05-01 15:29:38,480:INFO:create_model() successfully completed...................................... +2024-05-01 15:29:38,865:INFO:_master_model_container: 19 +2024-05-01 15:29:38,866:INFO:_display_container: 2 +2024-05-01 15:29:38,867:INFO:RandomForestRegressor(n_jobs=-1, random_state=2361) +2024-05-01 15:29:38,867:INFO:compare_models() successfully completed...................................... +2024-05-01 15:29:46,818:INFO:Initializing create_model() +2024-05-01 15:29:46,818:INFO:create_model(self=, estimator=xgboost, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:29:46,818:INFO:Checking exceptions +2024-05-01 15:29:46,851:INFO:Importing libraries +2024-05-01 15:29:46,851:INFO:Copying training dataset +2024-05-01 15:29:46,866:INFO:Defining folds +2024-05-01 15:29:46,866:INFO:Declaring metric variables +2024-05-01 15:29:46,882:INFO:Importing untrained model +2024-05-01 15:29:46,909:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 15:29:46,937:INFO:Starting cross validation +2024-05-01 15:29:46,940:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:29:58,781:INFO:Calculating mean and std +2024-05-01 15:29:58,786:INFO:Creating metrics dataframe +2024-05-01 15:29:58,812:INFO:Finalizing model +2024-05-01 15:30:00,350:INFO:Uploading results into container +2024-05-01 15:30:00,354:INFO:Uploading model into container now +2024-05-01 15:30:00,407:INFO:_master_model_container: 20 +2024-05-01 15:30:00,407:INFO:_display_container: 3 +2024-05-01 15:30:00,429: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, predictor='auto', + random_state=2361, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 15:30:00,430:INFO:create_model() successfully completed...................................... +2024-05-01 15:30:05,605:INFO:Initializing evaluate_model() +2024-05-01 15:30:05,606:INFO:evaluate_model(self=, estimator=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, predictor='auto', + random_state=2361, reg_alpha=0, reg_lambda=1, ...), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) +2024-05-01 15:30:05,635:INFO:Initializing plot_model() +2024-05-01 15:30:05,636:INFO:plot_model(self=, estimator=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, predictor='auto', + random_state=2361, reg_alpha=0, reg_lambda=1, ...), plot=pipeline, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-05-01 15:30:05,636:INFO:Checking exceptions +2024-05-01 15:30:05,646:INFO:Preloading libraries +2024-05-01 15:30:05,661:INFO:Copying training dataset +2024-05-01 15:30:05,661:INFO:Plot type: pipeline +2024-05-01 15:30:06,172:INFO:Visual Rendered Successfully +2024-05-01 15:30:06,318:INFO:plot_model() successfully completed...................................... +2024-05-01 15:30:25,668:INFO:Initializing tune_model() +2024-05-01 15:30:25,669:INFO:tune_model(self=, estimator=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, predictor='auto', + random_state=2361, reg_alpha=0, reg_lambda=1, ...), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=optuna, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-05-01 15:30:25,669:INFO:Checking exceptions +2024-05-01 15:30:25,669:INFO:Soft dependency imported: optuna: 3.6.1 +2024-05-01 15:30:25,983:INFO:Copying training dataset +2024-05-01 15:30:25,997:INFO:Checking base model +2024-05-01 15:30:25,998:INFO:Base model : Extreme Gradient Boosting +2024-05-01 15:30:26,032:INFO:Declaring metric variables +2024-05-01 15:30:26,051:INFO:Defining Hyperparameters +2024-05-01 15:30:26,317:INFO:Tuning with n_jobs=-1 +2024-05-01 15:30:26,319:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\optuna\samplers\_tpe\sampler.py:319: ExperimentalWarning: ``multivariate`` option is an experimental feature. The interface can change in the future. + warnings.warn( + +2024-05-01 15:30:26,322:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\optuna\samplers\_tpe\sampler.py:338: ExperimentalWarning: ``constant_liar`` option is an experimental feature. The interface can change in the future. + warnings.warn( + +2024-05-01 15:30:26,323:INFO:Initializing optuna.integration.OptunaSearchCV +2024-05-01 15:30:26,388:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:2458: ExperimentalWarning: OptunaSearchCV is experimental (supported from v0.17.0). The interface can change in the future. + model_grid = optuna.integration.OptunaSearchCV( # type: ignore + +2024-05-01 15:35:26,034:INFO:best_params: {'actual_estimator__learning_rate': 0.3132665087447957, 'actual_estimator__n_estimators': 291, 'actual_estimator__subsample': 0.67073183095928, 'actual_estimator__max_depth': 6, 'actual_estimator__colsample_bytree': 0.7496867202068435, 'actual_estimator__min_child_weight': 4, 'actual_estimator__reg_alpha': 0.007399227574348389, 'actual_estimator__reg_lambda': 0.14461634411473925, 'actual_estimator__scale_pos_weight': 22.998157197813196} +2024-05-01 15:35:26,074:INFO:Hyperparameter search completed +2024-05-01 15:35:26,075:INFO:SubProcess create_model() called ================================== +2024-05-01 15:35:26,100:INFO:Initializing create_model() +2024-05-01 15:35:26,101:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=2361, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'learning_rate': 0.3132665087447957, 'n_estimators': 291, 'subsample': 0.67073183095928, 'max_depth': 6, 'colsample_bytree': 0.7496867202068435, 'min_child_weight': 4, 'reg_alpha': 0.007399227574348389, 'reg_lambda': 0.14461634411473925, 'scale_pos_weight': 22.998157197813196}) +2024-05-01 15:35:26,103:INFO:Checking exceptions +2024-05-01 15:35:26,104:INFO:Importing libraries +2024-05-01 15:35:26,106:INFO:Copying training dataset +2024-05-01 15:35:26,264:INFO:Defining folds +2024-05-01 15:35:26,266:INFO:Declaring metric variables +2024-05-01 15:35:26,462:INFO:Importing untrained model +2024-05-01 15:35:26,463:INFO:Declaring custom model +2024-05-01 15:35:26,567:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 15:35:27,071:INFO:Starting cross validation +2024-05-01 15:35:27,289:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:38:06,244:INFO:Calculating mean and std +2024-05-01 15:38:06,277:INFO:Creating metrics dataframe +2024-05-01 15:38:06,486:INFO:Finalizing model +2024-05-01 15:38:17,980:INFO:Uploading results into container +2024-05-01 15:38:18,092:INFO:Uploading model into container now +2024-05-01 15:38:18,110:INFO:_master_model_container: 21 +2024-05-01 15:38:18,120:INFO:_display_container: 4 +2024-05-01 15:38:18,239:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, + colsample_bytree=0.7496867202068435, 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=0.3132665087447957, + max_bin=256, max_cat_to_onehot=4, max_delta_step=0, max_depth=6, + max_leaves=0, min_child_weight=4, missing=nan, + monotone_constraints='()', n_estimators=291, n_jobs=-1, + num_parallel_tree=1, predictor='auto', random_state=2361, + reg_alpha=0.007399227574348389, reg_lambda=0.14461634411473925, ...) +2024-05-01 15:38:18,241:INFO:create_model() successfully completed...................................... +2024-05-01 15:38:19,665:INFO:SubProcess create_model() end ================================== +2024-05-01 15:38:19,666:INFO:choose_better activated +2024-05-01 15:38:19,728:INFO:SubProcess create_model() called ================================== +2024-05-01 15:38:19,914:INFO:Initializing create_model() +2024-05-01 15:38:19,921:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=2361, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:38:19,922:INFO:Checking exceptions +2024-05-01 15:38:19,944:INFO:Importing libraries +2024-05-01 15:38:19,946:INFO:Copying training dataset +2024-05-01 15:38:20,013:INFO:Defining folds +2024-05-01 15:38:20,014:INFO:Declaring metric variables +2024-05-01 15:38:20,018:INFO:Importing untrained model +2024-05-01 15:38:20,019:INFO:Declaring custom model +2024-05-01 15:38:20,057:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 15:38:20,060:INFO:Starting cross validation +2024-05-01 15:38:20,074:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:38:53,290:INFO:Calculating mean and std +2024-05-01 15:38:53,300:INFO:Creating metrics dataframe +2024-05-01 15:38:53,355:INFO:Finalizing model +2024-05-01 15:38:57,328:INFO:Uploading results into container +2024-05-01 15:38:57,332:INFO:Uploading model into container now +2024-05-01 15:38:57,335:INFO:_master_model_container: 22 +2024-05-01 15:38:57,336:INFO:_display_container: 5 +2024-05-01 15:38:57,369: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, predictor='auto', + random_state=2361, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 15:38:57,370:INFO:create_model() successfully completed...................................... +2024-05-01 15:38:58,257:INFO:SubProcess create_model() end ================================== +2024-05-01 15:38:58,288: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, predictor='auto', + random_state=2361, reg_alpha=0, reg_lambda=1, ...) result for R2 is 0.9721 +2024-05-01 15:38:58,320:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, + colsample_bytree=0.7496867202068435, 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=0.3132665087447957, + max_bin=256, max_cat_to_onehot=4, max_delta_step=0, max_depth=6, + max_leaves=0, min_child_weight=4, missing=nan, + monotone_constraints='()', n_estimators=291, n_jobs=-1, + num_parallel_tree=1, predictor='auto', random_state=2361, + reg_alpha=0.007399227574348389, reg_lambda=0.14461634411473925, ...) result for R2 is 0.971 +2024-05-01 15:38:58,354: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, predictor='auto', + random_state=2361, reg_alpha=0, reg_lambda=1, ...) is best model +2024-05-01 15:38:58,355:INFO:choose_better completed +2024-05-01 15:38:58,359:INFO:Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one). +2024-05-01 15:38:58,547:INFO:_master_model_container: 22 +2024-05-01 15:38:58,548:INFO:_display_container: 4 +2024-05-01 15:38:58,582: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, predictor='auto', + random_state=2361, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 15:38:58,584:INFO:tune_model() successfully completed...................................... +2024-05-01 15:40:34,573:INFO:Initializing predict_model() +2024-05-01 15:40:34,574:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=2361, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000002819D3DEDE0>) +2024-05-01 15:40:34,574:INFO:Checking exceptions +2024-05-01 15:40:34,575:INFO:Preloading libraries +2024-05-01 15:40:34,590:INFO:Set up data. +2024-05-01 15:40:34,639:INFO:Set up index. +2024-05-01 15:40:38,599:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\pipeline.py:287: UserWarning: Persisting input arguments took 1.06s to run.If this happens often in your code, it can cause performance problems (results will be correct in all cases). The reason for this is probably some large input arguments for a wrapped function. + X, y = self._memory_full_transform( + +2024-05-01 15:42:03,603:INFO:Initializing compare_models() +2024-05-01 15:42:03,604:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-05-01 15:42:03,605:INFO:Checking exceptions +2024-05-01 15:42:03,620:INFO:Preparing display monitor +2024-05-01 15:42:03,982:INFO:Initializing Linear Regression +2024-05-01 15:42:03,984:INFO:Total runtime is 3.3350785573323566e-05 minutes +2024-05-01 15:42:04,185:INFO:SubProcess create_model() called ================================== +2024-05-01 15:42:04,187:INFO:Initializing create_model() +2024-05-01 15:42:04,188:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:42:04,188:INFO:Checking exceptions +2024-05-01 15:42:04,189:INFO:Importing libraries +2024-05-01 15:42:04,190:INFO:Copying training dataset +2024-05-01 15:42:04,309:INFO:Defining folds +2024-05-01 15:42:04,312:INFO:Declaring metric variables +2024-05-01 15:42:04,410:INFO:Importing untrained model +2024-05-01 15:42:04,528:INFO:Linear Regression Imported successfully +2024-05-01 15:42:04,717:INFO:Starting cross validation +2024-05-01 15:42:04,736:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:42:10,171:INFO:Calculating mean and std +2024-05-01 15:42:10,180:INFO:Creating metrics dataframe +2024-05-01 15:42:10,210:INFO:Uploading results into container +2024-05-01 15:42:10,222:INFO:Uploading model into container now +2024-05-01 15:42:10,225:INFO:_master_model_container: 23 +2024-05-01 15:42:10,226:INFO:_display_container: 5 +2024-05-01 15:42:10,228:INFO:LinearRegression(n_jobs=-1) +2024-05-01 15:42:10,229:INFO:create_model() successfully completed...................................... +2024-05-01 15:42:10,723:INFO:SubProcess create_model() end ================================== +2024-05-01 15:42:10,724:INFO:Creating metrics dataframe +2024-05-01 15:42:10,790:INFO:Initializing Lasso Regression +2024-05-01 15:42:10,791:INFO:Total runtime is 0.11346123615900676 minutes +2024-05-01 15:42:10,834:INFO:SubProcess create_model() called ================================== +2024-05-01 15:42:10,836:INFO:Initializing create_model() +2024-05-01 15:42:10,836:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:42:10,837:INFO:Checking exceptions +2024-05-01 15:42:10,837:INFO:Importing libraries +2024-05-01 15:42:10,838:INFO:Copying training dataset +2024-05-01 15:42:11,021:INFO:Defining folds +2024-05-01 15:42:11,064:INFO:Declaring metric variables +2024-05-01 15:42:11,477:INFO:Importing untrained model +2024-05-01 15:42:11,566:INFO:Lasso Regression Imported successfully +2024-05-01 15:42:11,687:INFO:Starting cross validation +2024-05-01 15:42:11,712:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:42:15,543:INFO:Calculating mean and std +2024-05-01 15:42:15,553:INFO:Creating metrics dataframe +2024-05-01 15:42:15,574:INFO:Uploading results into container +2024-05-01 15:42:15,580:INFO:Uploading model into container now +2024-05-01 15:42:15,586:INFO:_master_model_container: 24 +2024-05-01 15:42:15,587:INFO:_display_container: 5 +2024-05-01 15:42:15,599:INFO:Lasso(random_state=2361) +2024-05-01 15:42:15,600:INFO:create_model() successfully completed...................................... +2024-05-01 15:42:15,933:INFO:SubProcess create_model() end ================================== +2024-05-01 15:42:15,934:INFO:Creating metrics dataframe +2024-05-01 15:42:16,002:INFO:Initializing Ridge Regression +2024-05-01 15:42:16,004:INFO:Total runtime is 0.20036383867263796 minutes +2024-05-01 15:42:16,037:INFO:SubProcess create_model() called ================================== +2024-05-01 15:42:16,044:INFO:Initializing create_model() +2024-05-01 15:42:16,045:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:42:16,046:INFO:Checking exceptions +2024-05-01 15:42:16,046:INFO:Importing libraries +2024-05-01 15:42:16,047:INFO:Copying training dataset +2024-05-01 15:42:16,142:INFO:Defining folds +2024-05-01 15:42:16,145:INFO:Declaring metric variables +2024-05-01 15:42:16,220:INFO:Importing untrained model +2024-05-01 15:42:16,311:INFO:Ridge Regression Imported successfully +2024-05-01 15:42:16,503:INFO:Starting cross validation +2024-05-01 15:42:16,520:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:42:19,289:INFO:Calculating mean and std +2024-05-01 15:42:19,299:INFO:Creating metrics dataframe +2024-05-01 15:42:19,326:INFO:Uploading results into container +2024-05-01 15:42:19,331:INFO:Uploading model into container now +2024-05-01 15:42:19,333:INFO:_master_model_container: 25 +2024-05-01 15:42:19,334:INFO:_display_container: 5 +2024-05-01 15:42:19,338:INFO:Ridge(random_state=2361) +2024-05-01 15:42:19,339:INFO:create_model() successfully completed...................................... +2024-05-01 15:42:19,675:INFO:SubProcess create_model() end ================================== +2024-05-01 15:42:19,676:INFO:Creating metrics dataframe +2024-05-01 15:42:19,749:INFO:Initializing Elastic Net +2024-05-01 15:42:19,750:INFO:Total runtime is 0.26278911431630453 minutes +2024-05-01 15:42:19,846:INFO:SubProcess create_model() called ================================== +2024-05-01 15:42:19,848:INFO:Initializing create_model() +2024-05-01 15:42:19,850:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:42:19,858:INFO:Checking exceptions +2024-05-01 15:42:19,860:INFO:Importing libraries +2024-05-01 15:42:19,860:INFO:Copying training dataset +2024-05-01 15:42:19,967:INFO:Defining folds +2024-05-01 15:42:19,968:INFO:Declaring metric variables +2024-05-01 15:42:20,011:INFO:Importing untrained model +2024-05-01 15:42:20,160:INFO:Elastic Net Imported successfully +2024-05-01 15:42:20,312:INFO:Starting cross validation +2024-05-01 15:42:20,323:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:42:23,976:INFO:Calculating mean and std +2024-05-01 15:42:23,985:INFO:Creating metrics dataframe +2024-05-01 15:42:24,073:INFO:Uploading results into container +2024-05-01 15:42:24,078:INFO:Uploading model into container now +2024-05-01 15:42:24,081:INFO:_master_model_container: 26 +2024-05-01 15:42:24,081:INFO:_display_container: 5 +2024-05-01 15:42:24,084:INFO:ElasticNet(random_state=2361) +2024-05-01 15:42:24,084:INFO:create_model() successfully completed...................................... +2024-05-01 15:42:24,426:INFO:SubProcess create_model() end ================================== +2024-05-01 15:42:24,427:INFO:Creating metrics dataframe +2024-05-01 15:42:24,496:INFO:Initializing Least Angle Regression +2024-05-01 15:42:24,496:INFO:Total runtime is 0.34189889828364056 minutes +2024-05-01 15:42:24,522:INFO:SubProcess create_model() called ================================== +2024-05-01 15:42:24,544:INFO:Initializing create_model() +2024-05-01 15:42:24,544:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:42:24,545:INFO:Checking exceptions +2024-05-01 15:42:24,545:INFO:Importing libraries +2024-05-01 15:42:24,545:INFO:Copying training dataset +2024-05-01 15:42:24,633:INFO:Defining folds +2024-05-01 15:42:24,634:INFO:Declaring metric variables +2024-05-01 15:42:24,718:INFO:Importing untrained model +2024-05-01 15:42:24,758:INFO:Least Angle Regression Imported successfully +2024-05-01 15:42:24,961:INFO:Starting cross validation +2024-05-01 15:42:25,005:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:42:28,124:INFO:Calculating mean and std +2024-05-01 15:42:28,136:INFO:Creating metrics dataframe +2024-05-01 15:42:28,214:INFO:Uploading results into container +2024-05-01 15:42:28,219:INFO:Uploading model into container now +2024-05-01 15:42:28,223:INFO:_master_model_container: 27 +2024-05-01 15:42:28,223:INFO:_display_container: 5 +2024-05-01 15:42:28,226:INFO:Lars(random_state=2361) +2024-05-01 15:42:28,227:INFO:create_model() successfully completed...................................... +2024-05-01 15:42:28,560:INFO:SubProcess create_model() end ================================== +2024-05-01 15:42:28,560:INFO:Creating metrics dataframe +2024-05-01 15:42:28,634:INFO:Initializing Lasso Least Angle Regression +2024-05-01 15:42:28,636:INFO:Total runtime is 0.41088509956995645 minutes +2024-05-01 15:42:28,659:INFO:SubProcess create_model() called ================================== +2024-05-01 15:42:28,661:INFO:Initializing create_model() +2024-05-01 15:42:28,661:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:42:28,661:INFO:Checking exceptions +2024-05-01 15:42:28,662:INFO:Importing libraries +2024-05-01 15:42:28,662:INFO:Copying training dataset +2024-05-01 15:42:28,739:INFO:Defining folds +2024-05-01 15:42:28,740:INFO:Declaring metric variables +2024-05-01 15:42:28,821:INFO:Importing untrained model +2024-05-01 15:42:28,876:INFO:Lasso Least Angle Regression Imported successfully +2024-05-01 15:42:28,978:INFO:Starting cross validation +2024-05-01 15:42:28,989:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:42:32,703:INFO:Calculating mean and std +2024-05-01 15:42:32,722:INFO:Creating metrics dataframe +2024-05-01 15:42:32,825:INFO:Uploading results into container +2024-05-01 15:42:32,834:INFO:Uploading model into container now +2024-05-01 15:42:32,840:INFO:_master_model_container: 28 +2024-05-01 15:42:32,841:INFO:_display_container: 5 +2024-05-01 15:42:32,845:INFO:LassoLars(random_state=2361) +2024-05-01 15:42:32,846:INFO:create_model() successfully completed...................................... +2024-05-01 15:42:33,267:INFO:SubProcess create_model() end ================================== +2024-05-01 15:42:33,267:INFO:Creating metrics dataframe +2024-05-01 15:42:33,363:INFO:Initializing Orthogonal Matching Pursuit +2024-05-01 15:42:33,364:INFO:Total runtime is 0.48969486157099407 minutes +2024-05-01 15:42:33,397:INFO:SubProcess create_model() called ================================== +2024-05-01 15:42:33,402:INFO:Initializing create_model() +2024-05-01 15:42:33,402:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:42:33,403:INFO:Checking exceptions +2024-05-01 15:42:33,404:INFO:Importing libraries +2024-05-01 15:42:33,404:INFO:Copying training dataset +2024-05-01 15:42:33,502:INFO:Defining folds +2024-05-01 15:42:33,502:INFO:Declaring metric variables +2024-05-01 15:42:33,570:INFO:Importing untrained model +2024-05-01 15:42:33,612:INFO:Orthogonal Matching Pursuit Imported successfully +2024-05-01 15:42:33,858:INFO:Starting cross validation +2024-05-01 15:42:33,875:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:42:37,551:INFO:Calculating mean and std +2024-05-01 15:42:37,560:INFO:Creating metrics dataframe +2024-05-01 15:42:37,637:INFO:Uploading results into container +2024-05-01 15:42:37,641:INFO:Uploading model into container now +2024-05-01 15:42:37,642:INFO:_master_model_container: 29 +2024-05-01 15:42:37,643:INFO:_display_container: 5 +2024-05-01 15:42:37,644:INFO:OrthogonalMatchingPursuit() +2024-05-01 15:42:37,644:INFO:create_model() successfully completed...................................... +2024-05-01 15:42:37,965:INFO:SubProcess create_model() end ================================== +2024-05-01 15:42:37,966:INFO:Creating metrics dataframe +2024-05-01 15:42:38,050:INFO:Initializing Bayesian Ridge +2024-05-01 15:42:38,051:INFO:Total runtime is 0.5678055802981059 minutes +2024-05-01 15:42:38,105:INFO:SubProcess create_model() called ================================== +2024-05-01 15:42:38,107:INFO:Initializing create_model() +2024-05-01 15:42:38,108:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:42:38,109:INFO:Checking exceptions +2024-05-01 15:42:38,109:INFO:Importing libraries +2024-05-01 15:42:38,110:INFO:Copying training dataset +2024-05-01 15:42:38,204:INFO:Defining folds +2024-05-01 15:42:38,205:INFO:Declaring metric variables +2024-05-01 15:42:38,267:INFO:Importing untrained model +2024-05-01 15:42:38,301:INFO:Bayesian Ridge Imported successfully +2024-05-01 15:42:38,560:INFO:Starting cross validation +2024-05-01 15:42:38,583:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:42:43,484:INFO:Calculating mean and std +2024-05-01 15:42:43,496:INFO:Creating metrics dataframe +2024-05-01 15:42:43,581:INFO:Uploading results into container +2024-05-01 15:42:43,590:INFO:Uploading model into container now +2024-05-01 15:42:43,595:INFO:_master_model_container: 30 +2024-05-01 15:42:43,595:INFO:_display_container: 5 +2024-05-01 15:42:43,600:INFO:BayesianRidge() +2024-05-01 15:42:43,601:INFO:create_model() successfully completed...................................... +2024-05-01 15:42:43,969:INFO:SubProcess create_model() end ================================== +2024-05-01 15:42:43,969:INFO:Creating metrics dataframe +2024-05-01 15:42:44,047:INFO:Initializing Passive Aggressive Regressor +2024-05-01 15:42:44,048:INFO:Total runtime is 0.6677626808484396 minutes +2024-05-01 15:42:44,110:INFO:SubProcess create_model() called ================================== +2024-05-01 15:42:44,113:INFO:Initializing create_model() +2024-05-01 15:42:44,115:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:42:44,116:INFO:Checking exceptions +2024-05-01 15:42:44,117:INFO:Importing libraries +2024-05-01 15:42:44,117:INFO:Copying training dataset +2024-05-01 15:42:44,333:INFO:Defining folds +2024-05-01 15:42:44,334:INFO:Declaring metric variables +2024-05-01 15:42:44,406:INFO:Importing untrained model +2024-05-01 15:42:44,609:INFO:Passive Aggressive Regressor Imported successfully +2024-05-01 15:42:44,942:INFO:Starting cross validation +2024-05-01 15:42:44,956:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:42:49,707:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:42:49,709:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:42:49,720:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:42:50,064:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:42:54,392:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:42:54,471:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:42:54,490:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:42:54,789:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:42:57,377:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:42:57,389:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:42:57,616:INFO:Calculating mean and std +2024-05-01 15:42:57,624:INFO:Creating metrics dataframe +2024-05-01 15:42:57,684:INFO:Uploading results into container +2024-05-01 15:42:57,687:INFO:Uploading model into container now +2024-05-01 15:42:57,695:INFO:_master_model_container: 31 +2024-05-01 15:42:57,696:INFO:_display_container: 5 +2024-05-01 15:42:57,699:INFO:PassiveAggressiveRegressor(random_state=2361) +2024-05-01 15:42:57,700:INFO:create_model() successfully completed...................................... +2024-05-01 15:42:58,005:INFO:SubProcess create_model() end ================================== +2024-05-01 15:42:58,005:INFO:Creating metrics dataframe +2024-05-01 15:42:58,095:INFO:Initializing Huber Regressor +2024-05-01 15:42:58,096:INFO:Total runtime is 0.9018784483273825 minutes +2024-05-01 15:42:58,121:INFO:SubProcess create_model() called ================================== +2024-05-01 15:42:58,123:INFO:Initializing create_model() +2024-05-01 15:42:58,123:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:42:58,124:INFO:Checking exceptions +2024-05-01 15:42:58,125:INFO:Importing libraries +2024-05-01 15:42:58,126:INFO:Copying training dataset +2024-05-01 15:42:58,217:INFO:Defining folds +2024-05-01 15:42:58,218:INFO:Declaring metric variables +2024-05-01 15:42:58,278:INFO:Importing untrained model +2024-05-01 15:42:58,414:INFO:Huber Regressor Imported successfully +2024-05-01 15:42:58,603:INFO:Starting cross validation +2024-05-01 15:42:58,615:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:43:02,997:INFO:Calculating mean and std +2024-05-01 15:43:03,004:INFO:Creating metrics dataframe +2024-05-01 15:43:03,065:INFO:Uploading results into container +2024-05-01 15:43:03,068:INFO:Uploading model into container now +2024-05-01 15:43:03,069:INFO:_master_model_container: 32 +2024-05-01 15:43:03,070:INFO:_display_container: 5 +2024-05-01 15:43:03,072:INFO:HuberRegressor() +2024-05-01 15:43:03,073:INFO:create_model() successfully completed...................................... +2024-05-01 15:43:03,390:INFO:SubProcess create_model() end ================================== +2024-05-01 15:43:03,391:INFO:Creating metrics dataframe +2024-05-01 15:43:03,497:INFO:Initializing K Neighbors Regressor +2024-05-01 15:43:03,498:INFO:Total runtime is 0.9919281284014385 minutes +2024-05-01 15:43:03,531:INFO:SubProcess create_model() called ================================== +2024-05-01 15:43:03,533:INFO:Initializing create_model() +2024-05-01 15:43:03,534:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:43:03,535:INFO:Checking exceptions +2024-05-01 15:43:03,535:INFO:Importing libraries +2024-05-01 15:43:03,536:INFO:Copying training dataset +2024-05-01 15:43:03,653:INFO:Defining folds +2024-05-01 15:43:03,653:INFO:Declaring metric variables +2024-05-01 15:43:03,844:INFO:Importing untrained model +2024-05-01 15:43:03,873:INFO:K Neighbors Regressor Imported successfully +2024-05-01 15:43:04,037:INFO:Starting cross validation +2024-05-01 15:43:04,051:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:43:07,478:INFO:Calculating mean and std +2024-05-01 15:43:07,488:INFO:Creating metrics dataframe +2024-05-01 15:43:07,509:INFO:Uploading results into container +2024-05-01 15:43:07,517:INFO:Uploading model into container now +2024-05-01 15:43:07,519:INFO:_master_model_container: 33 +2024-05-01 15:43:07,520:INFO:_display_container: 5 +2024-05-01 15:43:07,522:INFO:KNeighborsRegressor(n_jobs=-1) +2024-05-01 15:43:07,523:INFO:create_model() successfully completed...................................... +2024-05-01 15:43:07,874:INFO:SubProcess create_model() end ================================== +2024-05-01 15:43:07,875:INFO:Creating metrics dataframe +2024-05-01 15:43:07,955:INFO:Initializing Decision Tree Regressor +2024-05-01 15:43:07,956:INFO:Total runtime is 1.0662257035573324 minutes +2024-05-01 15:43:07,986:INFO:SubProcess create_model() called ================================== +2024-05-01 15:43:07,988:INFO:Initializing create_model() +2024-05-01 15:43:07,991:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:43:07,992:INFO:Checking exceptions +2024-05-01 15:43:07,993:INFO:Importing libraries +2024-05-01 15:43:07,994:INFO:Copying training dataset +2024-05-01 15:43:08,078:INFO:Defining folds +2024-05-01 15:43:08,079:INFO:Declaring metric variables +2024-05-01 15:43:08,201:INFO:Importing untrained model +2024-05-01 15:43:08,266:INFO:Decision Tree Regressor Imported successfully +2024-05-01 15:43:08,516:INFO:Starting cross validation +2024-05-01 15:43:08,534:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:43:12,319:INFO:Calculating mean and std +2024-05-01 15:43:12,326:INFO:Creating metrics dataframe +2024-05-01 15:43:12,376:INFO:Uploading results into container +2024-05-01 15:43:12,388:INFO:Uploading model into container now +2024-05-01 15:43:12,390:INFO:_master_model_container: 34 +2024-05-01 15:43:12,391:INFO:_display_container: 5 +2024-05-01 15:43:12,395:INFO:DecisionTreeRegressor(random_state=2361) +2024-05-01 15:43:12,396:INFO:create_model() successfully completed...................................... +2024-05-01 15:43:12,689:INFO:SubProcess create_model() end ================================== +2024-05-01 15:43:12,689:INFO:Creating metrics dataframe +2024-05-01 15:43:12,790:INFO:Initializing Random Forest Regressor +2024-05-01 15:43:12,790:INFO:Total runtime is 1.1468008677164714 minutes +2024-05-01 15:43:12,833:INFO:SubProcess create_model() called ================================== +2024-05-01 15:43:12,835:INFO:Initializing create_model() +2024-05-01 15:43:12,835:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:43:12,836:INFO:Checking exceptions +2024-05-01 15:43:12,837:INFO:Importing libraries +2024-05-01 15:43:12,837:INFO:Copying training dataset +2024-05-01 15:43:12,914:INFO:Defining folds +2024-05-01 15:43:12,915:INFO:Declaring metric variables +2024-05-01 15:43:12,953:INFO:Importing untrained model +2024-05-01 15:43:13,088:INFO:Random Forest Regressor Imported successfully +2024-05-01 15:43:13,227:INFO:Starting cross validation +2024-05-01 15:43:13,261:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:44:24,397:INFO:Calculating mean and std +2024-05-01 15:44:24,408:INFO:Creating metrics dataframe +2024-05-01 15:44:24,438:INFO:Uploading results into container +2024-05-01 15:44:24,443:INFO:Uploading model into container now +2024-05-01 15:44:24,450:INFO:_master_model_container: 35 +2024-05-01 15:44:24,450:INFO:_display_container: 5 +2024-05-01 15:44:24,454:INFO:RandomForestRegressor(n_jobs=-1, random_state=2361) +2024-05-01 15:44:24,456:INFO:create_model() successfully completed...................................... +2024-05-01 15:44:24,957:INFO:SubProcess create_model() end ================================== +2024-05-01 15:44:24,960:INFO:Creating metrics dataframe +2024-05-01 15:44:25,105:INFO:Initializing Extra Trees Regressor +2024-05-01 15:44:25,106:INFO:Total runtime is 2.3520527919133505 minutes +2024-05-01 15:44:25,150:INFO:SubProcess create_model() called ================================== +2024-05-01 15:44:25,153:INFO:Initializing create_model() +2024-05-01 15:44:25,154:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:44:25,155:INFO:Checking exceptions +2024-05-01 15:44:25,156:INFO:Importing libraries +2024-05-01 15:44:25,156:INFO:Copying training dataset +2024-05-01 15:44:25,210:INFO:Defining folds +2024-05-01 15:44:25,211:INFO:Declaring metric variables +2024-05-01 15:44:25,247:INFO:Importing untrained model +2024-05-01 15:44:25,282:INFO:Extra Trees Regressor Imported successfully +2024-05-01 15:44:25,350:INFO:Starting cross validation +2024-05-01 15:44:25,360:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:45:04,339:INFO:Calculating mean and std +2024-05-01 15:45:04,350:INFO:Creating metrics dataframe +2024-05-01 15:45:04,465:INFO:Uploading results into container +2024-05-01 15:45:04,503:INFO:Uploading model into container now +2024-05-01 15:45:04,508:INFO:_master_model_container: 36 +2024-05-01 15:45:04,509:INFO:_display_container: 5 +2024-05-01 15:45:04,510:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=2361) +2024-05-01 15:45:04,511:INFO:create_model() successfully completed...................................... +2024-05-01 15:45:04,877:INFO:SubProcess create_model() end ================================== +2024-05-01 15:45:04,878:INFO:Creating metrics dataframe +2024-05-01 15:45:04,966:INFO:Initializing AdaBoost Regressor +2024-05-01 15:45:04,968:INFO:Total runtime is 3.0164066871007282 minutes +2024-05-01 15:45:05,003:INFO:SubProcess create_model() called ================================== +2024-05-01 15:45:05,005:INFO:Initializing create_model() +2024-05-01 15:45:05,007:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:45:05,007:INFO:Checking exceptions +2024-05-01 15:45:05,008:INFO:Importing libraries +2024-05-01 15:45:05,008:INFO:Copying training dataset +2024-05-01 15:45:05,082:INFO:Defining folds +2024-05-01 15:45:05,084:INFO:Declaring metric variables +2024-05-01 15:45:05,112:INFO:Importing untrained model +2024-05-01 15:45:05,282:INFO:AdaBoost Regressor Imported successfully +2024-05-01 15:45:05,572:INFO:Starting cross validation +2024-05-01 15:45:05,583:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:45:18,431:INFO:Calculating mean and std +2024-05-01 15:45:18,438:INFO:Creating metrics dataframe +2024-05-01 15:45:18,507:INFO:Uploading results into container +2024-05-01 15:45:18,512:INFO:Uploading model into container now +2024-05-01 15:45:18,515:INFO:_master_model_container: 37 +2024-05-01 15:45:18,516:INFO:_display_container: 5 +2024-05-01 15:45:18,519:INFO:AdaBoostRegressor(random_state=2361) +2024-05-01 15:45:18,519:INFO:create_model() successfully completed...................................... +2024-05-01 15:45:18,875:INFO:SubProcess create_model() end ================================== +2024-05-01 15:45:18,876:INFO:Creating metrics dataframe +2024-05-01 15:45:18,990:INFO:Initializing Gradient Boosting Regressor +2024-05-01 15:45:18,992:INFO:Total runtime is 3.2501561045646667 minutes +2024-05-01 15:45:19,020:INFO:SubProcess create_model() called ================================== +2024-05-01 15:45:19,022:INFO:Initializing create_model() +2024-05-01 15:45:19,023:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:45:19,023:INFO:Checking exceptions +2024-05-01 15:45:19,024:INFO:Importing libraries +2024-05-01 15:45:19,024:INFO:Copying training dataset +2024-05-01 15:45:19,118:INFO:Defining folds +2024-05-01 15:45:19,119:INFO:Declaring metric variables +2024-05-01 15:45:19,195:INFO:Importing untrained model +2024-05-01 15:45:19,371:INFO:Gradient Boosting Regressor Imported successfully +2024-05-01 15:45:19,637:INFO:Starting cross validation +2024-05-01 15:45:19,652:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:45:40,243:INFO:Calculating mean and std +2024-05-01 15:45:40,258:INFO:Creating metrics dataframe +2024-05-01 15:45:40,317:INFO:Uploading results into container +2024-05-01 15:45:40,328:INFO:Uploading model into container now +2024-05-01 15:45:40,336:INFO:_master_model_container: 38 +2024-05-01 15:45:40,336:INFO:_display_container: 5 +2024-05-01 15:45:40,340:INFO:GradientBoostingRegressor(random_state=2361) +2024-05-01 15:45:40,341:INFO:create_model() successfully completed...................................... +2024-05-01 15:45:40,723:INFO:SubProcess create_model() end ================================== +2024-05-01 15:45:40,723:INFO:Creating metrics dataframe +2024-05-01 15:45:40,863:INFO:Initializing Extreme Gradient Boosting +2024-05-01 15:45:40,865:INFO:Total runtime is 3.61470064719518 minutes +2024-05-01 15:45:40,903:INFO:SubProcess create_model() called ================================== +2024-05-01 15:45:40,910:INFO:Initializing create_model() +2024-05-01 15:45:40,911:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:45:40,912:INFO:Checking exceptions +2024-05-01 15:45:40,913:INFO:Importing libraries +2024-05-01 15:45:40,913:INFO:Copying training dataset +2024-05-01 15:45:40,996:INFO:Defining folds +2024-05-01 15:45:40,997:INFO:Declaring metric variables +2024-05-01 15:45:41,031:INFO:Importing untrained model +2024-05-01 15:45:41,112:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 15:45:41,268:INFO:Starting cross validation +2024-05-01 15:45:41,298:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:46:02,945:INFO:Calculating mean and std +2024-05-01 15:46:02,956:INFO:Creating metrics dataframe +2024-05-01 15:46:03,057:INFO:Uploading results into container +2024-05-01 15:46:03,071:INFO:Uploading model into container now +2024-05-01 15:46:03,076:INFO:_master_model_container: 39 +2024-05-01 15:46:03,077:INFO:_display_container: 5 +2024-05-01 15:46:03,117: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, predictor=None, random_state=2361, + reg_alpha=None, reg_lambda=None, ...) +2024-05-01 15:46:03,118:INFO:create_model() successfully completed...................................... +2024-05-01 15:46:03,528:INFO:SubProcess create_model() end ================================== +2024-05-01 15:46:03,528:INFO:Creating metrics dataframe +2024-05-01 15:46:03,660:INFO:Initializing Light Gradient Boosting Machine +2024-05-01 15:46:03,661:INFO:Total runtime is 3.994647518793742 minutes +2024-05-01 15:46:03,701:INFO:SubProcess create_model() called ================================== +2024-05-01 15:46:03,704:INFO:Initializing create_model() +2024-05-01 15:46:03,706:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:46:03,707:INFO:Checking exceptions +2024-05-01 15:46:03,710:INFO:Importing libraries +2024-05-01 15:46:03,710:INFO:Copying training dataset +2024-05-01 15:46:03,814:INFO:Defining folds +2024-05-01 15:46:03,815:INFO:Declaring metric variables +2024-05-01 15:46:03,895:INFO:Importing untrained model +2024-05-01 15:46:04,087:INFO:Light Gradient Boosting Machine Imported successfully +2024-05-01 15:46:04,215:INFO:Starting cross validation +2024-05-01 15:46:04,270:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:46:14,769:INFO:Calculating mean and std +2024-05-01 15:46:14,784:INFO:Creating metrics dataframe +2024-05-01 15:46:14,841:INFO:Uploading results into container +2024-05-01 15:46:14,846:INFO:Uploading model into container now +2024-05-01 15:46:14,848:INFO:_master_model_container: 40 +2024-05-01 15:46:14,849:INFO:_display_container: 5 +2024-05-01 15:46:14,852:INFO:LGBMRegressor(n_jobs=-1, random_state=2361) +2024-05-01 15:46:14,853:INFO:create_model() successfully completed...................................... +2024-05-01 15:46:15,214:INFO:SubProcess create_model() end ================================== +2024-05-01 15:46:15,215:INFO:Creating metrics dataframe +2024-05-01 15:46:15,347:INFO:Initializing Dummy Regressor +2024-05-01 15:46:15,348:INFO:Total runtime is 4.1894331256548565 minutes +2024-05-01 15:46:15,374:INFO:SubProcess create_model() called ================================== +2024-05-01 15:46:15,385:INFO:Initializing create_model() +2024-05-01 15:46:15,386:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:46:15,386:INFO:Checking exceptions +2024-05-01 15:46:15,387:INFO:Importing libraries +2024-05-01 15:46:15,387:INFO:Copying training dataset +2024-05-01 15:46:15,447:INFO:Defining folds +2024-05-01 15:46:15,448:INFO:Declaring metric variables +2024-05-01 15:46:15,496:INFO:Importing untrained model +2024-05-01 15:46:15,537:INFO:Dummy Regressor Imported successfully +2024-05-01 15:46:15,809:INFO:Starting cross validation +2024-05-01 15:46:15,831:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:46:18,509:INFO:Calculating mean and std +2024-05-01 15:46:18,522:INFO:Creating metrics dataframe +2024-05-01 15:46:18,558:INFO:Uploading results into container +2024-05-01 15:46:18,563:INFO:Uploading model into container now +2024-05-01 15:46:18,589:INFO:_master_model_container: 41 +2024-05-01 15:46:18,591:INFO:_display_container: 5 +2024-05-01 15:46:18,603:INFO:DummyRegressor() +2024-05-01 15:46:18,604:INFO:create_model() successfully completed...................................... +2024-05-01 15:46:19,012:INFO:SubProcess create_model() end ================================== +2024-05-01 15:46:19,013:INFO:Creating metrics dataframe +2024-05-01 15:46:19,308:INFO:Initializing create_model() +2024-05-01 15:46:19,313:INFO:create_model(self=, estimator=RandomForestRegressor(n_jobs=-1, random_state=2361), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:46:19,313:INFO:Checking exceptions +2024-05-01 15:46:19,327:INFO:Importing libraries +2024-05-01 15:46:19,328:INFO:Copying training dataset +2024-05-01 15:46:19,389:INFO:Defining folds +2024-05-01 15:46:19,390:INFO:Declaring metric variables +2024-05-01 15:46:19,397:INFO:Importing untrained model +2024-05-01 15:46:19,397:INFO:Declaring custom model +2024-05-01 15:46:19,456:INFO:Random Forest Regressor Imported successfully +2024-05-01 15:46:19,483:INFO:Cross validation set to False +2024-05-01 15:46:19,484:INFO:Fitting Model +2024-05-01 15:46:27,614:INFO:RandomForestRegressor(n_jobs=-1, random_state=2361) +2024-05-01 15:46:27,615:INFO:create_model() successfully completed...................................... +2024-05-01 15:46:28,505:INFO:_master_model_container: 41 +2024-05-01 15:46:28,506:INFO:_display_container: 5 +2024-05-01 15:46:28,509:INFO:RandomForestRegressor(n_jobs=-1, random_state=2361) +2024-05-01 15:46:28,509:INFO:compare_models() successfully completed...................................... +2024-05-01 15:47:29,475:INFO:Initializing create_model() +2024-05-01 15:47:29,477:INFO:create_model(self=, estimator=dt, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:47:29,477:INFO:Checking exceptions +2024-05-01 15:47:29,942:INFO:Importing libraries +2024-05-01 15:47:29,943:INFO:Copying training dataset +2024-05-01 15:47:30,257:INFO:Defining folds +2024-05-01 15:47:30,258:INFO:Declaring metric variables +2024-05-01 15:47:30,619:INFO:Importing untrained model +2024-05-01 15:47:30,951:INFO:Decision Tree Regressor Imported successfully +2024-05-01 15:47:31,388:INFO:Starting cross validation +2024-05-01 15:47:31,400:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:47:40,899:INFO:Calculating mean and std +2024-05-01 15:47:40,932:INFO:Creating metrics dataframe +2024-05-01 15:47:41,040:INFO:Finalizing model +2024-05-01 15:47:41,990:INFO:Uploading results into container +2024-05-01 15:47:41,995:INFO:Uploading model into container now +2024-05-01 15:47:42,162:INFO:_master_model_container: 42 +2024-05-01 15:47:42,163:INFO:_display_container: 6 +2024-05-01 15:47:42,166:INFO:DecisionTreeRegressor(random_state=2361) +2024-05-01 15:47:42,166:INFO:create_model() successfully completed...................................... +2024-05-01 15:47:54,425:INFO:Initializing evaluate_model() +2024-05-01 15:47:54,426:INFO:evaluate_model(self=, estimator=DecisionTreeRegressor(random_state=2361), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) +2024-05-01 15:47:54,505:INFO:Initializing plot_model() +2024-05-01 15:47:54,514:INFO:plot_model(self=, estimator=DecisionTreeRegressor(random_state=2361), plot=pipeline, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-05-01 15:47:54,515:INFO:Checking exceptions +2024-05-01 15:47:54,575:INFO:Preloading libraries +2024-05-01 15:47:54,620:INFO:Copying training dataset +2024-05-01 15:47:54,625:INFO:Plot type: pipeline +2024-05-01 15:47:57,417:INFO:Visual Rendered Successfully +2024-05-01 15:47:58,033:INFO:plot_model() successfully completed...................................... +2024-05-01 15:48:05,694:INFO:Initializing tune_model() +2024-05-01 15:48:05,695:INFO:tune_model(self=, estimator=DecisionTreeRegressor(random_state=2361), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-05-01 15:48:05,696:INFO:Checking exceptions +2024-05-01 15:48:05,943:INFO:Copying training dataset +2024-05-01 15:48:06,063:INFO:Checking base model +2024-05-01 15:48:06,065:INFO:Base model : Decision Tree Regressor +2024-05-01 15:48:06,340:INFO:Declaring metric variables +2024-05-01 15:48:06,483:INFO:Defining Hyperparameters +2024-05-01 15:48:08,072:INFO:Tuning with n_jobs=-1 +2024-05-01 15:48:08,072:INFO:Initializing RandomizedSearchCV +2024-05-01 15:48:50,402:INFO:best_params: {'actual_estimator__min_samples_split': 5, 'actual_estimator__min_samples_leaf': 4, 'actual_estimator__min_impurity_decrease': 0.5, 'actual_estimator__max_features': 'sqrt', 'actual_estimator__max_depth': 16, 'actual_estimator__criterion': 'squared_error'} +2024-05-01 15:48:50,413:INFO:Hyperparameter search completed +2024-05-01 15:48:50,414:INFO:SubProcess create_model() called ================================== +2024-05-01 15:48:50,427:INFO:Initializing create_model() +2024-05-01 15:48:50,427:INFO:create_model(self=, estimator=DecisionTreeRegressor(random_state=2361), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'min_samples_split': 5, 'min_samples_leaf': 4, 'min_impurity_decrease': 0.5, 'max_features': 'sqrt', 'max_depth': 16, 'criterion': 'squared_error'}) +2024-05-01 15:48:50,429:INFO:Checking exceptions +2024-05-01 15:48:50,430:INFO:Importing libraries +2024-05-01 15:48:50,430:INFO:Copying training dataset +2024-05-01 15:48:50,533:INFO:Defining folds +2024-05-01 15:48:50,534:INFO:Declaring metric variables +2024-05-01 15:48:50,608:INFO:Importing untrained model +2024-05-01 15:48:50,610:INFO:Declaring custom model +2024-05-01 15:48:50,650:INFO:Decision Tree Regressor Imported successfully +2024-05-01 15:48:50,760:INFO:Starting cross validation +2024-05-01 15:48:50,773:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:48:53,671:INFO:Calculating mean and std +2024-05-01 15:48:53,703:INFO:Creating metrics dataframe +2024-05-01 15:48:53,787:INFO:Finalizing model +2024-05-01 15:48:54,393:INFO:Uploading results into container +2024-05-01 15:48:54,434:INFO:Uploading model into container now +2024-05-01 15:48:54,439:INFO:_master_model_container: 43 +2024-05-01 15:48:54,439:INFO:_display_container: 7 +2024-05-01 15:48:54,444:INFO:DecisionTreeRegressor(max_depth=16, max_features='sqrt', + min_impurity_decrease=0.5, min_samples_leaf=4, + min_samples_split=5, random_state=2361) +2024-05-01 15:48:54,445:INFO:create_model() successfully completed...................................... +2024-05-01 15:48:55,097:INFO:SubProcess create_model() end ================================== +2024-05-01 15:48:55,099:INFO:choose_better activated +2024-05-01 15:48:55,129:INFO:SubProcess create_model() called ================================== +2024-05-01 15:48:55,133:INFO:Initializing create_model() +2024-05-01 15:48:55,135:INFO:create_model(self=, estimator=DecisionTreeRegressor(random_state=2361), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:48:55,136:INFO:Checking exceptions +2024-05-01 15:48:55,153:INFO:Importing libraries +2024-05-01 15:48:55,154:INFO:Copying training dataset +2024-05-01 15:48:55,206:INFO:Defining folds +2024-05-01 15:48:55,207:INFO:Declaring metric variables +2024-05-01 15:48:55,208:INFO:Importing untrained model +2024-05-01 15:48:55,209:INFO:Declaring custom model +2024-05-01 15:48:55,214:INFO:Decision Tree Regressor Imported successfully +2024-05-01 15:48:55,221:INFO:Starting cross validation +2024-05-01 15:48:55,232:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:48:59,172:INFO:Calculating mean and std +2024-05-01 15:48:59,176:INFO:Creating metrics dataframe +2024-05-01 15:48:59,193:INFO:Finalizing model +2024-05-01 15:48:59,863:INFO:Uploading results into container +2024-05-01 15:48:59,870:INFO:Uploading model into container now +2024-05-01 15:48:59,873:INFO:_master_model_container: 44 +2024-05-01 15:48:59,874:INFO:_display_container: 8 +2024-05-01 15:48:59,876:INFO:DecisionTreeRegressor(random_state=2361) +2024-05-01 15:48:59,876:INFO:create_model() successfully completed...................................... +2024-05-01 15:49:00,343:INFO:SubProcess create_model() end ================================== +2024-05-01 15:49:00,345:INFO:DecisionTreeRegressor(random_state=2361) result for R2 is 0.9549 +2024-05-01 15:49:00,352:INFO:DecisionTreeRegressor(max_depth=16, max_features='sqrt', + min_impurity_decrease=0.5, min_samples_leaf=4, + min_samples_split=5, random_state=2361) result for R2 is 0.7465 +2024-05-01 15:49:00,357:INFO:DecisionTreeRegressor(random_state=2361) is best model +2024-05-01 15:49:00,357:INFO:choose_better completed +2024-05-01 15:49:00,359:INFO:Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one). +2024-05-01 15:49:00,483:INFO:_master_model_container: 44 +2024-05-01 15:49:00,484:INFO:_display_container: 7 +2024-05-01 15:49:00,486:INFO:DecisionTreeRegressor(random_state=2361) +2024-05-01 15:49:00,487:INFO:tune_model() successfully completed...................................... +2024-05-01 15:49:29,168:INFO:Initializing predict_model() +2024-05-01 15:49:29,169:INFO:predict_model(self=, estimator=DecisionTreeRegressor(random_state=2361), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000002819D095080>) +2024-05-01 15:49:29,170:INFO:Checking exceptions +2024-05-01 15:49:29,170:INFO:Preloading libraries +2024-05-01 15:49:29,184:INFO:Set up data. +2024-05-01 15:49:29,210:INFO:Set up index. +2024-05-01 15:50:50,967:INFO:PyCaret RegressionExperiment +2024-05-01 15:50:50,967:INFO:Logging name: reg-default-name +2024-05-01 15:50:50,968:INFO:ML Usecase: MLUsecase.REGRESSION +2024-05-01 15:50:50,969:INFO:version 3.3.0 +2024-05-01 15:50:50,969:INFO:Initializing setup() +2024-05-01 15:50:50,970:INFO:self.USI: 27f4 +2024-05-01 15:50:50,970:INFO:self._variable_keys: {'exp_id', 'idx', 'exp_name_log', 'seed', 'html_param', 'fold_generator', 'logging_param', 'USI', 'y_test', 'pipeline', 'n_jobs_param', 'log_plots_param', 'memory', 'y', 'target_param', 'fold_shuffle_param', 'X', 'y_train', 'fold_groups_param', 'data', 'transform_target_param', 'gpu_param', '_available_plots', '_ml_usecase', 'gpu_n_jobs_param', 'X_test', 'X_train'} +2024-05-01 15:50:50,971:INFO:Checking environment +2024-05-01 15:50:50,972:INFO:python_version: 3.11.0 +2024-05-01 15:50:50,973:INFO:python_build: ('main', 'Oct 24 2022 18:26:48') +2024-05-01 15:50:50,973:INFO:machine: AMD64 +2024-05-01 15:50:50,975:INFO:platform: Windows-10-10.0.22000-SP0 +2024-05-01 15:50:50,994:INFO:Memory: svmem(total=8467492864, available=1352060928, percent=84.0, used=7115431936, free=1352060928) +2024-05-01 15:50:50,995:INFO:Physical Core: 2 +2024-05-01 15:50:50,996:INFO:Logical Core: 4 +2024-05-01 15:50:50,996:INFO:Checking libraries +2024-05-01 15:50:50,997:INFO:System: +2024-05-01 15:50:50,997:INFO: python: 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] +2024-05-01 15:50:50,997:INFO:executable: c:\Users\Jason\Documents\DataB3\.venv\Scripts\python.exe +2024-05-01 15:50:50,998:INFO: machine: Windows-10-10.0.22000-SP0 +2024-05-01 15:50:50,998:INFO:PyCaret required dependencies: +2024-05-01 15:50:50,999:INFO: pip: 24.0 +2024-05-01 15:50:50,999:INFO: setuptools: 65.5.0 +2024-05-01 15:50:51,000:INFO: pycaret: 3.3.0 +2024-05-01 15:50:51,000:INFO: IPython: 8.23.0 +2024-05-01 15:50:51,000:INFO: ipywidgets: 8.1.2 +2024-05-01 15:50:51,001:INFO: tqdm: 4.66.2 +2024-05-01 15:50:51,001:INFO: numpy: 1.24.4 +2024-05-01 15:50:51,001:INFO: pandas: 1.5.3 +2024-05-01 15:50:51,001:INFO: jinja2: 3.1.3 +2024-05-01 15:50:51,002:INFO: scipy: 1.11.4 +2024-05-01 15:50:51,002:INFO: joblib: 1.3.2 +2024-05-01 15:50:51,002:INFO: sklearn: 1.4.1.post1 +2024-05-01 15:50:51,003:INFO: pyod: 1.1.3 +2024-05-01 15:50:51,003:INFO: imblearn: 0.12.2 +2024-05-01 15:50:51,003:INFO: category_encoders: 2.6.3 +2024-05-01 15:50:51,004:INFO: lightgbm: 4.3.0 +2024-05-01 15:50:51,004:INFO: numba: 0.59.1 +2024-05-01 15:50:51,004:INFO: requests: 2.31.0 +2024-05-01 15:50:51,004:INFO: matplotlib: 3.8.3 +2024-05-01 15:50:51,005:INFO: scikitplot: 0.3.7 +2024-05-01 15:50:51,005:INFO: yellowbrick: 1.5 +2024-05-01 15:50:51,005:INFO: plotly: 5.20.0 +2024-05-01 15:50:51,006:INFO: plotly-resampler: Not installed +2024-05-01 15:50:51,006:INFO: kaleido: 0.2.1 +2024-05-01 15:50:51,006:INFO: schemdraw: 0.15 +2024-05-01 15:50:51,007:INFO: statsmodels: 0.14.1 +2024-05-01 15:50:51,007:INFO: sktime: 0.28.0 +2024-05-01 15:50:51,007:INFO: tbats: 1.1.3 +2024-05-01 15:50:51,008:INFO: pmdarima: 2.0.4 +2024-05-01 15:50:51,008:INFO: psutil: 5.9.8 +2024-05-01 15:50:51,008:INFO: markupsafe: 2.1.5 +2024-05-01 15:50:51,009:INFO: pickle5: Not installed +2024-05-01 15:50:51,009:INFO: cloudpickle: 3.0.0 +2024-05-01 15:50:51,009:INFO: deprecation: 2.1.0 +2024-05-01 15:50:51,009:INFO: xxhash: 3.4.1 +2024-05-01 15:50:51,010:INFO: wurlitzer: Not installed +2024-05-01 15:50:51,010:INFO:PyCaret optional dependencies: +2024-05-01 15:50:51,010:INFO: shap: Not installed +2024-05-01 15:50:51,011:INFO: interpret: Not installed +2024-05-01 15:50:51,011:INFO: umap: Not installed +2024-05-01 15:50:51,011:INFO: ydata_profiling: 4.7.0 +2024-05-01 15:50:51,012:INFO: explainerdashboard: Not installed +2024-05-01 15:50:51,012:INFO: autoviz: Not installed +2024-05-01 15:50:51,012:INFO: fairlearn: Not installed +2024-05-01 15:50:51,012:INFO: deepchecks: Not installed +2024-05-01 15:50:51,013:INFO: xgboost: 1.6.2 +2024-05-01 15:50:51,013:INFO: catboost: Not installed +2024-05-01 15:50:51,013:INFO: kmodes: Not installed +2024-05-01 15:50:51,014:INFO: mlxtend: Not installed +2024-05-01 15:50:51,014:INFO: statsforecast: Not installed +2024-05-01 15:50:51,014:INFO: tune_sklearn: Not installed +2024-05-01 15:50:51,014:INFO: ray: Not installed +2024-05-01 15:50:51,015:INFO: hyperopt: Not installed +2024-05-01 15:50:51,015:INFO: optuna: 3.6.1 +2024-05-01 15:50:51,015:INFO: skopt: Not installed +2024-05-01 15:50:51,016:INFO: mlflow: Not installed +2024-05-01 15:50:51,017:INFO: gradio: Not installed +2024-05-01 15:50:51,017:INFO: fastapi: Not installed +2024-05-01 15:50:51,018:INFO: uvicorn: Not installed +2024-05-01 15:50:51,018:INFO: m2cgen: Not installed +2024-05-01 15:50:51,019:INFO: evidently: Not installed +2024-05-01 15:50:51,019:INFO: fugue: Not installed +2024-05-01 15:50:51,020:INFO: streamlit: 1.33.0 +2024-05-01 15:50:51,021:INFO: prophet: 1.1.5 +2024-05-01 15:50:51,022:INFO:None +2024-05-01 15:50:51,022:INFO:Set up data. +2024-05-01 15:50:51,075:INFO:Set up folding strategy. +2024-05-01 15:50:51,076:INFO:Set up train/test split. +2024-05-01 15:50:51,126:INFO:Set up index. +2024-05-01 15:50:51,127:INFO:Assigning column types. +2024-05-01 15:50:51,177:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-05-01 15:50:51,179:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 15:50:51,243:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 15:50:51,319:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 15:50:52,105:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:50:52,765:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:50:52,769:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:50:52,799:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:50:52,801:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 15:50:52,865:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 15:50:52,921:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 15:50:53,731:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:50:54,332:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:50:54,335:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:50:54,366:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:50:54,367:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-05-01 15:50:54,424:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 15:50:54,477:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 15:50:55,255:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:50:55,807:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:50:55,813:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:50:55,853:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:50:55,933:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 15:50:55,987:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 15:50:56,732:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:50:57,396:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:50:57,403:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:50:57,436:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:50:57,439:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-05-01 15:50:57,552:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 15:50:58,267:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:50:58,835:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:50:58,839:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:50:58,875:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:50:58,999:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 15:50:59,708:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:51:00,325:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:51:00,328:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:51:00,356:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:51:00,357:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-05-01 15:51:01,236:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:51:01,862:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:51:01,865:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:51:01,900:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:51:02,810:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:51:03,552:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 15:51:03,557:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:51:03,609:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:51:03,612:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-05-01 15:51:04,625:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:51:05,378:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:51:05,420:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:51:06,573:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 15:51:07,185:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:51:07,233:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:51:07,235:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-05-01 15:51:08,589:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:51:08,617:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:51:10,088:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:51:10,125:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:51:10,132:INFO:Preparing preprocessing pipeline... +2024-05-01 15:51:10,132:INFO:Set up simple imputation. +2024-05-01 15:51:10,150:INFO:Set up encoding of categorical features. +2024-05-01 15:51:10,151:INFO:Set up feature normalization. +2024-05-01 15:51:10,647:INFO:Finished creating preprocessing pipeline. +2024-05-01 15:51:10,703:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\Jason\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['Date'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['Date'], + transformer=TargetEncoder(cols=['Date'], + handle_missing='return_nan'))), + ('normalize', TransformerWrapper(transformer=MinMaxScaler()))]) +2024-05-01 15:51:10,704:INFO:Creating final display dataframe. +2024-05-01 15:51:12,111:INFO:Setup _display_container: Description Value +0 Session id 6537 +1 Target Daily_Sales +2 Target type Regression +3 Original data shape (6435, 8) +4 Transformed data shape (6435, 8) +5 Transformed train set shape (4504, 8) +6 Transformed test set shape (1931, 8) +7 Numeric features 6 +8 Categorical features 1 +9 Preprocess True +10 Imputation type simple +11 Numeric imputation mean +12 Categorical imputation mode +13 Maximum one-hot encoding 25 +14 Encoding method None +15 Normalize True +16 Normalize method minmax +17 Fold Generator KFold +18 Fold Number 10 +19 CPU Jobs -1 +20 Use GPU False +21 Log Experiment False +22 Experiment Name reg-default-name +23 USI 27f4 +2024-05-01 15:51:14,420:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:51:14,450:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:51:15,969:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 15:51:15,997:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 15:51:16,001:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour. + warnings.warn( + +2024-05-01 15:51:16,002:INFO:setup() successfully completed in 25.25s............... +2024-05-01 15:51:16,172:INFO:Initializing compare_models() +2024-05-01 15:51:16,173:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-05-01 15:51:16,180:INFO:Checking exceptions +2024-05-01 15:51:16,194:INFO:Preparing display monitor +2024-05-01 15:51:16,643:INFO:Initializing Linear Regression +2024-05-01 15:51:16,643:INFO:Total runtime is 1.6661485036214194e-05 minutes +2024-05-01 15:51:16,687:INFO:SubProcess create_model() called ================================== +2024-05-01 15:51:16,689:INFO:Initializing create_model() +2024-05-01 15:51:16,690:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:51:16,690:INFO:Checking exceptions +2024-05-01 15:51:16,691:INFO:Importing libraries +2024-05-01 15:51:16,692:INFO:Copying training dataset +2024-05-01 15:51:16,731:INFO:Defining folds +2024-05-01 15:51:16,732:INFO:Declaring metric variables +2024-05-01 15:51:16,914:INFO:Importing untrained model +2024-05-01 15:51:17,032:INFO:Linear Regression Imported successfully +2024-05-01 15:51:17,278:INFO:Starting cross validation +2024-05-01 15:51:17,290:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:51:20,998:INFO:Calculating mean and std +2024-05-01 15:51:21,009:INFO:Creating metrics dataframe +2024-05-01 15:51:21,055:INFO:Uploading results into container +2024-05-01 15:51:21,064:INFO:Uploading model into container now +2024-05-01 15:51:21,079:INFO:_master_model_container: 1 +2024-05-01 15:51:21,080:INFO:_display_container: 2 +2024-05-01 15:51:21,082:INFO:LinearRegression(n_jobs=-1) +2024-05-01 15:51:21,082:INFO:create_model() successfully completed...................................... +2024-05-01 15:51:21,617:INFO:SubProcess create_model() end ================================== +2024-05-01 15:51:21,618:INFO:Creating metrics dataframe +2024-05-01 15:51:21,707:INFO:Initializing Lasso Regression +2024-05-01 15:51:21,708:INFO:Total runtime is 0.08443822860717774 minutes +2024-05-01 15:51:21,743:INFO:SubProcess create_model() called ================================== +2024-05-01 15:51:21,745:INFO:Initializing create_model() +2024-05-01 15:51:21,746:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:51:21,747:INFO:Checking exceptions +2024-05-01 15:51:21,753:INFO:Importing libraries +2024-05-01 15:51:21,754:INFO:Copying training dataset +2024-05-01 15:51:21,838:INFO:Defining folds +2024-05-01 15:51:21,840:INFO:Declaring metric variables +2024-05-01 15:51:21,909:INFO:Importing untrained model +2024-05-01 15:51:21,999:INFO:Lasso Regression Imported successfully +2024-05-01 15:51:22,140:INFO:Starting cross validation +2024-05-01 15:51:22,150:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:51:25,457:INFO:Calculating mean and std +2024-05-01 15:51:25,471:INFO:Creating metrics dataframe +2024-05-01 15:51:25,545:INFO:Uploading results into container +2024-05-01 15:51:25,550:INFO:Uploading model into container now +2024-05-01 15:51:25,554:INFO:_master_model_container: 2 +2024-05-01 15:51:25,555:INFO:_display_container: 2 +2024-05-01 15:51:25,557:INFO:Lasso(random_state=6537) +2024-05-01 15:51:25,557:INFO:create_model() successfully completed...................................... +2024-05-01 15:51:25,967:INFO:SubProcess create_model() end ================================== +2024-05-01 15:51:25,968:INFO:Creating metrics dataframe +2024-05-01 15:51:26,064:INFO:Initializing Ridge Regression +2024-05-01 15:51:26,065:INFO:Total runtime is 0.15704734325408937 minutes +2024-05-01 15:51:26,099:INFO:SubProcess create_model() called ================================== +2024-05-01 15:51:26,102:INFO:Initializing create_model() +2024-05-01 15:51:26,103:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:51:26,103:INFO:Checking exceptions +2024-05-01 15:51:26,104:INFO:Importing libraries +2024-05-01 15:51:26,105:INFO:Copying training dataset +2024-05-01 15:51:26,200:INFO:Defining folds +2024-05-01 15:51:26,200:INFO:Declaring metric variables +2024-05-01 15:51:26,235:INFO:Importing untrained model +2024-05-01 15:51:26,325:INFO:Ridge Regression Imported successfully +2024-05-01 15:51:26,470:INFO:Starting cross validation +2024-05-01 15:51:26,498:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:51:30,010:INFO:Calculating mean and std +2024-05-01 15:51:30,038:INFO:Creating metrics dataframe +2024-05-01 15:51:30,089:INFO:Uploading results into container +2024-05-01 15:51:30,093:INFO:Uploading model into container now +2024-05-01 15:51:30,096:INFO:_master_model_container: 3 +2024-05-01 15:51:30,097:INFO:_display_container: 2 +2024-05-01 15:51:30,099:INFO:Ridge(random_state=6537) +2024-05-01 15:51:30,099:INFO:create_model() successfully completed...................................... +2024-05-01 15:51:30,519:INFO:SubProcess create_model() end ================================== +2024-05-01 15:51:30,520:INFO:Creating metrics dataframe +2024-05-01 15:51:30,619:INFO:Initializing Elastic Net +2024-05-01 15:51:30,620:INFO:Total runtime is 0.23296218315760298 minutes +2024-05-01 15:51:30,654:INFO:SubProcess create_model() called ================================== +2024-05-01 15:51:30,657:INFO:Initializing create_model() +2024-05-01 15:51:30,658:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:51:30,659:INFO:Checking exceptions +2024-05-01 15:51:30,660:INFO:Importing libraries +2024-05-01 15:51:30,660:INFO:Copying training dataset +2024-05-01 15:51:30,729:INFO:Defining folds +2024-05-01 15:51:30,730:INFO:Declaring metric variables +2024-05-01 15:51:30,805:INFO:Importing untrained model +2024-05-01 15:51:30,848:INFO:Elastic Net Imported successfully +2024-05-01 15:51:30,976:INFO:Starting cross validation +2024-05-01 15:51:31,025:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:51:34,660:INFO:Calculating mean and std +2024-05-01 15:51:34,672:INFO:Creating metrics dataframe +2024-05-01 15:51:34,735:INFO:Uploading results into container +2024-05-01 15:51:34,739:INFO:Uploading model into container now +2024-05-01 15:51:34,743:INFO:_master_model_container: 4 +2024-05-01 15:51:34,743:INFO:_display_container: 2 +2024-05-01 15:51:34,746:INFO:ElasticNet(random_state=6537) +2024-05-01 15:51:34,747:INFO:create_model() successfully completed...................................... +2024-05-01 15:51:35,191:INFO:SubProcess create_model() end ================================== +2024-05-01 15:51:35,193:INFO:Creating metrics dataframe +2024-05-01 15:51:35,307:INFO:Initializing Least Angle Regression +2024-05-01 15:51:35,308:INFO:Total runtime is 0.31110723416010544 minutes +2024-05-01 15:51:35,347:INFO:SubProcess create_model() called ================================== +2024-05-01 15:51:35,349:INFO:Initializing create_model() +2024-05-01 15:51:35,351:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:51:35,351:INFO:Checking exceptions +2024-05-01 15:51:35,354:INFO:Importing libraries +2024-05-01 15:51:35,355:INFO:Copying training dataset +2024-05-01 15:51:35,433:INFO:Defining folds +2024-05-01 15:51:35,436:INFO:Declaring metric variables +2024-05-01 15:51:35,487:INFO:Importing untrained model +2024-05-01 15:51:35,550:INFO:Least Angle Regression Imported successfully +2024-05-01 15:51:35,676:INFO:Starting cross validation +2024-05-01 15:51:35,689:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:51:39,152:INFO:Calculating mean and std +2024-05-01 15:51:39,169:INFO:Creating metrics dataframe +2024-05-01 15:51:39,239:INFO:Uploading results into container +2024-05-01 15:51:39,244:INFO:Uploading model into container now +2024-05-01 15:51:39,247:INFO:_master_model_container: 5 +2024-05-01 15:51:39,248:INFO:_display_container: 2 +2024-05-01 15:51:39,251:INFO:Lars(random_state=6537) +2024-05-01 15:51:39,251:INFO:create_model() successfully completed...................................... +2024-05-01 15:51:39,660:INFO:SubProcess create_model() end ================================== +2024-05-01 15:51:39,661:INFO:Creating metrics dataframe +2024-05-01 15:51:39,778:INFO:Initializing Lasso Least Angle Regression +2024-05-01 15:51:39,779:INFO:Total runtime is 0.3856135169665019 minutes +2024-05-01 15:51:39,809:INFO:SubProcess create_model() called ================================== +2024-05-01 15:51:39,812:INFO:Initializing create_model() +2024-05-01 15:51:39,813:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:51:39,814:INFO:Checking exceptions +2024-05-01 15:51:39,814:INFO:Importing libraries +2024-05-01 15:51:39,815:INFO:Copying training dataset +2024-05-01 15:51:39,884:INFO:Defining folds +2024-05-01 15:51:39,885:INFO:Declaring metric variables +2024-05-01 15:51:39,957:INFO:Importing untrained model +2024-05-01 15:51:40,008:INFO:Lasso Least Angle Regression Imported successfully +2024-05-01 15:51:40,155:INFO:Starting cross validation +2024-05-01 15:51:40,179:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:51:43,275:INFO:Calculating mean and std +2024-05-01 15:51:43,338:INFO:Creating metrics dataframe +2024-05-01 15:51:43,408:INFO:Uploading results into container +2024-05-01 15:51:43,416:INFO:Uploading model into container now +2024-05-01 15:51:43,420:INFO:_master_model_container: 6 +2024-05-01 15:51:43,426:INFO:_display_container: 2 +2024-05-01 15:51:43,429:INFO:LassoLars(random_state=6537) +2024-05-01 15:51:43,431:INFO:create_model() successfully completed...................................... +2024-05-01 15:51:43,848:INFO:SubProcess create_model() end ================================== +2024-05-01 15:51:43,849:INFO:Creating metrics dataframe +2024-05-01 15:51:43,960:INFO:Initializing Orthogonal Matching Pursuit +2024-05-01 15:51:43,961:INFO:Total runtime is 0.45529096921285 minutes +2024-05-01 15:51:43,992:INFO:SubProcess create_model() called ================================== +2024-05-01 15:51:43,995:INFO:Initializing create_model() +2024-05-01 15:51:43,997:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:51:43,998:INFO:Checking exceptions +2024-05-01 15:51:43,998:INFO:Importing libraries +2024-05-01 15:51:43,999:INFO:Copying training dataset +2024-05-01 15:51:44,078:INFO:Defining folds +2024-05-01 15:51:44,080:INFO:Declaring metric variables +2024-05-01 15:51:44,134:INFO:Importing untrained model +2024-05-01 15:51:44,189:INFO:Orthogonal Matching Pursuit Imported successfully +2024-05-01 15:51:44,845:INFO:Starting cross validation +2024-05-01 15:51:44,859:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:51:47,874:INFO:Calculating mean and std +2024-05-01 15:51:47,899:INFO:Creating metrics dataframe +2024-05-01 15:51:47,952:INFO:Uploading results into container +2024-05-01 15:51:47,960:INFO:Uploading model into container now +2024-05-01 15:51:47,964:INFO:_master_model_container: 7 +2024-05-01 15:51:47,965:INFO:_display_container: 2 +2024-05-01 15:51:47,967:INFO:OrthogonalMatchingPursuit() +2024-05-01 15:51:47,967:INFO:create_model() successfully completed...................................... +2024-05-01 15:51:48,388:INFO:SubProcess create_model() end ================================== +2024-05-01 15:51:48,390:INFO:Creating metrics dataframe +2024-05-01 15:51:48,507:INFO:Initializing Bayesian Ridge +2024-05-01 15:51:48,508:INFO:Total runtime is 0.5311039129892986 minutes +2024-05-01 15:51:48,541:INFO:SubProcess create_model() called ================================== +2024-05-01 15:51:48,546:INFO:Initializing create_model() +2024-05-01 15:51:48,547:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:51:48,548:INFO:Checking exceptions +2024-05-01 15:51:48,549:INFO:Importing libraries +2024-05-01 15:51:48,551:INFO:Copying training dataset +2024-05-01 15:51:48,634:INFO:Defining folds +2024-05-01 15:51:48,640:INFO:Declaring metric variables +2024-05-01 15:51:48,704:INFO:Importing untrained model +2024-05-01 15:51:48,740:INFO:Bayesian Ridge Imported successfully +2024-05-01 15:51:48,850:INFO:Starting cross validation +2024-05-01 15:51:48,861:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:51:51,838:INFO:Calculating mean and std +2024-05-01 15:51:51,850:INFO:Creating metrics dataframe +2024-05-01 15:51:51,911:INFO:Uploading results into container +2024-05-01 15:51:51,937:INFO:Uploading model into container now +2024-05-01 15:51:51,940:INFO:_master_model_container: 8 +2024-05-01 15:51:51,941:INFO:_display_container: 2 +2024-05-01 15:51:51,944:INFO:BayesianRidge() +2024-05-01 15:51:51,945:INFO:create_model() successfully completed...................................... +2024-05-01 15:51:52,384:INFO:SubProcess create_model() end ================================== +2024-05-01 15:51:52,385:INFO:Creating metrics dataframe +2024-05-01 15:51:52,506:INFO:Initializing Passive Aggressive Regressor +2024-05-01 15:51:52,507:INFO:Total runtime is 0.5977506319681805 minutes +2024-05-01 15:51:52,539:INFO:SubProcess create_model() called ================================== +2024-05-01 15:51:52,541:INFO:Initializing create_model() +2024-05-01 15:51:52,543:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:51:52,547:INFO:Checking exceptions +2024-05-01 15:51:52,550:INFO:Importing libraries +2024-05-01 15:51:52,551:INFO:Copying training dataset +2024-05-01 15:51:52,623:INFO:Defining folds +2024-05-01 15:51:52,624:INFO:Declaring metric variables +2024-05-01 15:51:52,692:INFO:Importing untrained model +2024-05-01 15:51:52,743:INFO:Passive Aggressive Regressor Imported successfully +2024-05-01 15:51:52,875:INFO:Starting cross validation +2024-05-01 15:51:52,894:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:51:57,552:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:51:57,662:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:51:57,672:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:51:57,743:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:52:02,352:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:52:02,472:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:52:02,480:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:52:02,694:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:52:05,740:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:52:05,772:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 15:52:06,012:INFO:Calculating mean and std +2024-05-01 15:52:06,027:INFO:Creating metrics dataframe +2024-05-01 15:52:06,095:INFO:Uploading results into container +2024-05-01 15:52:06,099:INFO:Uploading model into container now +2024-05-01 15:52:06,103:INFO:_master_model_container: 9 +2024-05-01 15:52:06,104:INFO:_display_container: 2 +2024-05-01 15:52:06,108:INFO:PassiveAggressiveRegressor(random_state=6537) +2024-05-01 15:52:06,108:INFO:create_model() successfully completed...................................... +2024-05-01 15:52:06,536:INFO:SubProcess create_model() end ================================== +2024-05-01 15:52:06,537:INFO:Creating metrics dataframe +2024-05-01 15:52:06,656:INFO:Initializing Huber Regressor +2024-05-01 15:52:06,657:INFO:Total runtime is 0.8335586667060854 minutes +2024-05-01 15:52:06,688:INFO:SubProcess create_model() called ================================== +2024-05-01 15:52:06,691:INFO:Initializing create_model() +2024-05-01 15:52:06,692:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:52:06,694:INFO:Checking exceptions +2024-05-01 15:52:06,695:INFO:Importing libraries +2024-05-01 15:52:06,695:INFO:Copying training dataset +2024-05-01 15:52:06,758:INFO:Defining folds +2024-05-01 15:52:06,758:INFO:Declaring metric variables +2024-05-01 15:52:06,835:INFO:Importing untrained model +2024-05-01 15:52:06,895:INFO:Huber Regressor Imported successfully +2024-05-01 15:52:07,000:INFO:Starting cross validation +2024-05-01 15:52:07,012:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:52:10,803:INFO:Calculating mean and std +2024-05-01 15:52:10,814:INFO:Creating metrics dataframe +2024-05-01 15:52:10,861:INFO:Uploading results into container +2024-05-01 15:52:10,868:INFO:Uploading model into container now +2024-05-01 15:52:10,872:INFO:_master_model_container: 10 +2024-05-01 15:52:10,873:INFO:_display_container: 2 +2024-05-01 15:52:10,875:INFO:HuberRegressor() +2024-05-01 15:52:10,876:INFO:create_model() successfully completed...................................... +2024-05-01 15:52:11,364:INFO:SubProcess create_model() end ================================== +2024-05-01 15:52:11,375:INFO:Creating metrics dataframe +2024-05-01 15:52:11,684:INFO:Initializing K Neighbors Regressor +2024-05-01 15:52:11,684:INFO:Total runtime is 0.9173737883567812 minutes +2024-05-01 15:52:11,798:INFO:SubProcess create_model() called ================================== +2024-05-01 15:52:11,800:INFO:Initializing create_model() +2024-05-01 15:52:11,801:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:52:11,802:INFO:Checking exceptions +2024-05-01 15:52:11,803:INFO:Importing libraries +2024-05-01 15:52:11,803:INFO:Copying training dataset +2024-05-01 15:52:11,944:INFO:Defining folds +2024-05-01 15:52:11,948:INFO:Declaring metric variables +2024-05-01 15:52:12,078:INFO:Importing untrained model +2024-05-01 15:52:12,183:INFO:K Neighbors Regressor Imported successfully +2024-05-01 15:52:12,369:INFO:Starting cross validation +2024-05-01 15:52:12,381:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:52:16,460:INFO:Calculating mean and std +2024-05-01 15:52:16,472:INFO:Creating metrics dataframe +2024-05-01 15:52:16,540:INFO:Uploading results into container +2024-05-01 15:52:16,545:INFO:Uploading model into container now +2024-05-01 15:52:16,548:INFO:_master_model_container: 11 +2024-05-01 15:52:16,549:INFO:_display_container: 2 +2024-05-01 15:52:16,551:INFO:KNeighborsRegressor(n_jobs=-1) +2024-05-01 15:52:16,552:INFO:create_model() successfully completed...................................... +2024-05-01 15:52:16,976:INFO:SubProcess create_model() end ================================== +2024-05-01 15:52:16,977:INFO:Creating metrics dataframe +2024-05-01 15:52:17,106:INFO:Initializing Decision Tree Regressor +2024-05-01 15:52:17,108:INFO:Total runtime is 1.0077583630879723 minutes +2024-05-01 15:52:17,140:INFO:SubProcess create_model() called ================================== +2024-05-01 15:52:17,142:INFO:Initializing create_model() +2024-05-01 15:52:17,143:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:52:17,144:INFO:Checking exceptions +2024-05-01 15:52:17,145:INFO:Importing libraries +2024-05-01 15:52:17,145:INFO:Copying training dataset +2024-05-01 15:52:17,243:INFO:Defining folds +2024-05-01 15:52:17,245:INFO:Declaring metric variables +2024-05-01 15:52:17,303:INFO:Importing untrained model +2024-05-01 15:52:17,345:INFO:Decision Tree Regressor Imported successfully +2024-05-01 15:52:17,466:INFO:Starting cross validation +2024-05-01 15:52:17,477:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:52:21,667:INFO:Calculating mean and std +2024-05-01 15:52:21,683:INFO:Creating metrics dataframe +2024-05-01 15:52:21,746:INFO:Uploading results into container +2024-05-01 15:52:21,751:INFO:Uploading model into container now +2024-05-01 15:52:21,754:INFO:_master_model_container: 12 +2024-05-01 15:52:21,754:INFO:_display_container: 2 +2024-05-01 15:52:21,757:INFO:DecisionTreeRegressor(random_state=6537) +2024-05-01 15:52:21,758:INFO:create_model() successfully completed...................................... +2024-05-01 15:52:22,183:INFO:SubProcess create_model() end ================================== +2024-05-01 15:52:22,185:INFO:Creating metrics dataframe +2024-05-01 15:52:22,317:INFO:Initializing Random Forest Regressor +2024-05-01 15:52:22,318:INFO:Total runtime is 1.0946040630340579 minutes +2024-05-01 15:52:22,353:INFO:SubProcess create_model() called ================================== +2024-05-01 15:52:22,356:INFO:Initializing create_model() +2024-05-01 15:52:22,357:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:52:22,357:INFO:Checking exceptions +2024-05-01 15:52:22,358:INFO:Importing libraries +2024-05-01 15:52:22,358:INFO:Copying training dataset +2024-05-01 15:52:22,448:INFO:Defining folds +2024-05-01 15:52:22,449:INFO:Declaring metric variables +2024-05-01 15:52:22,522:INFO:Importing untrained model +2024-05-01 15:52:22,567:INFO:Random Forest Regressor Imported successfully +2024-05-01 15:52:22,708:INFO:Starting cross validation +2024-05-01 15:52:22,728:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:53:40,233:INFO:Calculating mean and std +2024-05-01 15:53:40,244:INFO:Creating metrics dataframe +2024-05-01 15:53:40,312:INFO:Uploading results into container +2024-05-01 15:53:40,323:INFO:Uploading model into container now +2024-05-01 15:53:40,326:INFO:_master_model_container: 13 +2024-05-01 15:53:40,327:INFO:_display_container: 2 +2024-05-01 15:53:40,330:INFO:RandomForestRegressor(n_jobs=-1, random_state=6537) +2024-05-01 15:53:40,331:INFO:create_model() successfully completed...................................... +2024-05-01 15:53:40,796:INFO:SubProcess create_model() end ================================== +2024-05-01 15:53:40,797:INFO:Creating metrics dataframe +2024-05-01 15:53:40,929:INFO:Initializing Extra Trees Regressor +2024-05-01 15:53:40,930:INFO:Total runtime is 2.404798495769501 minutes +2024-05-01 15:53:40,978:INFO:SubProcess create_model() called ================================== +2024-05-01 15:53:40,980:INFO:Initializing create_model() +2024-05-01 15:53:40,981:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:53:40,983:INFO:Checking exceptions +2024-05-01 15:53:40,984:INFO:Importing libraries +2024-05-01 15:53:40,985:INFO:Copying training dataset +2024-05-01 15:53:41,094:INFO:Defining folds +2024-05-01 15:53:41,096:INFO:Declaring metric variables +2024-05-01 15:53:41,148:INFO:Importing untrained model +2024-05-01 15:53:41,225:INFO:Extra Trees Regressor Imported successfully +2024-05-01 15:53:41,351:INFO:Starting cross validation +2024-05-01 15:53:41,363:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:54:22,641:INFO:Calculating mean and std +2024-05-01 15:54:22,653:INFO:Creating metrics dataframe +2024-05-01 15:54:22,712:INFO:Uploading results into container +2024-05-01 15:54:22,729:INFO:Uploading model into container now +2024-05-01 15:54:22,734:INFO:_master_model_container: 14 +2024-05-01 15:54:22,735:INFO:_display_container: 2 +2024-05-01 15:54:22,739:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=6537) +2024-05-01 15:54:22,739:INFO:create_model() successfully completed...................................... +2024-05-01 15:54:23,233:INFO:SubProcess create_model() end ================================== +2024-05-01 15:54:23,234:INFO:Creating metrics dataframe +2024-05-01 15:54:23,377:INFO:Initializing AdaBoost Regressor +2024-05-01 15:54:23,378:INFO:Total runtime is 3.1122614502906805 minutes +2024-05-01 15:54:23,417:INFO:SubProcess create_model() called ================================== +2024-05-01 15:54:23,419:INFO:Initializing create_model() +2024-05-01 15:54:23,420:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:54:23,421:INFO:Checking exceptions +2024-05-01 15:54:23,423:INFO:Importing libraries +2024-05-01 15:54:23,424:INFO:Copying training dataset +2024-05-01 15:54:23,499:INFO:Defining folds +2024-05-01 15:54:23,500:INFO:Declaring metric variables +2024-05-01 15:54:23,562:INFO:Importing untrained model +2024-05-01 15:54:23,620:INFO:AdaBoost Regressor Imported successfully +2024-05-01 15:54:23,756:INFO:Starting cross validation +2024-05-01 15:54:23,777:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:54:38,203:INFO:Calculating mean and std +2024-05-01 15:54:38,215:INFO:Creating metrics dataframe +2024-05-01 15:54:38,280:INFO:Uploading results into container +2024-05-01 15:54:38,285:INFO:Uploading model into container now +2024-05-01 15:54:38,288:INFO:_master_model_container: 15 +2024-05-01 15:54:38,288:INFO:_display_container: 2 +2024-05-01 15:54:38,290:INFO:AdaBoostRegressor(random_state=6537) +2024-05-01 15:54:38,291:INFO:create_model() successfully completed...................................... +2024-05-01 15:54:38,714:INFO:SubProcess create_model() end ================================== +2024-05-01 15:54:38,715:INFO:Creating metrics dataframe +2024-05-01 15:54:38,880:INFO:Initializing Gradient Boosting Regressor +2024-05-01 15:54:38,881:INFO:Total runtime is 3.370655469099681 minutes +2024-05-01 15:54:38,926:INFO:SubProcess create_model() called ================================== +2024-05-01 15:54:38,928:INFO:Initializing create_model() +2024-05-01 15:54:38,929:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:54:38,930:INFO:Checking exceptions +2024-05-01 15:54:38,930:INFO:Importing libraries +2024-05-01 15:54:38,931:INFO:Copying training dataset +2024-05-01 15:54:39,074:INFO:Defining folds +2024-05-01 15:54:39,075:INFO:Declaring metric variables +2024-05-01 15:54:39,120:INFO:Importing untrained model +2024-05-01 15:54:39,166:INFO:Gradient Boosting Regressor Imported successfully +2024-05-01 15:54:39,308:INFO:Starting cross validation +2024-05-01 15:54:39,358:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:55:02,990:INFO:Calculating mean and std +2024-05-01 15:55:02,998:INFO:Creating metrics dataframe +2024-05-01 15:55:03,071:INFO:Uploading results into container +2024-05-01 15:55:03,079:INFO:Uploading model into container now +2024-05-01 15:55:03,081:INFO:_master_model_container: 16 +2024-05-01 15:55:03,081:INFO:_display_container: 2 +2024-05-01 15:55:03,084:INFO:GradientBoostingRegressor(random_state=6537) +2024-05-01 15:55:03,084:INFO:create_model() successfully completed...................................... +2024-05-01 15:55:03,471:INFO:SubProcess create_model() end ================================== +2024-05-01 15:55:03,472:INFO:Creating metrics dataframe +2024-05-01 15:55:03,616:INFO:Initializing Extreme Gradient Boosting +2024-05-01 15:55:03,617:INFO:Total runtime is 3.782911018530528 minutes +2024-05-01 15:55:03,651:INFO:SubProcess create_model() called ================================== +2024-05-01 15:55:03,653:INFO:Initializing create_model() +2024-05-01 15:55:03,654:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:55:03,655:INFO:Checking exceptions +2024-05-01 15:55:03,656:INFO:Importing libraries +2024-05-01 15:55:03,656:INFO:Copying training dataset +2024-05-01 15:55:03,706:INFO:Defining folds +2024-05-01 15:55:03,710:INFO:Declaring metric variables +2024-05-01 15:55:03,770:INFO:Importing untrained model +2024-05-01 15:55:03,827:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 15:55:03,947:INFO:Starting cross validation +2024-05-01 15:55:03,956:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:55:24,146:INFO:Calculating mean and std +2024-05-01 15:55:24,162:INFO:Creating metrics dataframe +2024-05-01 15:55:24,283:INFO:Uploading results into container +2024-05-01 15:55:24,289:INFO:Uploading model into container now +2024-05-01 15:55:24,292:INFO:_master_model_container: 17 +2024-05-01 15:55:24,293:INFO:_display_container: 2 +2024-05-01 15:55:24,303: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, predictor=None, random_state=6537, + reg_alpha=None, reg_lambda=None, ...) +2024-05-01 15:55:24,304:INFO:create_model() successfully completed...................................... +2024-05-01 15:55:24,855:INFO:SubProcess create_model() end ================================== +2024-05-01 15:55:24,858:INFO:Creating metrics dataframe +2024-05-01 15:55:25,014:INFO:Initializing Light Gradient Boosting Machine +2024-05-01 15:55:25,016:INFO:Total runtime is 4.139549009005229 minutes +2024-05-01 15:55:25,059:INFO:SubProcess create_model() called ================================== +2024-05-01 15:55:25,061:INFO:Initializing create_model() +2024-05-01 15:55:25,063:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:55:25,064:INFO:Checking exceptions +2024-05-01 15:55:25,064:INFO:Importing libraries +2024-05-01 15:55:25,065:INFO:Copying training dataset +2024-05-01 15:55:25,128:INFO:Defining folds +2024-05-01 15:55:25,129:INFO:Declaring metric variables +2024-05-01 15:55:25,206:INFO:Importing untrained model +2024-05-01 15:55:25,253:INFO:Light Gradient Boosting Machine Imported successfully +2024-05-01 15:55:25,394:INFO:Starting cross validation +2024-05-01 15:55:25,407:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:55:36,473:INFO:Calculating mean and std +2024-05-01 15:55:36,485:INFO:Creating metrics dataframe +2024-05-01 15:55:36,526:INFO:Uploading results into container +2024-05-01 15:55:36,530:INFO:Uploading model into container now +2024-05-01 15:55:36,534:INFO:_master_model_container: 18 +2024-05-01 15:55:36,535:INFO:_display_container: 2 +2024-05-01 15:55:36,540:INFO:LGBMRegressor(n_jobs=-1, random_state=6537) +2024-05-01 15:55:36,540:INFO:create_model() successfully completed...................................... +2024-05-01 15:55:37,064:INFO:SubProcess create_model() end ================================== +2024-05-01 15:55:37,065:INFO:Creating metrics dataframe +2024-05-01 15:55:37,225:INFO:Initializing Dummy Regressor +2024-05-01 15:55:37,226:INFO:Total runtime is 4.343063326676687 minutes +2024-05-01 15:55:37,262:INFO:SubProcess create_model() called ================================== +2024-05-01 15:55:37,264:INFO:Initializing create_model() +2024-05-01 15:55:37,266:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:55:37,266:INFO:Checking exceptions +2024-05-01 15:55:37,267:INFO:Importing libraries +2024-05-01 15:55:37,267:INFO:Copying training dataset +2024-05-01 15:55:37,359:INFO:Defining folds +2024-05-01 15:55:37,360:INFO:Declaring metric variables +2024-05-01 15:55:37,412:INFO:Importing untrained model +2024-05-01 15:55:37,460:INFO:Dummy Regressor Imported successfully +2024-05-01 15:55:37,605:INFO:Starting cross validation +2024-05-01 15:55:37,614:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:55:41,628:INFO:Calculating mean and std +2024-05-01 15:55:41,635:INFO:Creating metrics dataframe +2024-05-01 15:55:41,756:INFO:Uploading results into container +2024-05-01 15:55:41,762:INFO:Uploading model into container now +2024-05-01 15:55:41,765:INFO:_master_model_container: 19 +2024-05-01 15:55:41,787:INFO:_display_container: 2 +2024-05-01 15:55:41,791:INFO:DummyRegressor() +2024-05-01 15:55:41,792:INFO:create_model() successfully completed...................................... +2024-05-01 15:55:42,219:INFO:SubProcess create_model() end ================================== +2024-05-01 15:55:42,220:INFO:Creating metrics dataframe +2024-05-01 15:55:42,520:INFO:Initializing create_model() +2024-05-01 15:55:42,521:INFO:create_model(self=, 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, predictor=None, random_state=6537, + reg_alpha=None, reg_lambda=None, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:55:42,522:INFO:Checking exceptions +2024-05-01 15:55:42,533:INFO:Importing libraries +2024-05-01 15:55:42,534:INFO:Copying training dataset +2024-05-01 15:55:42,617:INFO:Defining folds +2024-05-01 15:55:42,618:INFO:Declaring metric variables +2024-05-01 15:55:42,619:INFO:Importing untrained model +2024-05-01 15:55:42,620:INFO:Declaring custom model +2024-05-01 15:55:42,634:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 15:55:42,643:INFO:Cross validation set to False +2024-05-01 15:55:42,644:INFO:Fitting Model +2024-05-01 15:55:46,226: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, predictor='auto', + random_state=6537, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 15:55:46,227:INFO:create_model() successfully completed...................................... +2024-05-01 15:55:47,273:INFO:_master_model_container: 19 +2024-05-01 15:55:47,274:INFO:_display_container: 2 +2024-05-01 15:55:47,308: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, predictor='auto', + random_state=6537, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 15:55:47,310:INFO:compare_models() successfully completed...................................... +2024-05-01 15:55:47,739:INFO:Initializing create_model() +2024-05-01 15:55:47,740:INFO:create_model(self=, estimator=et, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 15:55:47,740:INFO:Checking exceptions +2024-05-01 15:55:48,112:INFO:Importing libraries +2024-05-01 15:55:48,113:INFO:Copying training dataset +2024-05-01 15:55:48,426:INFO:Defining folds +2024-05-01 15:55:48,427:INFO:Declaring metric variables +2024-05-01 15:55:48,647:INFO:Importing untrained model +2024-05-01 15:55:48,731:INFO:Extra Trees Regressor Imported successfully +2024-05-01 15:55:48,927:INFO:Starting cross validation +2024-05-01 15:55:48,943:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 15:56:31,802:INFO:Calculating mean and std +2024-05-01 15:56:31,814:INFO:Creating metrics dataframe +2024-05-01 15:56:31,922:INFO:Finalizing model +2024-05-01 15:56:37,398:INFO:Uploading results into container +2024-05-01 15:56:37,406:INFO:Uploading model into container now +2024-05-01 15:56:37,575:INFO:_master_model_container: 20 +2024-05-01 15:56:37,575:INFO:_display_container: 3 +2024-05-01 15:56:37,580:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=6537) +2024-05-01 15:56:37,581:INFO:create_model() successfully completed...................................... +2024-05-01 15:56:38,514:INFO:Initializing evaluate_model() +2024-05-01 15:56:38,515:INFO:evaluate_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=6537), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) +2024-05-01 15:56:38,610:INFO:Initializing plot_model() +2024-05-01 15:56:38,610:INFO:plot_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=6537), plot=pipeline, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-05-01 15:56:38,611:INFO:Checking exceptions +2024-05-01 15:56:38,948:INFO:Preloading libraries +2024-05-01 15:56:39,812:INFO:Copying training dataset +2024-05-01 15:56:39,813:INFO:Plot type: pipeline +2024-05-01 15:56:42,090:INFO:Visual Rendered Successfully +2024-05-01 15:56:42,549:INFO:plot_model() successfully completed...................................... +2024-05-01 15:56:42,845:INFO:Initializing tune_model() +2024-05-01 15:56:42,846:INFO:tune_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=6537), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-05-01 15:56:42,847:INFO:Checking exceptions +2024-05-01 15:56:43,020:INFO:Copying training dataset +2024-05-01 15:56:43,109:INFO:Checking base model +2024-05-01 15:56:43,111:INFO:Base model : Extra Trees Regressor +2024-05-01 15:56:43,204:INFO:Declaring metric variables +2024-05-01 15:56:43,553:INFO:Defining Hyperparameters +2024-05-01 15:56:44,280:INFO:Tuning with n_jobs=-1 +2024-05-01 15:56:44,280:INFO:Initializing RandomizedSearchCV +2024-05-01 16:21:28,599:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 16:21:28,600:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 16:21:28,600:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 16:21:28,601:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 16:21:30,225:INFO:PyCaret RegressionExperiment +2024-05-01 16:21:30,226:INFO:Logging name: reg-default-name +2024-05-01 16:21:30,227:INFO:ML Usecase: MLUsecase.REGRESSION +2024-05-01 16:21:30,228:INFO:version 3.3.0 +2024-05-01 16:21:30,228:INFO:Initializing setup() +2024-05-01 16:21:30,229:INFO:self.USI: 92ea +2024-05-01 16:21:30,230:INFO:self._variable_keys: {'pipeline', 'USI', 'X_train', 'data', 'y', 'gpu_param', 'exp_id', '_available_plots', 'y_train', 'fold_groups_param', 'fold_shuffle_param', 'idx', 'X_test', 'logging_param', '_ml_usecase', 'exp_name_log', 'X', 'memory', 'seed', 'n_jobs_param', 'fold_generator', 'y_test', 'transform_target_param', 'target_param', 'log_plots_param', 'gpu_n_jobs_param', 'html_param'} +2024-05-01 16:21:30,231:INFO:Checking environment +2024-05-01 16:21:30,231:INFO:python_version: 3.11.0 +2024-05-01 16:21:30,232:INFO:python_build: ('main', 'Oct 24 2022 18:26:48') +2024-05-01 16:21:30,232:INFO:machine: AMD64 +2024-05-01 16:21:30,233:INFO:platform: Windows-10-10.0.22000-SP0 +2024-05-01 16:21:30,246:INFO:Memory: svmem(total=8467492864, available=2892337152, percent=65.8, used=5575155712, free=2892337152) +2024-05-01 16:21:30,247:INFO:Physical Core: 2 +2024-05-01 16:21:30,248:INFO:Logical Core: 4 +2024-05-01 16:21:30,248:INFO:Checking libraries +2024-05-01 16:21:30,249:INFO:System: +2024-05-01 16:21:30,249:INFO: python: 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] +2024-05-01 16:21:30,250:INFO:executable: c:\Users\Jason\Documents\DataB3\.venv\Scripts\python.exe +2024-05-01 16:21:30,250:INFO: machine: Windows-10-10.0.22000-SP0 +2024-05-01 16:21:30,251:INFO:PyCaret required dependencies: +2024-05-01 16:21:30,611:INFO: pip: 24.0 +2024-05-01 16:21:30,612:INFO: setuptools: 65.5.0 +2024-05-01 16:21:30,612:INFO: pycaret: 3.3.0 +2024-05-01 16:21:30,613:INFO: IPython: 8.23.0 +2024-05-01 16:21:30,613:INFO: ipywidgets: 8.1.2 +2024-05-01 16:21:30,614:INFO: tqdm: 4.66.2 +2024-05-01 16:21:30,614:INFO: numpy: 1.24.4 +2024-05-01 16:21:30,614:INFO: pandas: 1.5.3 +2024-05-01 16:21:30,615:INFO: jinja2: 3.1.3 +2024-05-01 16:21:30,615:INFO: scipy: 1.11.4 +2024-05-01 16:21:30,616:INFO: joblib: 1.3.2 +2024-05-01 16:21:30,616:INFO: sklearn: 1.4.1.post1 +2024-05-01 16:21:30,617:INFO: pyod: 1.1.3 +2024-05-01 16:21:30,618:INFO: imblearn: 0.12.2 +2024-05-01 16:21:30,618:INFO: category_encoders: 2.6.3 +2024-05-01 16:21:30,619:INFO: lightgbm: 4.3.0 +2024-05-01 16:21:30,620:INFO: numba: 0.59.1 +2024-05-01 16:21:30,621:INFO: requests: 2.31.0 +2024-05-01 16:21:30,622:INFO: matplotlib: 3.8.3 +2024-05-01 16:21:30,623:INFO: scikitplot: 0.3.7 +2024-05-01 16:21:30,624:INFO: yellowbrick: 1.5 +2024-05-01 16:21:30,624:INFO: plotly: 5.20.0 +2024-05-01 16:21:30,624:INFO: plotly-resampler: Not installed +2024-05-01 16:21:30,625:INFO: kaleido: 0.2.1 +2024-05-01 16:21:30,625:INFO: schemdraw: 0.15 +2024-05-01 16:21:30,625:INFO: statsmodels: 0.14.1 +2024-05-01 16:21:30,625:INFO: sktime: 0.28.0 +2024-05-01 16:21:30,626:INFO: tbats: 1.1.3 +2024-05-01 16:21:30,626:INFO: pmdarima: 2.0.4 +2024-05-01 16:21:30,626:INFO: psutil: 5.9.8 +2024-05-01 16:21:30,627:INFO: markupsafe: 2.1.5 +2024-05-01 16:21:30,627:INFO: pickle5: Not installed +2024-05-01 16:21:30,627:INFO: cloudpickle: 3.0.0 +2024-05-01 16:21:30,627:INFO: deprecation: 2.1.0 +2024-05-01 16:21:30,628:INFO: xxhash: 3.4.1 +2024-05-01 16:21:30,628:INFO: wurlitzer: Not installed +2024-05-01 16:21:30,628:INFO:PyCaret optional dependencies: +2024-05-01 16:21:30,753:INFO: shap: Not installed +2024-05-01 16:21:30,754:INFO: interpret: Not installed +2024-05-01 16:21:30,754:INFO: umap: Not installed +2024-05-01 16:21:30,755:INFO: ydata_profiling: 4.7.0 +2024-05-01 16:21:30,755:INFO: explainerdashboard: Not installed +2024-05-01 16:21:30,756:INFO: autoviz: Not installed +2024-05-01 16:21:30,756:INFO: fairlearn: Not installed +2024-05-01 16:21:30,757:INFO: deepchecks: Not installed +2024-05-01 16:21:30,757:INFO: xgboost: 1.6.2 +2024-05-01 16:21:30,758:INFO: catboost: Not installed +2024-05-01 16:21:30,758:INFO: kmodes: Not installed +2024-05-01 16:21:30,759:INFO: mlxtend: Not installed +2024-05-01 16:21:30,759:INFO: statsforecast: Not installed +2024-05-01 16:21:30,761:INFO: tune_sklearn: Not installed +2024-05-01 16:21:30,762:INFO: ray: Not installed +2024-05-01 16:21:30,763:INFO: hyperopt: Not installed +2024-05-01 16:21:30,764:INFO: optuna: 3.6.1 +2024-05-01 16:21:30,764:INFO: skopt: Not installed +2024-05-01 16:21:30,765:INFO: mlflow: Not installed +2024-05-01 16:21:30,765:INFO: gradio: Not installed +2024-05-01 16:21:30,765:INFO: fastapi: Not installed +2024-05-01 16:21:30,766:INFO: uvicorn: Not installed +2024-05-01 16:21:30,766:INFO: m2cgen: Not installed +2024-05-01 16:21:30,767:INFO: evidently: Not installed +2024-05-01 16:21:30,767:INFO: fugue: Not installed +2024-05-01 16:21:30,768:INFO: streamlit: 1.33.0 +2024-05-01 16:21:30,768:INFO: prophet: 1.1.5 +2024-05-01 16:21:30,768:INFO:None +2024-05-01 16:21:30,769:INFO:Set up data. +2024-05-01 16:21:30,831:INFO:Set up folding strategy. +2024-05-01 16:21:30,832:INFO:Set up train/test split. +2024-05-01 16:21:30,873:INFO:Set up index. +2024-05-01 16:21:30,874:INFO:Assigning column types. +2024-05-01 16:21:30,907:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-05-01 16:21:30,909:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 16:21:30,989:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 16:21:31,087:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 16:21:32,081:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:21:32,605:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:21:32,611:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:21:33,326:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:21:33,329:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 16:21:33,384:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 16:21:33,475:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 16:21:34,170:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:21:34,705:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:21:34,708:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:21:34,737:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:21:34,740:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-05-01 16:21:34,791:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 16:21:34,845:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 16:21:35,590:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:21:36,115:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:21:36,120:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:21:36,147:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:21:36,215:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 16:21:36,266:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 16:21:36,939:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:21:37,484:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:21:37,488:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:21:37,528:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:21:37,531:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-05-01 16:21:37,658:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 16:21:38,324:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:21:38,911:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:21:38,914:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:21:38,944:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:21:39,051:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 16:21:39,812:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:21:40,417:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:21:40,421:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:21:40,447:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:21:40,449:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-05-01 16:21:41,251:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:21:41,997:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:21:42,003:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:21:42,047:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:21:43,277:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:21:44,122:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:21:44,128:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:21:44,163:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:21:44,167:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-05-01 16:21:45,557:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:21:46,523:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:21:46,601:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:21:47,969:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:21:48,734:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:21:48,776:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:21:48,778:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-05-01 16:21:50,560:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:21:50,611:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:21:52,654:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:21:52,696:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:21:52,710:INFO:Preparing preprocessing pipeline... +2024-05-01 16:21:52,711:INFO:Set up simple imputation. +2024-05-01 16:21:52,731:INFO:Set up encoding of categorical features. +2024-05-01 16:21:52,732:INFO:Set up feature normalization. +2024-05-01 16:21:53,461:INFO:Finished creating preprocessing pipeline. +2024-05-01 16:21:53,543:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\Jason\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['Date'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['Date'], + transformer=TargetEncoder(cols=['Date'], + handle_missing='return_nan'))), + ('normalize', TransformerWrapper(transformer=MinMaxScaler()))]) +2024-05-01 16:21:53,544:INFO:Creating final display dataframe. +2024-05-01 16:21:55,156:INFO:Setup _display_container: Description Value +0 Session id 3895 +1 Target Daily_Sales +2 Target type Regression +3 Original data shape (6435, 8) +4 Transformed data shape (6435, 8) +5 Transformed train set shape (4504, 8) +6 Transformed test set shape (1931, 8) +7 Numeric features 6 +8 Categorical features 1 +9 Preprocess True +10 Imputation type simple +11 Numeric imputation mean +12 Categorical imputation mode +13 Maximum one-hot encoding 25 +14 Encoding method None +15 Normalize True +16 Normalize method minmax +17 Fold Generator KFold +18 Fold Number 10 +19 CPU Jobs -1 +20 Use GPU False +21 Log Experiment False +22 Experiment Name reg-default-name +23 USI 92ea +2024-05-01 16:21:57,138:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:21:57,185:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:21:59,021:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:21:59,063:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:21:59,112:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour. + warnings.warn( + +2024-05-01 16:21:59,114:INFO:setup() successfully completed in 29.07s............... +2024-05-01 16:21:59,295:INFO:Initializing compare_models() +2024-05-01 16:21:59,296:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-05-01 16:21:59,296:INFO:Checking exceptions +2024-05-01 16:21:59,313:INFO:Preparing display monitor +2024-05-01 16:21:59,761:INFO:Initializing Linear Regression +2024-05-01 16:21:59,763:INFO:Total runtime is 1.6740957895914714e-05 minutes +2024-05-01 16:21:59,832:INFO:SubProcess create_model() called ================================== +2024-05-01 16:21:59,838:INFO:Initializing create_model() +2024-05-01 16:21:59,839:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:21:59,840:INFO:Checking exceptions +2024-05-01 16:21:59,841:INFO:Importing libraries +2024-05-01 16:21:59,842:INFO:Copying training dataset +2024-05-01 16:21:59,908:INFO:Defining folds +2024-05-01 16:21:59,910:INFO:Declaring metric variables +2024-05-01 16:21:59,948:INFO:Importing untrained model +2024-05-01 16:21:59,988:INFO:Linear Regression Imported successfully +2024-05-01 16:22:00,089:INFO:Starting cross validation +2024-05-01 16:22:00,240:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:22:41,653:INFO:Calculating mean and std +2024-05-01 16:22:41,670:INFO:Creating metrics dataframe +2024-05-01 16:22:41,723:INFO:Uploading results into container +2024-05-01 16:22:41,728:INFO:Uploading model into container now +2024-05-01 16:22:41,731:INFO:_master_model_container: 1 +2024-05-01 16:22:41,732:INFO:_display_container: 2 +2024-05-01 16:22:41,733:INFO:LinearRegression(n_jobs=-1) +2024-05-01 16:22:41,734:INFO:create_model() successfully completed...................................... +2024-05-01 16:22:42,121:INFO:SubProcess create_model() end ================================== +2024-05-01 16:22:42,122:INFO:Creating metrics dataframe +2024-05-01 16:22:42,214:INFO:Initializing Lasso Regression +2024-05-01 16:22:42,215:INFO:Total runtime is 0.7075749238332113 minutes +2024-05-01 16:22:42,250:INFO:SubProcess create_model() called ================================== +2024-05-01 16:22:42,253:INFO:Initializing create_model() +2024-05-01 16:22:42,254:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:22:42,254:INFO:Checking exceptions +2024-05-01 16:22:42,255:INFO:Importing libraries +2024-05-01 16:22:42,256:INFO:Copying training dataset +2024-05-01 16:22:42,306:INFO:Defining folds +2024-05-01 16:22:42,307:INFO:Declaring metric variables +2024-05-01 16:22:42,342:INFO:Importing untrained model +2024-05-01 16:22:42,395:INFO:Lasso Regression Imported successfully +2024-05-01 16:22:42,468:INFO:Starting cross validation +2024-05-01 16:22:42,480:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:22:45,171:INFO:Calculating mean and std +2024-05-01 16:22:45,182:INFO:Creating metrics dataframe +2024-05-01 16:22:45,229:INFO:Uploading results into container +2024-05-01 16:22:45,236:INFO:Uploading model into container now +2024-05-01 16:22:45,239:INFO:_master_model_container: 2 +2024-05-01 16:22:45,240:INFO:_display_container: 2 +2024-05-01 16:22:45,243:INFO:Lasso(random_state=3895) +2024-05-01 16:22:45,244:INFO:create_model() successfully completed...................................... +2024-05-01 16:22:45,578:INFO:SubProcess create_model() end ================================== +2024-05-01 16:22:45,579:INFO:Creating metrics dataframe +2024-05-01 16:22:45,646:INFO:Initializing Ridge Regression +2024-05-01 16:22:45,647:INFO:Total runtime is 0.7647716959317525 minutes +2024-05-01 16:22:45,675:INFO:SubProcess create_model() called ================================== +2024-05-01 16:22:45,677:INFO:Initializing create_model() +2024-05-01 16:22:45,679:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:22:45,679:INFO:Checking exceptions +2024-05-01 16:22:45,683:INFO:Importing libraries +2024-05-01 16:22:45,684:INFO:Copying training dataset +2024-05-01 16:22:45,725:INFO:Defining folds +2024-05-01 16:22:45,726:INFO:Declaring metric variables +2024-05-01 16:22:45,763:INFO:Importing untrained model +2024-05-01 16:22:45,794:INFO:Ridge Regression Imported successfully +2024-05-01 16:22:45,851:INFO:Starting cross validation +2024-05-01 16:22:45,861:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:22:48,315:INFO:Calculating mean and std +2024-05-01 16:22:48,326:INFO:Creating metrics dataframe +2024-05-01 16:22:48,359:INFO:Uploading results into container +2024-05-01 16:22:48,363:INFO:Uploading model into container now +2024-05-01 16:22:48,365:INFO:_master_model_container: 3 +2024-05-01 16:22:48,366:INFO:_display_container: 2 +2024-05-01 16:22:48,368:INFO:Ridge(random_state=3895) +2024-05-01 16:22:48,373:INFO:create_model() successfully completed...................................... +2024-05-01 16:22:48,718:INFO:SubProcess create_model() end ================================== +2024-05-01 16:22:48,719:INFO:Creating metrics dataframe +2024-05-01 16:22:48,797:INFO:Initializing Elastic Net +2024-05-01 16:22:48,798:INFO:Total runtime is 0.8172895312309265 minutes +2024-05-01 16:22:48,829:INFO:SubProcess create_model() called ================================== +2024-05-01 16:22:48,831:INFO:Initializing create_model() +2024-05-01 16:22:48,832:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:22:48,845:INFO:Checking exceptions +2024-05-01 16:22:48,845:INFO:Importing libraries +2024-05-01 16:22:48,846:INFO:Copying training dataset +2024-05-01 16:22:48,932:INFO:Defining folds +2024-05-01 16:22:48,933:INFO:Declaring metric variables +2024-05-01 16:22:48,969:INFO:Importing untrained model +2024-05-01 16:22:49,003:INFO:Elastic Net Imported successfully +2024-05-01 16:22:49,071:INFO:Starting cross validation +2024-05-01 16:22:49,081:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:22:51,718:INFO:Calculating mean and std +2024-05-01 16:22:51,733:INFO:Creating metrics dataframe +2024-05-01 16:22:51,769:INFO:Uploading results into container +2024-05-01 16:22:51,772:INFO:Uploading model into container now +2024-05-01 16:22:51,774:INFO:_master_model_container: 4 +2024-05-01 16:22:51,775:INFO:_display_container: 2 +2024-05-01 16:22:51,776:INFO:ElasticNet(random_state=3895) +2024-05-01 16:22:51,777:INFO:create_model() successfully completed...................................... +2024-05-01 16:22:52,076:INFO:SubProcess create_model() end ================================== +2024-05-01 16:22:52,077:INFO:Creating metrics dataframe +2024-05-01 16:22:52,165:INFO:Initializing Least Angle Regression +2024-05-01 16:22:52,166:INFO:Total runtime is 0.8734206318855285 minutes +2024-05-01 16:22:52,225:INFO:SubProcess create_model() called ================================== +2024-05-01 16:22:52,227:INFO:Initializing create_model() +2024-05-01 16:22:52,228:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:22:52,229:INFO:Checking exceptions +2024-05-01 16:22:52,230:INFO:Importing libraries +2024-05-01 16:22:52,231:INFO:Copying training dataset +2024-05-01 16:22:52,295:INFO:Defining folds +2024-05-01 16:22:52,296:INFO:Declaring metric variables +2024-05-01 16:22:52,352:INFO:Importing untrained model +2024-05-01 16:22:52,403:INFO:Least Angle Regression Imported successfully +2024-05-01 16:22:52,490:INFO:Starting cross validation +2024-05-01 16:22:52,504:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:22:55,730:INFO:Calculating mean and std +2024-05-01 16:22:55,741:INFO:Creating metrics dataframe +2024-05-01 16:22:55,786:INFO:Uploading results into container +2024-05-01 16:22:55,790:INFO:Uploading model into container now +2024-05-01 16:22:55,793:INFO:_master_model_container: 5 +2024-05-01 16:22:55,794:INFO:_display_container: 2 +2024-05-01 16:22:55,796:INFO:Lars(random_state=3895) +2024-05-01 16:22:55,797:INFO:create_model() successfully completed...................................... +2024-05-01 16:22:56,156:INFO:SubProcess create_model() end ================================== +2024-05-01 16:22:56,157:INFO:Creating metrics dataframe +2024-05-01 16:22:56,281:INFO:Initializing Lasso Least Angle Regression +2024-05-01 16:22:56,282:INFO:Total runtime is 0.9420068462689717 minutes +2024-05-01 16:22:56,319:INFO:SubProcess create_model() called ================================== +2024-05-01 16:22:56,326:INFO:Initializing create_model() +2024-05-01 16:22:56,328:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:22:56,330:INFO:Checking exceptions +2024-05-01 16:22:56,332:INFO:Importing libraries +2024-05-01 16:22:56,333:INFO:Copying training dataset +2024-05-01 16:22:56,390:INFO:Defining folds +2024-05-01 16:22:56,391:INFO:Declaring metric variables +2024-05-01 16:22:56,452:INFO:Importing untrained model +2024-05-01 16:22:56,490:INFO:Lasso Least Angle Regression Imported successfully +2024-05-01 16:22:56,577:INFO:Starting cross validation +2024-05-01 16:22:56,590:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:22:59,180:INFO:Calculating mean and std +2024-05-01 16:22:59,192:INFO:Creating metrics dataframe +2024-05-01 16:22:59,233:INFO:Uploading results into container +2024-05-01 16:22:59,236:INFO:Uploading model into container now +2024-05-01 16:22:59,238:INFO:_master_model_container: 6 +2024-05-01 16:22:59,239:INFO:_display_container: 2 +2024-05-01 16:22:59,240:INFO:LassoLars(random_state=3895) +2024-05-01 16:22:59,240:INFO:create_model() successfully completed...................................... +2024-05-01 16:22:59,530:INFO:SubProcess create_model() end ================================== +2024-05-01 16:22:59,531:INFO:Creating metrics dataframe +2024-05-01 16:22:59,606:INFO:Initializing Orthogonal Matching Pursuit +2024-05-01 16:22:59,607:INFO:Total runtime is 0.9974386612574259 minutes +2024-05-01 16:22:59,639:INFO:SubProcess create_model() called ================================== +2024-05-01 16:22:59,642:INFO:Initializing create_model() +2024-05-01 16:22:59,643:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:22:59,643:INFO:Checking exceptions +2024-05-01 16:22:59,645:INFO:Importing libraries +2024-05-01 16:22:59,645:INFO:Copying training dataset +2024-05-01 16:22:59,694:INFO:Defining folds +2024-05-01 16:22:59,696:INFO:Declaring metric variables +2024-05-01 16:22:59,735:INFO:Importing untrained model +2024-05-01 16:22:59,782:INFO:Orthogonal Matching Pursuit Imported successfully +2024-05-01 16:22:59,840:INFO:Starting cross validation +2024-05-01 16:22:59,858:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:23:02,405:INFO:Calculating mean and std +2024-05-01 16:23:02,416:INFO:Creating metrics dataframe +2024-05-01 16:23:02,460:INFO:Uploading results into container +2024-05-01 16:23:02,465:INFO:Uploading model into container now +2024-05-01 16:23:02,468:INFO:_master_model_container: 7 +2024-05-01 16:23:02,469:INFO:_display_container: 2 +2024-05-01 16:23:02,470:INFO:OrthogonalMatchingPursuit() +2024-05-01 16:23:02,471:INFO:create_model() successfully completed...................................... +2024-05-01 16:23:02,827:INFO:SubProcess create_model() end ================================== +2024-05-01 16:23:02,828:INFO:Creating metrics dataframe +2024-05-01 16:23:02,933:INFO:Initializing Bayesian Ridge +2024-05-01 16:23:02,934:INFO:Total runtime is 1.0528870066006977 minutes +2024-05-01 16:23:02,969:INFO:SubProcess create_model() called ================================== +2024-05-01 16:23:02,971:INFO:Initializing create_model() +2024-05-01 16:23:02,972:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:23:02,973:INFO:Checking exceptions +2024-05-01 16:23:02,973:INFO:Importing libraries +2024-05-01 16:23:02,974:INFO:Copying training dataset +2024-05-01 16:23:03,027:INFO:Defining folds +2024-05-01 16:23:03,028:INFO:Declaring metric variables +2024-05-01 16:23:03,073:INFO:Importing untrained model +2024-05-01 16:23:03,111:INFO:Bayesian Ridge Imported successfully +2024-05-01 16:23:03,178:INFO:Starting cross validation +2024-05-01 16:23:03,197:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:23:05,849:INFO:Calculating mean and std +2024-05-01 16:23:05,890:INFO:Creating metrics dataframe +2024-05-01 16:23:05,926:INFO:Uploading results into container +2024-05-01 16:23:05,929:INFO:Uploading model into container now +2024-05-01 16:23:05,932:INFO:_master_model_container: 8 +2024-05-01 16:23:05,932:INFO:_display_container: 2 +2024-05-01 16:23:05,935:INFO:BayesianRidge() +2024-05-01 16:23:05,935:INFO:create_model() successfully completed...................................... +2024-05-01 16:23:06,284:INFO:SubProcess create_model() end ================================== +2024-05-01 16:23:06,285:INFO:Creating metrics dataframe +2024-05-01 16:23:06,396:INFO:Initializing Passive Aggressive Regressor +2024-05-01 16:23:06,397:INFO:Total runtime is 1.1106000105539957 minutes +2024-05-01 16:23:06,437:INFO:SubProcess create_model() called ================================== +2024-05-01 16:23:06,439:INFO:Initializing create_model() +2024-05-01 16:23:06,440:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:23:06,441:INFO:Checking exceptions +2024-05-01 16:23:06,442:INFO:Importing libraries +2024-05-01 16:23:06,442:INFO:Copying training dataset +2024-05-01 16:23:06,493:INFO:Defining folds +2024-05-01 16:23:06,494:INFO:Declaring metric variables +2024-05-01 16:23:06,530:INFO:Importing untrained model +2024-05-01 16:23:06,566:INFO:Passive Aggressive Regressor Imported successfully +2024-05-01 16:23:06,630:INFO:Starting cross validation +2024-05-01 16:23:06,644:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:23:11,120:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:23:11,218:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:23:11,250:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:23:11,414:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:23:15,156:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:23:15,354:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:23:15,649:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:23:15,677:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:23:18,317:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:23:18,433:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:23:18,643:INFO:Calculating mean and std +2024-05-01 16:23:18,654:INFO:Creating metrics dataframe +2024-05-01 16:23:18,715:INFO:Uploading results into container +2024-05-01 16:23:18,719:INFO:Uploading model into container now +2024-05-01 16:23:18,722:INFO:_master_model_container: 9 +2024-05-01 16:23:18,723:INFO:_display_container: 2 +2024-05-01 16:23:18,731:INFO:PassiveAggressiveRegressor(random_state=3895) +2024-05-01 16:23:18,732:INFO:create_model() successfully completed...................................... +2024-05-01 16:23:19,113:INFO:SubProcess create_model() end ================================== +2024-05-01 16:23:19,114:INFO:Creating metrics dataframe +2024-05-01 16:23:19,227:INFO:Initializing Huber Regressor +2024-05-01 16:23:19,228:INFO:Total runtime is 1.3244510451952616 minutes +2024-05-01 16:23:19,260:INFO:SubProcess create_model() called ================================== +2024-05-01 16:23:19,263:INFO:Initializing create_model() +2024-05-01 16:23:19,264:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:23:19,264:INFO:Checking exceptions +2024-05-01 16:23:19,265:INFO:Importing libraries +2024-05-01 16:23:19,265:INFO:Copying training dataset +2024-05-01 16:23:19,330:INFO:Defining folds +2024-05-01 16:23:19,330:INFO:Declaring metric variables +2024-05-01 16:23:19,371:INFO:Importing untrained model +2024-05-01 16:23:19,405:INFO:Huber Regressor Imported successfully +2024-05-01 16:23:19,478:INFO:Starting cross validation +2024-05-01 16:23:19,492:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:23:22,607:INFO:Calculating mean and std +2024-05-01 16:23:22,613:INFO:Creating metrics dataframe +2024-05-01 16:23:22,639:INFO:Uploading results into container +2024-05-01 16:23:22,643:INFO:Uploading model into container now +2024-05-01 16:23:22,645:INFO:_master_model_container: 10 +2024-05-01 16:23:22,645:INFO:_display_container: 2 +2024-05-01 16:23:22,646:INFO:HuberRegressor() +2024-05-01 16:23:22,647:INFO:create_model() successfully completed...................................... +2024-05-01 16:23:22,951:INFO:SubProcess create_model() end ================================== +2024-05-01 16:23:22,952:INFO:Creating metrics dataframe +2024-05-01 16:23:23,023:INFO:Initializing K Neighbors Regressor +2024-05-01 16:23:23,024:INFO:Total runtime is 1.3877089222272236 minutes +2024-05-01 16:23:23,049:INFO:SubProcess create_model() called ================================== +2024-05-01 16:23:23,050:INFO:Initializing create_model() +2024-05-01 16:23:23,051:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:23:23,051:INFO:Checking exceptions +2024-05-01 16:23:23,052:INFO:Importing libraries +2024-05-01 16:23:23,052:INFO:Copying training dataset +2024-05-01 16:23:23,104:INFO:Defining folds +2024-05-01 16:23:23,105:INFO:Declaring metric variables +2024-05-01 16:23:23,140:INFO:Importing untrained model +2024-05-01 16:23:23,170:INFO:K Neighbors Regressor Imported successfully +2024-05-01 16:23:23,245:INFO:Starting cross validation +2024-05-01 16:23:23,257:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:23:26,445:INFO:Calculating mean and std +2024-05-01 16:23:26,452:INFO:Creating metrics dataframe +2024-05-01 16:23:26,477:INFO:Uploading results into container +2024-05-01 16:23:26,485:INFO:Uploading model into container now +2024-05-01 16:23:26,488:INFO:_master_model_container: 11 +2024-05-01 16:23:26,489:INFO:_display_container: 2 +2024-05-01 16:23:26,491:INFO:KNeighborsRegressor(n_jobs=-1) +2024-05-01 16:23:26,492:INFO:create_model() successfully completed...................................... +2024-05-01 16:23:26,800:INFO:SubProcess create_model() end ================================== +2024-05-01 16:23:26,802:INFO:Creating metrics dataframe +2024-05-01 16:23:26,906:INFO:Initializing Decision Tree Regressor +2024-05-01 16:23:26,907:INFO:Total runtime is 1.4524419506390889 minutes +2024-05-01 16:23:26,946:INFO:SubProcess create_model() called ================================== +2024-05-01 16:23:26,948:INFO:Initializing create_model() +2024-05-01 16:23:26,948:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:23:26,949:INFO:Checking exceptions +2024-05-01 16:23:26,950:INFO:Importing libraries +2024-05-01 16:23:26,950:INFO:Copying training dataset +2024-05-01 16:23:27,000:INFO:Defining folds +2024-05-01 16:23:27,001:INFO:Declaring metric variables +2024-05-01 16:23:27,027:INFO:Importing untrained model +2024-05-01 16:23:27,066:INFO:Decision Tree Regressor Imported successfully +2024-05-01 16:23:27,116:INFO:Starting cross validation +2024-05-01 16:23:27,142:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:23:31,108:INFO:Calculating mean and std +2024-05-01 16:23:31,114:INFO:Creating metrics dataframe +2024-05-01 16:23:31,146:INFO:Uploading results into container +2024-05-01 16:23:31,152:INFO:Uploading model into container now +2024-05-01 16:23:31,162:INFO:_master_model_container: 12 +2024-05-01 16:23:31,162:INFO:_display_container: 2 +2024-05-01 16:23:31,164:INFO:DecisionTreeRegressor(random_state=3895) +2024-05-01 16:23:31,165:INFO:create_model() successfully completed...................................... +2024-05-01 16:23:31,447:INFO:SubProcess create_model() end ================================== +2024-05-01 16:23:31,448:INFO:Creating metrics dataframe +2024-05-01 16:23:31,542:INFO:Initializing Random Forest Regressor +2024-05-01 16:23:31,542:INFO:Total runtime is 1.5296867569287618 minutes +2024-05-01 16:23:31,568:INFO:SubProcess create_model() called ================================== +2024-05-01 16:23:31,569:INFO:Initializing create_model() +2024-05-01 16:23:31,570:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:23:31,570:INFO:Checking exceptions +2024-05-01 16:23:31,571:INFO:Importing libraries +2024-05-01 16:23:31,571:INFO:Copying training dataset +2024-05-01 16:23:31,612:INFO:Defining folds +2024-05-01 16:23:31,613:INFO:Declaring metric variables +2024-05-01 16:23:31,652:INFO:Importing untrained model +2024-05-01 16:23:31,695:INFO:Random Forest Regressor Imported successfully +2024-05-01 16:23:31,749:INFO:Starting cross validation +2024-05-01 16:23:31,759:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:24:51,468:INFO:Calculating mean and std +2024-05-01 16:24:51,477:INFO:Creating metrics dataframe +2024-05-01 16:24:51,569:INFO:Uploading results into container +2024-05-01 16:24:51,574:INFO:Uploading model into container now +2024-05-01 16:24:51,577:INFO:_master_model_container: 13 +2024-05-01 16:24:51,578:INFO:_display_container: 2 +2024-05-01 16:24:51,581:INFO:RandomForestRegressor(n_jobs=-1, random_state=3895) +2024-05-01 16:24:51,582:INFO:create_model() successfully completed...................................... +2024-05-01 16:24:52,099:INFO:SubProcess create_model() end ================================== +2024-05-01 16:24:52,099:INFO:Creating metrics dataframe +2024-05-01 16:24:52,238:INFO:Initializing Extra Trees Regressor +2024-05-01 16:24:52,239:INFO:Total runtime is 2.874639924367269 minutes +2024-05-01 16:24:52,280:INFO:SubProcess create_model() called ================================== +2024-05-01 16:24:52,282:INFO:Initializing create_model() +2024-05-01 16:24:52,283:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:24:52,284:INFO:Checking exceptions +2024-05-01 16:24:52,285:INFO:Importing libraries +2024-05-01 16:24:52,285:INFO:Copying training dataset +2024-05-01 16:24:52,359:INFO:Defining folds +2024-05-01 16:24:52,360:INFO:Declaring metric variables +2024-05-01 16:24:52,452:INFO:Importing untrained model +2024-05-01 16:24:52,497:INFO:Extra Trees Regressor Imported successfully +2024-05-01 16:24:52,639:INFO:Starting cross validation +2024-05-01 16:24:52,656:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:25:32,385:INFO:Calculating mean and std +2024-05-01 16:25:32,396:INFO:Creating metrics dataframe +2024-05-01 16:25:32,475:INFO:Uploading results into container +2024-05-01 16:25:32,481:INFO:Uploading model into container now +2024-05-01 16:25:32,485:INFO:_master_model_container: 14 +2024-05-01 16:25:32,485:INFO:_display_container: 2 +2024-05-01 16:25:32,491:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=3895) +2024-05-01 16:25:32,492:INFO:create_model() successfully completed...................................... +2024-05-01 16:25:32,898:INFO:SubProcess create_model() end ================================== +2024-05-01 16:25:32,902:INFO:Creating metrics dataframe +2024-05-01 16:25:33,053:INFO:Initializing AdaBoost Regressor +2024-05-01 16:25:33,054:INFO:Total runtime is 3.554890859127045 minutes +2024-05-01 16:25:33,156:INFO:SubProcess create_model() called ================================== +2024-05-01 16:25:33,170:INFO:Initializing create_model() +2024-05-01 16:25:33,175:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:25:33,176:INFO:Checking exceptions +2024-05-01 16:25:33,177:INFO:Importing libraries +2024-05-01 16:25:33,178:INFO:Copying training dataset +2024-05-01 16:25:33,249:INFO:Defining folds +2024-05-01 16:25:33,250:INFO:Declaring metric variables +2024-05-01 16:25:33,334:INFO:Importing untrained model +2024-05-01 16:25:33,373:INFO:AdaBoost Regressor Imported successfully +2024-05-01 16:25:33,500:INFO:Starting cross validation +2024-05-01 16:25:33,511:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:25:47,132:INFO:Calculating mean and std +2024-05-01 16:25:47,143:INFO:Creating metrics dataframe +2024-05-01 16:25:47,227:INFO:Uploading results into container +2024-05-01 16:25:47,239:INFO:Uploading model into container now +2024-05-01 16:25:47,244:INFO:_master_model_container: 15 +2024-05-01 16:25:47,244:INFO:_display_container: 2 +2024-05-01 16:25:47,246:INFO:AdaBoostRegressor(random_state=3895) +2024-05-01 16:25:47,247:INFO:create_model() successfully completed...................................... +2024-05-01 16:25:47,661:INFO:SubProcess create_model() end ================================== +2024-05-01 16:25:47,662:INFO:Creating metrics dataframe +2024-05-01 16:25:47,836:INFO:Initializing Gradient Boosting Regressor +2024-05-01 16:25:47,838:INFO:Total runtime is 3.801261675357819 minutes +2024-05-01 16:25:47,880:INFO:SubProcess create_model() called ================================== +2024-05-01 16:25:47,882:INFO:Initializing create_model() +2024-05-01 16:25:47,884:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:25:47,884:INFO:Checking exceptions +2024-05-01 16:25:47,885:INFO:Importing libraries +2024-05-01 16:25:47,886:INFO:Copying training dataset +2024-05-01 16:25:47,950:INFO:Defining folds +2024-05-01 16:25:47,951:INFO:Declaring metric variables +2024-05-01 16:25:48,026:INFO:Importing untrained model +2024-05-01 16:25:48,077:INFO:Gradient Boosting Regressor Imported successfully +2024-05-01 16:25:48,209:INFO:Starting cross validation +2024-05-01 16:25:48,225:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:26:10,216:INFO:Calculating mean and std +2024-05-01 16:26:10,228:INFO:Creating metrics dataframe +2024-05-01 16:26:10,309:INFO:Uploading results into container +2024-05-01 16:26:10,315:INFO:Uploading model into container now +2024-05-01 16:26:10,322:INFO:_master_model_container: 16 +2024-05-01 16:26:10,323:INFO:_display_container: 2 +2024-05-01 16:26:10,326:INFO:GradientBoostingRegressor(random_state=3895) +2024-05-01 16:26:10,327:INFO:create_model() successfully completed...................................... +2024-05-01 16:26:10,743:INFO:SubProcess create_model() end ================================== +2024-05-01 16:26:10,744:INFO:Creating metrics dataframe +2024-05-01 16:26:10,878:INFO:Initializing Extreme Gradient Boosting +2024-05-01 16:26:10,878:INFO:Total runtime is 4.1852876385053 minutes +2024-05-01 16:26:10,903:INFO:SubProcess create_model() called ================================== +2024-05-01 16:26:10,905:INFO:Initializing create_model() +2024-05-01 16:26:10,905:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:26:10,906:INFO:Checking exceptions +2024-05-01 16:26:10,906:INFO:Importing libraries +2024-05-01 16:26:10,907:INFO:Copying training dataset +2024-05-01 16:26:10,977:INFO:Defining folds +2024-05-01 16:26:10,977:INFO:Declaring metric variables +2024-05-01 16:26:11,064:INFO:Importing untrained model +2024-05-01 16:26:11,125:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 16:26:11,287:INFO:Starting cross validation +2024-05-01 16:26:11,302:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:26:32,967:INFO:Calculating mean and std +2024-05-01 16:26:32,977:INFO:Creating metrics dataframe +2024-05-01 16:26:33,059:INFO:Uploading results into container +2024-05-01 16:26:33,062:INFO:Uploading model into container now +2024-05-01 16:26:33,064:INFO:_master_model_container: 17 +2024-05-01 16:26:33,065:INFO:_display_container: 2 +2024-05-01 16:26:33,078: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, predictor=None, random_state=3895, + reg_alpha=None, reg_lambda=None, ...) +2024-05-01 16:26:33,079:INFO:create_model() successfully completed...................................... +2024-05-01 16:26:33,424:INFO:SubProcess create_model() end ================================== +2024-05-01 16:26:33,425:INFO:Creating metrics dataframe +2024-05-01 16:26:33,543:INFO:Initializing Light Gradient Boosting Machine +2024-05-01 16:26:33,544:INFO:Total runtime is 4.563052745660146 minutes +2024-05-01 16:26:33,576:INFO:SubProcess create_model() called ================================== +2024-05-01 16:26:33,579:INFO:Initializing create_model() +2024-05-01 16:26:33,580:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:26:33,581:INFO:Checking exceptions +2024-05-01 16:26:33,582:INFO:Importing libraries +2024-05-01 16:26:33,583:INFO:Copying training dataset +2024-05-01 16:26:33,647:INFO:Defining folds +2024-05-01 16:26:33,648:INFO:Declaring metric variables +2024-05-01 16:26:33,740:INFO:Importing untrained model +2024-05-01 16:26:33,788:INFO:Light Gradient Boosting Machine Imported successfully +2024-05-01 16:26:33,930:INFO:Starting cross validation +2024-05-01 16:26:33,943:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:26:45,637:INFO:Calculating mean and std +2024-05-01 16:26:45,700:INFO:Creating metrics dataframe +2024-05-01 16:26:45,759:INFO:Uploading results into container +2024-05-01 16:26:45,763:INFO:Uploading model into container now +2024-05-01 16:26:45,768:INFO:_master_model_container: 18 +2024-05-01 16:26:45,769:INFO:_display_container: 2 +2024-05-01 16:26:45,772:INFO:LGBMRegressor(n_jobs=-1, random_state=3895) +2024-05-01 16:26:45,773:INFO:create_model() successfully completed...................................... +2024-05-01 16:26:46,253:INFO:SubProcess create_model() end ================================== +2024-05-01 16:26:46,254:INFO:Creating metrics dataframe +2024-05-01 16:26:46,388:INFO:Initializing Dummy Regressor +2024-05-01 16:26:46,390:INFO:Total runtime is 4.777153519789378 minutes +2024-05-01 16:26:46,430:INFO:SubProcess create_model() called ================================== +2024-05-01 16:26:46,433:INFO:Initializing create_model() +2024-05-01 16:26:46,434:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:26:46,434:INFO:Checking exceptions +2024-05-01 16:26:46,435:INFO:Importing libraries +2024-05-01 16:26:46,436:INFO:Copying training dataset +2024-05-01 16:26:46,542:INFO:Defining folds +2024-05-01 16:26:46,543:INFO:Declaring metric variables +2024-05-01 16:26:46,621:INFO:Importing untrained model +2024-05-01 16:26:46,699:INFO:Dummy Regressor Imported successfully +2024-05-01 16:26:46,855:INFO:Starting cross validation +2024-05-01 16:26:46,868:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:26:49,912:INFO:Calculating mean and std +2024-05-01 16:26:49,922:INFO:Creating metrics dataframe +2024-05-01 16:26:49,978:INFO:Uploading results into container +2024-05-01 16:26:49,983:INFO:Uploading model into container now +2024-05-01 16:26:49,985:INFO:_master_model_container: 19 +2024-05-01 16:26:49,985:INFO:_display_container: 2 +2024-05-01 16:26:49,986:INFO:DummyRegressor() +2024-05-01 16:26:49,988:INFO:create_model() successfully completed...................................... +2024-05-01 16:26:50,335:INFO:SubProcess create_model() end ================================== +2024-05-01 16:26:50,336:INFO:Creating metrics dataframe +2024-05-01 16:26:50,570:INFO:Initializing create_model() +2024-05-01 16:26:50,571:INFO:create_model(self=, estimator=RandomForestRegressor(n_jobs=-1, random_state=3895), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:26:50,573:INFO:Checking exceptions +2024-05-01 16:26:50,609:INFO:Importing libraries +2024-05-01 16:26:50,610:INFO:Copying training dataset +2024-05-01 16:26:50,727:INFO:Defining folds +2024-05-01 16:26:50,727:INFO:Declaring metric variables +2024-05-01 16:26:50,729:INFO:Importing untrained model +2024-05-01 16:26:50,729:INFO:Declaring custom model +2024-05-01 16:26:50,735:INFO:Random Forest Regressor Imported successfully +2024-05-01 16:26:50,743:INFO:Cross validation set to False +2024-05-01 16:26:50,744:INFO:Fitting Model +2024-05-01 16:27:00,070:INFO:RandomForestRegressor(n_jobs=-1, random_state=3895) +2024-05-01 16:27:00,071:INFO:create_model() successfully completed...................................... +2024-05-01 16:27:01,106:INFO:_master_model_container: 19 +2024-05-01 16:27:01,107:INFO:_display_container: 2 +2024-05-01 16:27:01,110:INFO:RandomForestRegressor(n_jobs=-1, random_state=3895) +2024-05-01 16:27:01,112:INFO:compare_models() successfully completed...................................... +2024-05-01 16:27:01,555:INFO:Initializing create_model() +2024-05-01 16:27:01,556:INFO:create_model(self=, estimator=et, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:27:01,556:INFO:Checking exceptions +2024-05-01 16:27:01,760:INFO:Importing libraries +2024-05-01 16:27:01,761:INFO:Copying training dataset +2024-05-01 16:27:01,817:INFO:Defining folds +2024-05-01 16:27:01,818:INFO:Declaring metric variables +2024-05-01 16:27:01,925:INFO:Importing untrained model +2024-05-01 16:27:01,960:INFO:Extra Trees Regressor Imported successfully +2024-05-01 16:27:02,240:INFO:Starting cross validation +2024-05-01 16:27:02,366:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:27:45,722:INFO:Calculating mean and std +2024-05-01 16:27:45,734:INFO:Creating metrics dataframe +2024-05-01 16:27:45,844:INFO:Finalizing model +2024-05-01 16:27:50,814:INFO:Uploading results into container +2024-05-01 16:27:50,835:INFO:Uploading model into container now +2024-05-01 16:27:51,015:INFO:_master_model_container: 20 +2024-05-01 16:27:51,016:INFO:_display_container: 3 +2024-05-01 16:27:51,020:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=3895) +2024-05-01 16:27:51,020:INFO:create_model() successfully completed...................................... +2024-05-01 16:27:51,743:INFO:Initializing evaluate_model() +2024-05-01 16:27:51,744:INFO:evaluate_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=3895), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) +2024-05-01 16:27:51,885:INFO:Initializing plot_model() +2024-05-01 16:27:51,885:INFO:plot_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=3895), plot=pipeline, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-05-01 16:27:51,886:INFO:Checking exceptions +2024-05-01 16:27:52,343:INFO:Preloading libraries +2024-05-01 16:27:53,387:INFO:Copying training dataset +2024-05-01 16:27:53,388:INFO:Plot type: pipeline +2024-05-01 16:27:56,271:INFO:Visual Rendered Successfully +2024-05-01 16:27:56,638:INFO:plot_model() successfully completed...................................... +2024-05-01 16:27:56,935:INFO:Initializing tune_model() +2024-05-01 16:27:56,936:INFO:tune_model(self=, estimator=ExtraTreesRegressor(n_jobs=-1, random_state=3895), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-05-01 16:27:56,937:INFO:Checking exceptions +2024-05-01 16:27:57,157:INFO:Copying training dataset +2024-05-01 16:27:57,226:INFO:Checking base model +2024-05-01 16:27:57,227:INFO:Base model : Extra Trees Regressor +2024-05-01 16:27:57,313:INFO:Declaring metric variables +2024-05-01 16:27:57,367:INFO:Defining Hyperparameters +2024-05-01 16:27:57,812:INFO:Tuning with n_jobs=-1 +2024-05-01 16:27:57,813:INFO:Initializing RandomizedSearchCV +2024-05-01 16:39:14,468:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 16:39:14,469:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 16:39:14,470:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 16:39:14,471:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 16:39:15,840:INFO:PyCaret RegressionExperiment +2024-05-01 16:39:15,840:INFO:Logging name: reg-default-name +2024-05-01 16:39:15,842:INFO:ML Usecase: MLUsecase.REGRESSION +2024-05-01 16:39:15,842:INFO:version 3.3.0 +2024-05-01 16:39:15,842:INFO:Initializing setup() +2024-05-01 16:39:15,843:INFO:self.USI: c5c0 +2024-05-01 16:39:15,843:INFO:self._variable_keys: {'log_plots_param', 'n_jobs_param', 'transform_target_param', 'y_test', 'exp_id', 'fold_shuffle_param', 'html_param', 'seed', '_available_plots', 'X', 'USI', '_ml_usecase', 'X_test', 'y', 'y_train', 'fold_groups_param', 'data', 'X_train', 'logging_param', 'gpu_n_jobs_param', 'memory', 'exp_name_log', 'fold_generator', 'pipeline', 'target_param', 'gpu_param', 'idx'} +2024-05-01 16:39:15,843:INFO:Checking environment +2024-05-01 16:39:15,844:INFO:python_version: 3.11.0 +2024-05-01 16:39:15,844:INFO:python_build: ('main', 'Oct 24 2022 18:26:48') +2024-05-01 16:39:15,844:INFO:machine: AMD64 +2024-05-01 16:39:15,845:INFO:platform: Windows-10-10.0.22000-SP0 +2024-05-01 16:39:15,890:INFO:Memory: svmem(total=8467492864, available=2729467904, percent=67.8, used=5738024960, free=2729467904) +2024-05-01 16:39:15,894:INFO:Physical Core: 2 +2024-05-01 16:39:15,895:INFO:Logical Core: 4 +2024-05-01 16:39:15,898:INFO:Checking libraries +2024-05-01 16:39:15,899:INFO:System: +2024-05-01 16:39:15,899:INFO: python: 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] +2024-05-01 16:39:15,900:INFO:executable: c:\Users\Jason\Documents\DataB3\.venv\Scripts\python.exe +2024-05-01 16:39:15,900:INFO: machine: Windows-10-10.0.22000-SP0 +2024-05-01 16:39:15,901:INFO:PyCaret required dependencies: +2024-05-01 16:39:16,102:INFO: pip: 24.0 +2024-05-01 16:39:16,103:INFO: setuptools: 65.5.0 +2024-05-01 16:39:16,103:INFO: pycaret: 3.3.0 +2024-05-01 16:39:16,103:INFO: IPython: 8.23.0 +2024-05-01 16:39:16,104:INFO: ipywidgets: 8.1.2 +2024-05-01 16:39:16,104:INFO: tqdm: 4.66.2 +2024-05-01 16:39:16,104:INFO: numpy: 1.24.4 +2024-05-01 16:39:16,104:INFO: pandas: 1.5.3 +2024-05-01 16:39:16,105:INFO: jinja2: 3.1.3 +2024-05-01 16:39:16,105:INFO: scipy: 1.11.4 +2024-05-01 16:39:16,105:INFO: joblib: 1.3.2 +2024-05-01 16:39:16,106:INFO: sklearn: 1.4.1.post1 +2024-05-01 16:39:16,106:INFO: pyod: 1.1.3 +2024-05-01 16:39:16,106:INFO: imblearn: 0.12.2 +2024-05-01 16:39:16,106:INFO: category_encoders: 2.6.3 +2024-05-01 16:39:16,107:INFO: lightgbm: 4.3.0 +2024-05-01 16:39:16,107:INFO: numba: 0.59.1 +2024-05-01 16:39:16,107:INFO: requests: 2.31.0 +2024-05-01 16:39:16,107:INFO: matplotlib: 3.8.3 +2024-05-01 16:39:16,108:INFO: scikitplot: 0.3.7 +2024-05-01 16:39:16,108:INFO: yellowbrick: 1.5 +2024-05-01 16:39:16,108:INFO: plotly: 5.20.0 +2024-05-01 16:39:16,108:INFO: plotly-resampler: Not installed +2024-05-01 16:39:16,109:INFO: kaleido: 0.2.1 +2024-05-01 16:39:16,109:INFO: schemdraw: 0.15 +2024-05-01 16:39:16,109:INFO: statsmodels: 0.14.1 +2024-05-01 16:39:16,110:INFO: sktime: 0.28.0 +2024-05-01 16:39:16,110:INFO: tbats: 1.1.3 +2024-05-01 16:39:16,110:INFO: pmdarima: 2.0.4 +2024-05-01 16:39:16,110:INFO: psutil: 5.9.8 +2024-05-01 16:39:16,111:INFO: markupsafe: 2.1.5 +2024-05-01 16:39:16,111:INFO: pickle5: Not installed +2024-05-01 16:39:16,111:INFO: cloudpickle: 3.0.0 +2024-05-01 16:39:16,112:INFO: deprecation: 2.1.0 +2024-05-01 16:39:16,112:INFO: xxhash: 3.4.1 +2024-05-01 16:39:16,113:INFO: wurlitzer: Not installed +2024-05-01 16:39:16,113:INFO:PyCaret optional dependencies: +2024-05-01 16:39:16,244:INFO: shap: Not installed +2024-05-01 16:39:16,244:INFO: interpret: Not installed +2024-05-01 16:39:16,245:INFO: umap: Not installed +2024-05-01 16:39:16,245:INFO: ydata_profiling: 4.7.0 +2024-05-01 16:39:16,245:INFO: explainerdashboard: Not installed +2024-05-01 16:39:16,245:INFO: autoviz: Not installed +2024-05-01 16:39:16,246:INFO: fairlearn: Not installed +2024-05-01 16:39:16,246:INFO: deepchecks: Not installed +2024-05-01 16:39:16,246:INFO: xgboost: 1.6.2 +2024-05-01 16:39:16,247:INFO: catboost: Not installed +2024-05-01 16:39:16,247:INFO: kmodes: Not installed +2024-05-01 16:39:16,247:INFO: mlxtend: Not installed +2024-05-01 16:39:16,247:INFO: statsforecast: Not installed +2024-05-01 16:39:16,248:INFO: tune_sklearn: Not installed +2024-05-01 16:39:16,248:INFO: ray: Not installed +2024-05-01 16:39:16,248:INFO: hyperopt: Not installed +2024-05-01 16:39:16,248:INFO: optuna: 3.6.1 +2024-05-01 16:39:16,249:INFO: skopt: Not installed +2024-05-01 16:39:16,250:INFO: mlflow: Not installed +2024-05-01 16:39:16,250:INFO: gradio: Not installed +2024-05-01 16:39:16,250:INFO: fastapi: Not installed +2024-05-01 16:39:16,251:INFO: uvicorn: Not installed +2024-05-01 16:39:16,251:INFO: m2cgen: Not installed +2024-05-01 16:39:16,252:INFO: evidently: Not installed +2024-05-01 16:39:16,252:INFO: fugue: Not installed +2024-05-01 16:39:16,253:INFO: streamlit: 1.33.0 +2024-05-01 16:39:16,253:INFO: prophet: 1.1.5 +2024-05-01 16:39:16,254:INFO:None +2024-05-01 16:39:16,255:INFO:Set up data. +2024-05-01 16:39:16,311:INFO:Set up folding strategy. +2024-05-01 16:39:16,312:INFO:Set up train/test split. +2024-05-01 16:39:16,343:INFO:Set up index. +2024-05-01 16:39:16,345:INFO:Assigning column types. +2024-05-01 16:39:16,367:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-05-01 16:39:16,368:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 16:39:16,424:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 16:39:16,481:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 16:39:17,299:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:39:17,890:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:39:17,895:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:39:18,587:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:39:18,592:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 16:39:18,657:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 16:39:18,716:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 16:39:19,405:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:39:19,966:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:39:19,969:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:39:19,997:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:39:20,000:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-05-01 16:39:20,065:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 16:39:20,205:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 16:39:21,387:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:39:22,175:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:39:22,182:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:39:22,227:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:39:22,291:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 16:39:22,349:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 16:39:23,392:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:39:24,447:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:39:24,452:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:39:24,504:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:39:24,510:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-05-01 16:39:24,702:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 16:39:25,991:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:39:27,036:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:39:27,039:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:39:27,095:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:39:27,275:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 16:39:28,468:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:39:29,482:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:39:29,487:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:39:29,540:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:39:29,544:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-05-01 16:39:30,817:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:39:31,607:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:39:31,613:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:39:31,645:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:39:32,793:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:39:33,568:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 16:39:33,572:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:39:33,600:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:39:33,603:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-05-01 16:39:34,713:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:39:35,706:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:39:35,810:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:39:36,938:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 16:39:37,721:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:39:37,768:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:39:37,771:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-05-01 16:39:39,685:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:39:39,725:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:39:41,680:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:39:41,723:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:39:41,738:INFO:Preparing preprocessing pipeline... +2024-05-01 16:39:41,739:INFO:Set up simple imputation. +2024-05-01 16:39:41,758:INFO:Set up encoding of categorical features. +2024-05-01 16:39:41,759:INFO:Set up feature normalization. +2024-05-01 16:39:42,349:INFO:Finished creating preprocessing pipeline. +2024-05-01 16:39:42,417:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\Jason\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=['Date'], + transformer=SimpleImputer(strategy='most_frequent'))), + ('rest_encoding', + TransformerWrapper(include=['Date'], + transformer=TargetEncoder(cols=['Date'], + handle_missing='return_nan'))), + ('normalize', TransformerWrapper(transformer=MinMaxScaler()))]) +2024-05-01 16:39:42,418:INFO:Creating final display dataframe. +2024-05-01 16:39:44,304:INFO:Setup _display_container: Description Value +0 Session id 1679 +1 Target Daily_Sales +2 Target type Regression +3 Original data shape (6435, 8) +4 Transformed data shape (6435, 8) +5 Transformed train set shape (4504, 8) +6 Transformed test set shape (1931, 8) +7 Numeric features 6 +8 Categorical features 1 +9 Preprocess True +10 Imputation type simple +11 Numeric imputation mean +12 Categorical imputation mode +13 Maximum one-hot encoding 25 +14 Encoding method None +15 Normalize True +16 Normalize method minmax +17 Fold Generator KFold +18 Fold Number 10 +19 CPU Jobs -1 +20 Use GPU False +21 Log Experiment False +22 Experiment Name reg-default-name +23 USI c5c0 +2024-05-01 16:39:46,058:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:39:46,113:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:39:48,037:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 16:39:48,085:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 16:39:48,095:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour. + warnings.warn( + +2024-05-01 16:39:48,096:INFO:setup() successfully completed in 32.43s............... +2024-05-01 16:39:48,379:INFO:Initializing compare_models() +2024-05-01 16:39:48,379:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-05-01 16:39:48,380:INFO:Checking exceptions +2024-05-01 16:39:48,396:INFO:Preparing display monitor +2024-05-01 16:39:48,864:INFO:Initializing Linear Regression +2024-05-01 16:39:48,865:INFO:Total runtime is 1.6609827677408855e-05 minutes +2024-05-01 16:39:48,911:INFO:SubProcess create_model() called ================================== +2024-05-01 16:39:48,915:INFO:Initializing create_model() +2024-05-01 16:39:48,916:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:39:48,917:INFO:Checking exceptions +2024-05-01 16:39:48,918:INFO:Importing libraries +2024-05-01 16:39:48,919:INFO:Copying training dataset +2024-05-01 16:39:48,964:INFO:Defining folds +2024-05-01 16:39:48,965:INFO:Declaring metric variables +2024-05-01 16:39:49,012:INFO:Importing untrained model +2024-05-01 16:39:49,049:INFO:Linear Regression Imported successfully +2024-05-01 16:39:49,138:INFO:Starting cross validation +2024-05-01 16:39:49,209:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:40:31,698:INFO:Calculating mean and std +2024-05-01 16:40:31,712:INFO:Creating metrics dataframe +2024-05-01 16:40:31,759:INFO:Uploading results into container +2024-05-01 16:40:31,764:INFO:Uploading model into container now +2024-05-01 16:40:31,768:INFO:_master_model_container: 1 +2024-05-01 16:40:31,769:INFO:_display_container: 2 +2024-05-01 16:40:31,771:INFO:LinearRegression(n_jobs=-1) +2024-05-01 16:40:31,771:INFO:create_model() successfully completed...................................... +2024-05-01 16:40:32,186:INFO:SubProcess create_model() end ================================== +2024-05-01 16:40:32,187:INFO:Creating metrics dataframe +2024-05-01 16:40:32,305:INFO:Initializing Lasso Regression +2024-05-01 16:40:32,306:INFO:Total runtime is 0.7240428924560547 minutes +2024-05-01 16:40:32,345:INFO:SubProcess create_model() called ================================== +2024-05-01 16:40:32,348:INFO:Initializing create_model() +2024-05-01 16:40:32,348:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:40:32,349:INFO:Checking exceptions +2024-05-01 16:40:32,350:INFO:Importing libraries +2024-05-01 16:40:32,350:INFO:Copying training dataset +2024-05-01 16:40:32,459:INFO:Defining folds +2024-05-01 16:40:32,460:INFO:Declaring metric variables +2024-05-01 16:40:32,531:INFO:Importing untrained model +2024-05-01 16:40:32,604:INFO:Lasso Regression Imported successfully +2024-05-01 16:40:32,794:INFO:Starting cross validation +2024-05-01 16:40:32,851:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:40:35,875:INFO:Calculating mean and std +2024-05-01 16:40:35,887:INFO:Creating metrics dataframe +2024-05-01 16:40:35,930:INFO:Uploading results into container +2024-05-01 16:40:35,934:INFO:Uploading model into container now +2024-05-01 16:40:35,938:INFO:_master_model_container: 2 +2024-05-01 16:40:35,939:INFO:_display_container: 2 +2024-05-01 16:40:35,941:INFO:Lasso(random_state=1679) +2024-05-01 16:40:35,942:INFO:create_model() successfully completed...................................... +2024-05-01 16:40:36,302:INFO:SubProcess create_model() end ================================== +2024-05-01 16:40:36,302:INFO:Creating metrics dataframe +2024-05-01 16:40:36,390:INFO:Initializing Ridge Regression +2024-05-01 16:40:36,391:INFO:Total runtime is 0.7921294887860616 minutes +2024-05-01 16:40:36,424:INFO:SubProcess create_model() called ================================== +2024-05-01 16:40:36,426:INFO:Initializing create_model() +2024-05-01 16:40:36,427:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:40:36,428:INFO:Checking exceptions +2024-05-01 16:40:36,430:INFO:Importing libraries +2024-05-01 16:40:36,430:INFO:Copying training dataset +2024-05-01 16:40:36,485:INFO:Defining folds +2024-05-01 16:40:36,486:INFO:Declaring metric variables +2024-05-01 16:40:36,528:INFO:Importing untrained model +2024-05-01 16:40:36,570:INFO:Ridge Regression Imported successfully +2024-05-01 16:40:36,634:INFO:Starting cross validation +2024-05-01 16:40:36,644:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:40:39,542:INFO:Calculating mean and std +2024-05-01 16:40:39,553:INFO:Creating metrics dataframe +2024-05-01 16:40:39,607:INFO:Uploading results into container +2024-05-01 16:40:39,612:INFO:Uploading model into container now +2024-05-01 16:40:39,618:INFO:_master_model_container: 3 +2024-05-01 16:40:39,619:INFO:_display_container: 2 +2024-05-01 16:40:39,622:INFO:Ridge(random_state=1679) +2024-05-01 16:40:39,622:INFO:create_model() successfully completed...................................... +2024-05-01 16:40:40,064:INFO:SubProcess create_model() end ================================== +2024-05-01 16:40:40,065:INFO:Creating metrics dataframe +2024-05-01 16:40:40,170:INFO:Initializing Elastic Net +2024-05-01 16:40:40,171:INFO:Total runtime is 0.8551042318344116 minutes +2024-05-01 16:40:40,211:INFO:SubProcess create_model() called ================================== +2024-05-01 16:40:40,213:INFO:Initializing create_model() +2024-05-01 16:40:40,214:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:40:40,214:INFO:Checking exceptions +2024-05-01 16:40:40,215:INFO:Importing libraries +2024-05-01 16:40:40,216:INFO:Copying training dataset +2024-05-01 16:40:40,281:INFO:Defining folds +2024-05-01 16:40:40,283:INFO:Declaring metric variables +2024-05-01 16:40:40,327:INFO:Importing untrained model +2024-05-01 16:40:40,368:INFO:Elastic Net Imported successfully +2024-05-01 16:40:40,473:INFO:Starting cross validation +2024-05-01 16:40:40,489:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:40:44,472:INFO:Calculating mean and std +2024-05-01 16:40:44,531:INFO:Creating metrics dataframe +2024-05-01 16:40:44,629:INFO:Uploading results into container +2024-05-01 16:40:44,635:INFO:Uploading model into container now +2024-05-01 16:40:44,638:INFO:_master_model_container: 4 +2024-05-01 16:40:44,639:INFO:_display_container: 2 +2024-05-01 16:40:44,653:INFO:ElasticNet(random_state=1679) +2024-05-01 16:40:44,653:INFO:create_model() successfully completed...................................... +2024-05-01 16:40:45,303:INFO:SubProcess create_model() end ================================== +2024-05-01 16:40:45,304:INFO:Creating metrics dataframe +2024-05-01 16:40:45,478:INFO:Initializing Least Angle Regression +2024-05-01 16:40:45,479:INFO:Total runtime is 0.9435886303583781 minutes +2024-05-01 16:40:45,515:INFO:SubProcess create_model() called ================================== +2024-05-01 16:40:45,524:INFO:Initializing create_model() +2024-05-01 16:40:45,572:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:40:45,572:INFO:Checking exceptions +2024-05-01 16:40:45,573:INFO:Importing libraries +2024-05-01 16:40:45,574:INFO:Copying training dataset +2024-05-01 16:40:45,637:INFO:Defining folds +2024-05-01 16:40:45,644:INFO:Declaring metric variables +2024-05-01 16:40:45,719:INFO:Importing untrained model +2024-05-01 16:40:45,777:INFO:Least Angle Regression Imported successfully +2024-05-01 16:40:45,923:INFO:Starting cross validation +2024-05-01 16:40:45,936:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:40:54,818:INFO:Calculating mean and std +2024-05-01 16:40:54,843:INFO:Creating metrics dataframe +2024-05-01 16:40:54,909:INFO:Uploading results into container +2024-05-01 16:40:54,916:INFO:Uploading model into container now +2024-05-01 16:40:54,920:INFO:_master_model_container: 5 +2024-05-01 16:40:54,921:INFO:_display_container: 2 +2024-05-01 16:40:54,924:INFO:Lars(random_state=1679) +2024-05-01 16:40:54,924:INFO:create_model() successfully completed...................................... +2024-05-01 16:40:55,466:INFO:SubProcess create_model() end ================================== +2024-05-01 16:40:55,503:INFO:Creating metrics dataframe +2024-05-01 16:40:55,677:INFO:Initializing Lasso Least Angle Regression +2024-05-01 16:40:55,678:INFO:Total runtime is 1.1135637323061625 minutes +2024-05-01 16:40:55,738:INFO:SubProcess create_model() called ================================== +2024-05-01 16:40:55,743:INFO:Initializing create_model() +2024-05-01 16:40:55,744:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:40:55,745:INFO:Checking exceptions +2024-05-01 16:40:55,745:INFO:Importing libraries +2024-05-01 16:40:55,746:INFO:Copying training dataset +2024-05-01 16:40:55,829:INFO:Defining folds +2024-05-01 16:40:55,830:INFO:Declaring metric variables +2024-05-01 16:40:55,871:INFO:Importing untrained model +2024-05-01 16:40:55,945:INFO:Lasso Least Angle Regression Imported successfully +2024-05-01 16:40:56,075:INFO:Starting cross validation +2024-05-01 16:40:56,104:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:40:59,848:INFO:Calculating mean and std +2024-05-01 16:40:59,867:INFO:Creating metrics dataframe +2024-05-01 16:40:59,928:INFO:Uploading results into container +2024-05-01 16:40:59,934:INFO:Uploading model into container now +2024-05-01 16:40:59,937:INFO:_master_model_container: 6 +2024-05-01 16:40:59,939:INFO:_display_container: 2 +2024-05-01 16:40:59,943:INFO:LassoLars(random_state=1679) +2024-05-01 16:40:59,943:INFO:create_model() successfully completed...................................... +2024-05-01 16:41:00,573:INFO:SubProcess create_model() end ================================== +2024-05-01 16:41:00,574:INFO:Creating metrics dataframe +2024-05-01 16:41:00,791:INFO:Initializing Orthogonal Matching Pursuit +2024-05-01 16:41:00,792:INFO:Total runtime is 1.1988010644912719 minutes +2024-05-01 16:41:00,830:INFO:SubProcess create_model() called ================================== +2024-05-01 16:41:00,832:INFO:Initializing create_model() +2024-05-01 16:41:00,850:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:41:00,851:INFO:Checking exceptions +2024-05-01 16:41:00,852:INFO:Importing libraries +2024-05-01 16:41:00,852:INFO:Copying training dataset +2024-05-01 16:41:00,958:INFO:Defining folds +2024-05-01 16:41:00,962:INFO:Declaring metric variables +2024-05-01 16:41:01,041:INFO:Importing untrained model +2024-05-01 16:41:01,097:INFO:Orthogonal Matching Pursuit Imported successfully +2024-05-01 16:41:01,252:INFO:Starting cross validation +2024-05-01 16:41:01,268:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:41:05,435:INFO:Calculating mean and std +2024-05-01 16:41:05,447:INFO:Creating metrics dataframe +2024-05-01 16:41:05,523:INFO:Uploading results into container +2024-05-01 16:41:05,530:INFO:Uploading model into container now +2024-05-01 16:41:05,535:INFO:_master_model_container: 7 +2024-05-01 16:41:05,535:INFO:_display_container: 2 +2024-05-01 16:41:05,544:INFO:OrthogonalMatchingPursuit() +2024-05-01 16:41:05,545:INFO:create_model() successfully completed...................................... +2024-05-01 16:41:06,073:INFO:SubProcess create_model() end ================================== +2024-05-01 16:41:06,075:INFO:Creating metrics dataframe +2024-05-01 16:41:06,265:INFO:Initializing Bayesian Ridge +2024-05-01 16:41:06,266:INFO:Total runtime is 1.2900328795115152 minutes +2024-05-01 16:41:06,336:INFO:SubProcess create_model() called ================================== +2024-05-01 16:41:06,338:INFO:Initializing create_model() +2024-05-01 16:41:06,339:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:41:06,339:INFO:Checking exceptions +2024-05-01 16:41:06,340:INFO:Importing libraries +2024-05-01 16:41:06,341:INFO:Copying training dataset +2024-05-01 16:41:06,436:INFO:Defining folds +2024-05-01 16:41:06,437:INFO:Declaring metric variables +2024-05-01 16:41:06,478:INFO:Importing untrained model +2024-05-01 16:41:06,532:INFO:Bayesian Ridge Imported successfully +2024-05-01 16:41:06,629:INFO:Starting cross validation +2024-05-01 16:41:06,666:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:41:10,788:INFO:Calculating mean and std +2024-05-01 16:41:10,802:INFO:Creating metrics dataframe +2024-05-01 16:41:10,844:INFO:Uploading results into container +2024-05-01 16:41:10,849:INFO:Uploading model into container now +2024-05-01 16:41:10,852:INFO:_master_model_container: 8 +2024-05-01 16:41:10,853:INFO:_display_container: 2 +2024-05-01 16:41:10,855:INFO:BayesianRidge() +2024-05-01 16:41:10,856:INFO:create_model() successfully completed...................................... +2024-05-01 16:41:11,219:INFO:SubProcess create_model() end ================================== +2024-05-01 16:41:11,220:INFO:Creating metrics dataframe +2024-05-01 16:41:11,337:INFO:Initializing Passive Aggressive Regressor +2024-05-01 16:41:11,338:INFO:Total runtime is 1.374554169178009 minutes +2024-05-01 16:41:11,369:INFO:SubProcess create_model() called ================================== +2024-05-01 16:41:11,372:INFO:Initializing create_model() +2024-05-01 16:41:11,375:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:41:11,377:INFO:Checking exceptions +2024-05-01 16:41:11,377:INFO:Importing libraries +2024-05-01 16:41:11,378:INFO:Copying training dataset +2024-05-01 16:41:11,445:INFO:Defining folds +2024-05-01 16:41:11,446:INFO:Declaring metric variables +2024-05-01 16:41:11,484:INFO:Importing untrained model +2024-05-01 16:41:11,538:INFO:Passive Aggressive Regressor Imported successfully +2024-05-01 16:41:11,604:INFO:Starting cross validation +2024-05-01 16:41:11,615:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:41:16,649:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:41:16,749:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:41:16,788:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:41:16,983:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:41:28,410:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:41:28,448:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:41:28,681:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:41:28,747:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:41:31,891:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:41:31,954:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 16:41:32,191:INFO:Calculating mean and std +2024-05-01 16:41:32,204:INFO:Creating metrics dataframe +2024-05-01 16:41:32,276:INFO:Uploading results into container +2024-05-01 16:41:32,282:INFO:Uploading model into container now +2024-05-01 16:41:32,285:INFO:_master_model_container: 9 +2024-05-01 16:41:32,286:INFO:_display_container: 2 +2024-05-01 16:41:32,294:INFO:PassiveAggressiveRegressor(random_state=1679) +2024-05-01 16:41:32,295:INFO:create_model() successfully completed...................................... +2024-05-01 16:41:32,745:INFO:SubProcess create_model() end ================================== +2024-05-01 16:41:32,747:INFO:Creating metrics dataframe +2024-05-01 16:41:32,888:INFO:Initializing Huber Regressor +2024-05-01 16:41:32,891:INFO:Total runtime is 1.7337865432103474 minutes +2024-05-01 16:41:32,927:INFO:SubProcess create_model() called ================================== +2024-05-01 16:41:32,944:INFO:Initializing create_model() +2024-05-01 16:41:32,944:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:41:32,946:INFO:Checking exceptions +2024-05-01 16:41:32,946:INFO:Importing libraries +2024-05-01 16:41:32,947:INFO:Copying training dataset +2024-05-01 16:41:33,149:INFO:Defining folds +2024-05-01 16:41:33,150:INFO:Declaring metric variables +2024-05-01 16:41:33,307:INFO:Importing untrained model +2024-05-01 16:41:33,427:INFO:Huber Regressor Imported successfully +2024-05-01 16:41:33,624:INFO:Starting cross validation +2024-05-01 16:41:33,635:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:41:37,405:INFO:Calculating mean and std +2024-05-01 16:41:37,417:INFO:Creating metrics dataframe +2024-05-01 16:41:37,479:INFO:Uploading results into container +2024-05-01 16:41:37,488:INFO:Uploading model into container now +2024-05-01 16:41:37,492:INFO:_master_model_container: 10 +2024-05-01 16:41:37,495:INFO:_display_container: 2 +2024-05-01 16:41:37,497:INFO:HuberRegressor() +2024-05-01 16:41:37,498:INFO:create_model() successfully completed...................................... +2024-05-01 16:41:37,873:INFO:SubProcess create_model() end ================================== +2024-05-01 16:41:37,874:INFO:Creating metrics dataframe +2024-05-01 16:41:37,988:INFO:Initializing K Neighbors Regressor +2024-05-01 16:41:37,989:INFO:Total runtime is 1.8187574903170267 minutes +2024-05-01 16:41:38,050:INFO:SubProcess create_model() called ================================== +2024-05-01 16:41:38,052:INFO:Initializing create_model() +2024-05-01 16:41:38,052:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:41:38,053:INFO:Checking exceptions +2024-05-01 16:41:38,053:INFO:Importing libraries +2024-05-01 16:41:38,054:INFO:Copying training dataset +2024-05-01 16:41:38,258:INFO:Defining folds +2024-05-01 16:41:38,259:INFO:Declaring metric variables +2024-05-01 16:41:38,358:INFO:Importing untrained model +2024-05-01 16:41:38,416:INFO:K Neighbors Regressor Imported successfully +2024-05-01 16:41:38,547:INFO:Starting cross validation +2024-05-01 16:41:38,563:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:41:42,474:INFO:Calculating mean and std +2024-05-01 16:41:42,484:INFO:Creating metrics dataframe +2024-05-01 16:41:42,569:INFO:Uploading results into container +2024-05-01 16:41:42,580:INFO:Uploading model into container now +2024-05-01 16:41:42,583:INFO:_master_model_container: 11 +2024-05-01 16:41:42,584:INFO:_display_container: 2 +2024-05-01 16:41:42,588:INFO:KNeighborsRegressor(n_jobs=-1) +2024-05-01 16:41:42,594:INFO:create_model() successfully completed...................................... +2024-05-01 16:41:43,041:INFO:SubProcess create_model() end ================================== +2024-05-01 16:41:43,042:INFO:Creating metrics dataframe +2024-05-01 16:41:43,225:INFO:Initializing Decision Tree Regressor +2024-05-01 16:41:43,226:INFO:Total runtime is 1.9060351490974425 minutes +2024-05-01 16:41:43,265:INFO:SubProcess create_model() called ================================== +2024-05-01 16:41:43,269:INFO:Initializing create_model() +2024-05-01 16:41:43,270:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:41:43,270:INFO:Checking exceptions +2024-05-01 16:41:43,271:INFO:Importing libraries +2024-05-01 16:41:43,272:INFO:Copying training dataset +2024-05-01 16:41:43,351:INFO:Defining folds +2024-05-01 16:41:43,352:INFO:Declaring metric variables +2024-05-01 16:41:43,416:INFO:Importing untrained model +2024-05-01 16:41:43,542:INFO:Decision Tree Regressor Imported successfully +2024-05-01 16:41:43,823:INFO:Starting cross validation +2024-05-01 16:41:43,860:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:41:48,677:INFO:Calculating mean and std +2024-05-01 16:41:48,690:INFO:Creating metrics dataframe +2024-05-01 16:41:48,798:INFO:Uploading results into container +2024-05-01 16:41:48,809:INFO:Uploading model into container now +2024-05-01 16:41:48,819:INFO:_master_model_container: 12 +2024-05-01 16:41:48,820:INFO:_display_container: 2 +2024-05-01 16:41:48,824:INFO:DecisionTreeRegressor(random_state=1679) +2024-05-01 16:41:48,825:INFO:create_model() successfully completed...................................... +2024-05-01 16:41:49,275:INFO:SubProcess create_model() end ================================== +2024-05-01 16:41:49,275:INFO:Creating metrics dataframe +2024-05-01 16:41:49,462:INFO:Initializing Random Forest Regressor +2024-05-01 16:41:49,463:INFO:Total runtime is 2.009988307952881 minutes +2024-05-01 16:41:49,502:INFO:SubProcess create_model() called ================================== +2024-05-01 16:41:49,505:INFO:Initializing create_model() +2024-05-01 16:41:49,507:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:41:49,508:INFO:Checking exceptions +2024-05-01 16:41:49,512:INFO:Importing libraries +2024-05-01 16:41:49,513:INFO:Copying training dataset +2024-05-01 16:41:49,728:INFO:Defining folds +2024-05-01 16:41:49,729:INFO:Declaring metric variables +2024-05-01 16:41:49,770:INFO:Importing untrained model +2024-05-01 16:41:49,882:INFO:Random Forest Regressor Imported successfully +2024-05-01 16:41:50,046:INFO:Starting cross validation +2024-05-01 16:41:50,061:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:43:02,675:INFO:Calculating mean and std +2024-05-01 16:43:02,689:INFO:Creating metrics dataframe +2024-05-01 16:43:02,766:INFO:Uploading results into container +2024-05-01 16:43:02,774:INFO:Uploading model into container now +2024-05-01 16:43:02,781:INFO:_master_model_container: 13 +2024-05-01 16:43:02,782:INFO:_display_container: 2 +2024-05-01 16:43:02,785:INFO:RandomForestRegressor(n_jobs=-1, random_state=1679) +2024-05-01 16:43:02,786:INFO:create_model() successfully completed...................................... +2024-05-01 16:43:03,168:INFO:SubProcess create_model() end ================================== +2024-05-01 16:43:03,169:INFO:Creating metrics dataframe +2024-05-01 16:43:03,300:INFO:Initializing Extra Trees Regressor +2024-05-01 16:43:03,301:INFO:Total runtime is 3.240620827674866 minutes +2024-05-01 16:43:03,346:INFO:SubProcess create_model() called ================================== +2024-05-01 16:43:03,348:INFO:Initializing create_model() +2024-05-01 16:43:03,349:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:43:03,350:INFO:Checking exceptions +2024-05-01 16:43:03,351:INFO:Importing libraries +2024-05-01 16:43:03,352:INFO:Copying training dataset +2024-05-01 16:43:03,445:INFO:Defining folds +2024-05-01 16:43:03,446:INFO:Declaring metric variables +2024-05-01 16:43:03,519:INFO:Importing untrained model +2024-05-01 16:43:03,766:INFO:Extra Trees Regressor Imported successfully +2024-05-01 16:43:03,885:INFO:Starting cross validation +2024-05-01 16:43:03,899:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:43:43,171:INFO:Calculating mean and std +2024-05-01 16:43:43,186:INFO:Creating metrics dataframe +2024-05-01 16:43:43,243:INFO:Uploading results into container +2024-05-01 16:43:43,260:INFO:Uploading model into container now +2024-05-01 16:43:43,264:INFO:_master_model_container: 14 +2024-05-01 16:43:43,267:INFO:_display_container: 2 +2024-05-01 16:43:43,270:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=1679) +2024-05-01 16:43:43,271:INFO:create_model() successfully completed...................................... +2024-05-01 16:43:43,671:INFO:SubProcess create_model() end ================================== +2024-05-01 16:43:43,672:INFO:Creating metrics dataframe +2024-05-01 16:43:43,817:INFO:Initializing AdaBoost Regressor +2024-05-01 16:43:43,818:INFO:Total runtime is 3.915910454591115 minutes +2024-05-01 16:43:43,888:INFO:SubProcess create_model() called ================================== +2024-05-01 16:43:43,890:INFO:Initializing create_model() +2024-05-01 16:43:43,892:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:43:43,905:INFO:Checking exceptions +2024-05-01 16:43:43,905:INFO:Importing libraries +2024-05-01 16:43:43,908:INFO:Copying training dataset +2024-05-01 16:43:44,104:INFO:Defining folds +2024-05-01 16:43:44,105:INFO:Declaring metric variables +2024-05-01 16:43:44,135:INFO:Importing untrained model +2024-05-01 16:43:44,202:INFO:AdaBoost Regressor Imported successfully +2024-05-01 16:43:44,396:INFO:Starting cross validation +2024-05-01 16:43:44,410:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:43:56,943:INFO:Calculating mean and std +2024-05-01 16:43:56,954:INFO:Creating metrics dataframe +2024-05-01 16:43:57,029:INFO:Uploading results into container +2024-05-01 16:43:57,040:INFO:Uploading model into container now +2024-05-01 16:43:57,052:INFO:_master_model_container: 15 +2024-05-01 16:43:57,053:INFO:_display_container: 2 +2024-05-01 16:43:57,054:INFO:AdaBoostRegressor(random_state=1679) +2024-05-01 16:43:57,055:INFO:create_model() successfully completed...................................... +2024-05-01 16:43:57,427:INFO:SubProcess create_model() end ================================== +2024-05-01 16:43:57,428:INFO:Creating metrics dataframe +2024-05-01 16:43:57,577:INFO:Initializing Gradient Boosting Regressor +2024-05-01 16:43:57,578:INFO:Total runtime is 4.145213683446248 minutes +2024-05-01 16:43:57,612:INFO:SubProcess create_model() called ================================== +2024-05-01 16:43:57,614:INFO:Initializing create_model() +2024-05-01 16:43:57,615:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:43:57,615:INFO:Checking exceptions +2024-05-01 16:43:57,616:INFO:Importing libraries +2024-05-01 16:43:57,617:INFO:Copying training dataset +2024-05-01 16:43:57,706:INFO:Defining folds +2024-05-01 16:43:57,707:INFO:Declaring metric variables +2024-05-01 16:43:57,818:INFO:Importing untrained model +2024-05-01 16:43:58,063:INFO:Gradient Boosting Regressor Imported successfully +2024-05-01 16:43:58,197:INFO:Starting cross validation +2024-05-01 16:43:58,210:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:44:18,802:INFO:Calculating mean and std +2024-05-01 16:44:18,815:INFO:Creating metrics dataframe +2024-05-01 16:44:18,892:INFO:Uploading results into container +2024-05-01 16:44:18,896:INFO:Uploading model into container now +2024-05-01 16:44:18,899:INFO:_master_model_container: 16 +2024-05-01 16:44:18,900:INFO:_display_container: 2 +2024-05-01 16:44:18,905:INFO:GradientBoostingRegressor(random_state=1679) +2024-05-01 16:44:18,906:INFO:create_model() successfully completed...................................... +2024-05-01 16:44:19,282:INFO:SubProcess create_model() end ================================== +2024-05-01 16:44:19,283:INFO:Creating metrics dataframe +2024-05-01 16:44:19,424:INFO:Initializing Extreme Gradient Boosting +2024-05-01 16:44:19,425:INFO:Total runtime is 4.509358088175455 minutes +2024-05-01 16:44:19,463:INFO:SubProcess create_model() called ================================== +2024-05-01 16:44:19,466:INFO:Initializing create_model() +2024-05-01 16:44:19,471:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:44:19,472:INFO:Checking exceptions +2024-05-01 16:44:19,472:INFO:Importing libraries +2024-05-01 16:44:19,473:INFO:Copying training dataset +2024-05-01 16:44:19,548:INFO:Defining folds +2024-05-01 16:44:19,549:INFO:Declaring metric variables +2024-05-01 16:44:19,591:INFO:Importing untrained model +2024-05-01 16:44:19,766:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 16:44:20,069:INFO:Starting cross validation +2024-05-01 16:44:20,088:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:44:44,183:INFO:Calculating mean and std +2024-05-01 16:44:44,207:INFO:Creating metrics dataframe +2024-05-01 16:44:44,280:INFO:Uploading results into container +2024-05-01 16:44:44,285:INFO:Uploading model into container now +2024-05-01 16:44:44,291:INFO:_master_model_container: 17 +2024-05-01 16:44:44,292:INFO:_display_container: 2 +2024-05-01 16:44:44,304: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, predictor=None, random_state=1679, + reg_alpha=None, reg_lambda=None, ...) +2024-05-01 16:44:44,305:INFO:create_model() successfully completed...................................... +2024-05-01 16:44:44,686:INFO:SubProcess create_model() end ================================== +2024-05-01 16:44:44,687:INFO:Creating metrics dataframe +2024-05-01 16:44:44,847:INFO:Initializing Light Gradient Boosting Machine +2024-05-01 16:44:44,848:INFO:Total runtime is 4.933047036329905 minutes +2024-05-01 16:44:44,882:INFO:SubProcess create_model() called ================================== +2024-05-01 16:44:44,886:INFO:Initializing create_model() +2024-05-01 16:44:44,886:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:44:44,887:INFO:Checking exceptions +2024-05-01 16:44:44,888:INFO:Importing libraries +2024-05-01 16:44:44,888:INFO:Copying training dataset +2024-05-01 16:44:44,980:INFO:Defining folds +2024-05-01 16:44:44,981:INFO:Declaring metric variables +2024-05-01 16:44:45,017:INFO:Importing untrained model +2024-05-01 16:44:45,232:INFO:Light Gradient Boosting Machine Imported successfully +2024-05-01 16:44:45,436:INFO:Starting cross validation +2024-05-01 16:44:45,445:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:44:55,322:INFO:Calculating mean and std +2024-05-01 16:44:55,333:INFO:Creating metrics dataframe +2024-05-01 16:44:55,390:INFO:Uploading results into container +2024-05-01 16:44:55,400:INFO:Uploading model into container now +2024-05-01 16:44:55,403:INFO:_master_model_container: 18 +2024-05-01 16:44:55,404:INFO:_display_container: 2 +2024-05-01 16:44:55,407:INFO:LGBMRegressor(n_jobs=-1, random_state=1679) +2024-05-01 16:44:55,408:INFO:create_model() successfully completed...................................... +2024-05-01 16:44:55,811:INFO:SubProcess create_model() end ================================== +2024-05-01 16:44:55,812:INFO:Creating metrics dataframe +2024-05-01 16:44:55,975:INFO:Initializing Dummy Regressor +2024-05-01 16:44:55,975:INFO:Total runtime is 5.118524368604024 minutes +2024-05-01 16:44:56,006:INFO:SubProcess create_model() called ================================== +2024-05-01 16:44:56,010:INFO:Initializing create_model() +2024-05-01 16:44:56,010:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:44:56,012:INFO:Checking exceptions +2024-05-01 16:44:56,014:INFO:Importing libraries +2024-05-01 16:44:56,014:INFO:Copying training dataset +2024-05-01 16:44:56,099:INFO:Defining folds +2024-05-01 16:44:56,101:INFO:Declaring metric variables +2024-05-01 16:44:56,135:INFO:Importing untrained model +2024-05-01 16:44:56,175:INFO:Dummy Regressor Imported successfully +2024-05-01 16:44:56,387:INFO:Starting cross validation +2024-05-01 16:44:56,427:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:45:00,221:INFO:Calculating mean and std +2024-05-01 16:45:00,233:INFO:Creating metrics dataframe +2024-05-01 16:45:00,316:INFO:Uploading results into container +2024-05-01 16:45:00,322:INFO:Uploading model into container now +2024-05-01 16:45:00,327:INFO:_master_model_container: 19 +2024-05-01 16:45:00,327:INFO:_display_container: 2 +2024-05-01 16:45:00,329:INFO:DummyRegressor() +2024-05-01 16:45:00,330:INFO:create_model() successfully completed...................................... +2024-05-01 16:45:00,700:INFO:SubProcess create_model() end ================================== +2024-05-01 16:45:00,702:INFO:Creating metrics dataframe +2024-05-01 16:45:01,139:INFO:Initializing create_model() +2024-05-01 16:45:01,140:INFO:create_model(self=, 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, predictor=None, random_state=1679, + reg_alpha=None, reg_lambda=None, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:45:01,141:INFO:Checking exceptions +2024-05-01 16:45:01,155:INFO:Importing libraries +2024-05-01 16:45:01,156:INFO:Copying training dataset +2024-05-01 16:45:01,386:INFO:Defining folds +2024-05-01 16:45:01,386:INFO:Declaring metric variables +2024-05-01 16:45:01,391:INFO:Importing untrained model +2024-05-01 16:45:01,392:INFO:Declaring custom model +2024-05-01 16:45:01,423:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 16:45:01,440:INFO:Cross validation set to False +2024-05-01 16:45:01,441:INFO:Fitting Model +2024-05-01 16:45:04,565: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, predictor='auto', + random_state=1679, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 16:45:04,565:INFO:create_model() successfully completed...................................... +2024-05-01 16:45:05,606:INFO:_master_model_container: 19 +2024-05-01 16:45:05,608:INFO:_display_container: 2 +2024-05-01 16:45:05,760: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, predictor='auto', + random_state=1679, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 16:45:05,761:INFO:compare_models() successfully completed...................................... +2024-05-01 16:45:06,426:INFO:Initializing create_model() +2024-05-01 16:45:06,426:INFO:create_model(self=, estimator=xgboost, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:45:06,427:INFO:Checking exceptions +2024-05-01 16:45:06,831:INFO:Importing libraries +2024-05-01 16:45:06,833:INFO:Copying training dataset +2024-05-01 16:45:07,189:INFO:Defining folds +2024-05-01 16:45:07,190:INFO:Declaring metric variables +2024-05-01 16:45:07,228:INFO:Importing untrained model +2024-05-01 16:45:07,308:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 16:45:07,491:INFO:Starting cross validation +2024-05-01 16:45:07,501:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:45:25,087:INFO:Calculating mean and std +2024-05-01 16:45:25,099:INFO:Creating metrics dataframe +2024-05-01 16:45:25,155:INFO:Finalizing model +2024-05-01 16:45:30,677:INFO:Uploading results into container +2024-05-01 16:45:30,685:INFO:Uploading model into container now +2024-05-01 16:45:30,857:INFO:_master_model_container: 20 +2024-05-01 16:45:30,858:INFO:_display_container: 3 +2024-05-01 16:45:30,887: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, predictor='auto', + random_state=1679, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 16:45:30,888:INFO:create_model() successfully completed...................................... +2024-05-01 16:45:32,578:INFO:Initializing evaluate_model() +2024-05-01 16:45:32,579:INFO:evaluate_model(self=, estimator=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, predictor='auto', + random_state=1679, reg_alpha=0, reg_lambda=1, ...), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) +2024-05-01 16:45:32,939:INFO:Initializing plot_model() +2024-05-01 16:45:32,939:INFO:plot_model(self=, estimator=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, predictor='auto', + random_state=1679, reg_alpha=0, reg_lambda=1, ...), plot=pipeline, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-05-01 16:45:32,940:INFO:Checking exceptions +2024-05-01 16:45:33,128:INFO:Preloading libraries +2024-05-01 16:45:33,293:INFO:Copying training dataset +2024-05-01 16:45:33,294:INFO:Plot type: pipeline +2024-05-01 16:45:37,316:INFO:Visual Rendered Successfully +2024-05-01 16:45:37,759:INFO:plot_model() successfully completed...................................... +2024-05-01 16:45:38,218:INFO:Initializing tune_model() +2024-05-01 16:45:38,219:INFO:tune_model(self=, estimator=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, predictor='auto', + random_state=1679, reg_alpha=0, reg_lambda=1, ...), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-05-01 16:45:38,220:INFO:Checking exceptions +2024-05-01 16:45:38,724:INFO:Copying training dataset +2024-05-01 16:45:38,758:INFO:Checking base model +2024-05-01 16:45:38,759:INFO:Base model : Extreme Gradient Boosting +2024-05-01 16:45:38,865:INFO:Declaring metric variables +2024-05-01 16:45:38,977:INFO:Defining Hyperparameters +2024-05-01 16:45:41,654:INFO:Tuning with n_jobs=-1 +2024-05-01 16:45:41,655:INFO:Initializing RandomizedSearchCV +2024-05-01 16:50:11,028:INFO:best_params: {'actual_estimator__subsample': 0.9, 'actual_estimator__scale_pos_weight': 24.3, 'actual_estimator__reg_lambda': 0.001, 'actual_estimator__reg_alpha': 0.001, 'actual_estimator__n_estimators': 210, 'actual_estimator__min_child_weight': 3, 'actual_estimator__max_depth': 7, 'actual_estimator__learning_rate': 0.1, 'actual_estimator__colsample_bytree': 0.9} +2024-05-01 16:50:11,036:INFO:Hyperparameter search completed +2024-05-01 16:50:11,037:INFO:SubProcess create_model() called ================================== +2024-05-01 16:50:11,054:INFO:Initializing create_model() +2024-05-01 16:50:11,054:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=1679, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'subsample': 0.9, 'scale_pos_weight': 24.3, 'reg_lambda': 0.001, 'reg_alpha': 0.001, 'n_estimators': 210, 'min_child_weight': 3, 'max_depth': 7, 'learning_rate': 0.1, 'colsample_bytree': 0.9}) +2024-05-01 16:50:11,055:INFO:Checking exceptions +2024-05-01 16:50:11,058:INFO:Importing libraries +2024-05-01 16:50:11,059:INFO:Copying training dataset +2024-05-01 16:50:11,152:INFO:Defining folds +2024-05-01 16:50:11,153:INFO:Declaring metric variables +2024-05-01 16:50:11,214:INFO:Importing untrained model +2024-05-01 16:50:11,215:INFO:Declaring custom model +2024-05-01 16:50:11,307:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 16:50:11,458:INFO:Starting cross validation +2024-05-01 16:50:11,475:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:50:53,136:INFO:Calculating mean and std +2024-05-01 16:50:53,152:INFO:Creating metrics dataframe +2024-05-01 16:50:53,247:INFO:Finalizing model +2024-05-01 16:51:01,893:INFO:Uploading results into container +2024-05-01 16:51:01,908:INFO:Uploading model into container now +2024-05-01 16:51:01,920:INFO:_master_model_container: 21 +2024-05-01 16:51:01,922:INFO:_display_container: 4 +2024-05-01 16:51:02,020:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.9, + 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=0.1, max_bin=256, max_cat_to_onehot=4, + max_delta_step=0, max_depth=7, max_leaves=0, min_child_weight=3, + missing=nan, monotone_constraints='()', n_estimators=210, + n_jobs=-1, num_parallel_tree=1, predictor='auto', + random_state=1679, reg_alpha=0.001, reg_lambda=0.001, ...) +2024-05-01 16:51:02,024:INFO:create_model() successfully completed...................................... +2024-05-01 16:51:02,665:INFO:SubProcess create_model() end ================================== +2024-05-01 16:51:02,666:INFO:choose_better activated +2024-05-01 16:51:02,699:INFO:SubProcess create_model() called ================================== +2024-05-01 16:51:02,751:INFO:Initializing create_model() +2024-05-01 16:51:02,752:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=1679, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 16:51:02,753:INFO:Checking exceptions +2024-05-01 16:51:02,764:INFO:Importing libraries +2024-05-01 16:51:02,765:INFO:Copying training dataset +2024-05-01 16:51:02,842:INFO:Defining folds +2024-05-01 16:51:02,843:INFO:Declaring metric variables +2024-05-01 16:51:02,845:INFO:Importing untrained model +2024-05-01 16:51:02,847:INFO:Declaring custom model +2024-05-01 16:51:02,872:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 16:51:02,875:INFO:Starting cross validation +2024-05-01 16:51:02,889:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 16:51:23,129:INFO:Calculating mean and std +2024-05-01 16:51:23,135:INFO:Creating metrics dataframe +2024-05-01 16:51:23,152:INFO:Finalizing model +2024-05-01 16:51:26,614:INFO:Uploading results into container +2024-05-01 16:51:26,619:INFO:Uploading model into container now +2024-05-01 16:51:26,624:INFO:_master_model_container: 22 +2024-05-01 16:51:26,624:INFO:_display_container: 5 +2024-05-01 16:51:26,655: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, predictor='auto', + random_state=1679, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 16:51:26,656:INFO:create_model() successfully completed...................................... +2024-05-01 16:51:27,073:INFO:SubProcess create_model() end ================================== +2024-05-01 16:51:27,098: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, predictor='auto', + random_state=1679, reg_alpha=0, reg_lambda=1, ...) result for R2 is 0.9769 +2024-05-01 16:51:27,123:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.9, + 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=0.1, max_bin=256, max_cat_to_onehot=4, + max_delta_step=0, max_depth=7, max_leaves=0, min_child_weight=3, + missing=nan, monotone_constraints='()', n_estimators=210, + n_jobs=-1, num_parallel_tree=1, predictor='auto', + random_state=1679, reg_alpha=0.001, reg_lambda=0.001, ...) result for R2 is 0.9796 +2024-05-01 16:51:27,149:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.9, + 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=0.1, max_bin=256, max_cat_to_onehot=4, + max_delta_step=0, max_depth=7, max_leaves=0, min_child_weight=3, + missing=nan, monotone_constraints='()', n_estimators=210, + n_jobs=-1, num_parallel_tree=1, predictor='auto', + random_state=1679, reg_alpha=0.001, reg_lambda=0.001, ...) is best model +2024-05-01 16:51:27,150:INFO:choose_better completed +2024-05-01 16:51:27,314:INFO:_master_model_container: 22 +2024-05-01 16:51:27,315:INFO:_display_container: 4 +2024-05-01 16:51:27,339:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.9, + 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=0.1, max_bin=256, max_cat_to_onehot=4, + max_delta_step=0, max_depth=7, max_leaves=0, min_child_weight=3, + missing=nan, monotone_constraints='()', n_estimators=210, + n_jobs=-1, num_parallel_tree=1, predictor='auto', + random_state=1679, reg_alpha=0.001, reg_lambda=0.001, ...) +2024-05-01 16:51:27,340:INFO:tune_model() successfully completed...................................... +2024-05-01 16:51:30,679:INFO:Initializing predict_model() +2024-05-01 16:51:30,680:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=1679, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000002CC18220540>) +2024-05-01 16:51:30,681:INFO:Checking exceptions +2024-05-01 16:51:30,681:INFO:Preloading libraries +2024-05-01 16:51:30,696:INFO:Set up data. +2024-05-01 16:51:30,728:INFO:Set up index. +2024-05-01 17:17:45,082:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 17:17:45,082:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 17:17:45,082:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 17:17:45,082:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 17:18:25,793:INFO:PyCaret RegressionExperiment +2024-05-01 17:18:25,794:INFO:Logging name: reg-default-name +2024-05-01 17:18:25,794:INFO:ML Usecase: MLUsecase.REGRESSION +2024-05-01 17:18:25,794:INFO:version 3.3.0 +2024-05-01 17:18:25,794:INFO:Initializing setup() +2024-05-01 17:18:25,794:INFO:self.USI: 3baa +2024-05-01 17:18:25,794:INFO:self._variable_keys: {'data', '_ml_usecase', 'exp_name_log', 'y_test', 'X', '_available_plots', 'X_train', 'USI', 'html_param', 'X_test', 'exp_id', 'fold_generator', 'log_plots_param', 'pipeline', 'gpu_n_jobs_param', 'idx', 'n_jobs_param', 'logging_param', 'fold_shuffle_param', 'target_param', 'transform_target_param', 'memory', 'y', 'y_train', 'gpu_param', 'seed', 'fold_groups_param'} +2024-05-01 17:18:25,795:INFO:Checking environment +2024-05-01 17:18:25,795:INFO:python_version: 3.11.0 +2024-05-01 17:18:25,795:INFO:python_build: ('main', 'Oct 24 2022 18:26:48') +2024-05-01 17:18:25,795:INFO:machine: AMD64 +2024-05-01 17:18:25,795:INFO:platform: Windows-10-10.0.22000-SP0 +2024-05-01 17:18:25,801:INFO:Memory: svmem(total=8467492864, available=1674739712, percent=80.2, used=6792753152, free=1674739712) +2024-05-01 17:18:25,801:INFO:Physical Core: 2 +2024-05-01 17:18:25,801:INFO:Logical Core: 4 +2024-05-01 17:18:25,801:INFO:Checking libraries +2024-05-01 17:18:25,801:INFO:System: +2024-05-01 17:18:25,801:INFO: python: 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] +2024-05-01 17:18:25,801:INFO:executable: c:\Users\Jason\Documents\DataB3\.venv\Scripts\python.exe +2024-05-01 17:18:25,801:INFO: machine: Windows-10-10.0.22000-SP0 +2024-05-01 17:18:25,802:INFO:PyCaret required dependencies: +2024-05-01 17:18:25,974:INFO: pip: 24.0 +2024-05-01 17:18:25,974:INFO: setuptools: 65.5.0 +2024-05-01 17:18:25,974:INFO: pycaret: 3.3.0 +2024-05-01 17:18:25,974:INFO: IPython: 8.23.0 +2024-05-01 17:18:25,974:INFO: ipywidgets: 8.1.2 +2024-05-01 17:18:25,974:INFO: tqdm: 4.66.2 +2024-05-01 17:18:25,975:INFO: numpy: 1.24.4 +2024-05-01 17:18:25,975:INFO: pandas: 1.5.3 +2024-05-01 17:18:25,975:INFO: jinja2: 3.1.3 +2024-05-01 17:18:25,975:INFO: scipy: 1.11.4 +2024-05-01 17:18:25,975:INFO: joblib: 1.3.2 +2024-05-01 17:18:25,975:INFO: sklearn: 1.4.1.post1 +2024-05-01 17:18:25,975:INFO: pyod: 1.1.3 +2024-05-01 17:18:25,975:INFO: imblearn: 0.12.2 +2024-05-01 17:18:25,975:INFO: category_encoders: 2.6.3 +2024-05-01 17:18:25,975:INFO: lightgbm: 4.3.0 +2024-05-01 17:18:25,975:INFO: numba: 0.59.1 +2024-05-01 17:18:25,976:INFO: requests: 2.31.0 +2024-05-01 17:18:25,976:INFO: matplotlib: 3.8.3 +2024-05-01 17:18:25,976:INFO: scikitplot: 0.3.7 +2024-05-01 17:18:25,976:INFO: yellowbrick: 1.5 +2024-05-01 17:18:25,976:INFO: plotly: 5.20.0 +2024-05-01 17:18:25,976:INFO: plotly-resampler: Not installed +2024-05-01 17:18:25,976:INFO: kaleido: 0.2.1 +2024-05-01 17:18:25,976:INFO: schemdraw: 0.15 +2024-05-01 17:18:25,976:INFO: statsmodels: 0.14.1 +2024-05-01 17:18:25,977:INFO: sktime: 0.28.0 +2024-05-01 17:18:25,977:INFO: tbats: 1.1.3 +2024-05-01 17:18:25,977:INFO: pmdarima: 2.0.4 +2024-05-01 17:18:25,977:INFO: psutil: 5.9.8 +2024-05-01 17:18:25,977:INFO: markupsafe: 2.1.5 +2024-05-01 17:18:25,977:INFO: pickle5: Not installed +2024-05-01 17:18:25,978:INFO: cloudpickle: 3.0.0 +2024-05-01 17:18:25,978:INFO: deprecation: 2.1.0 +2024-05-01 17:18:25,978:INFO: xxhash: 3.4.1 +2024-05-01 17:18:25,978:INFO: wurlitzer: Not installed +2024-05-01 17:18:25,978:INFO:PyCaret optional dependencies: +2024-05-01 17:18:26,006:INFO: shap: Not installed +2024-05-01 17:18:26,006:INFO: interpret: Not installed +2024-05-01 17:18:26,006:INFO: umap: Not installed +2024-05-01 17:18:26,006:INFO: ydata_profiling: 4.7.0 +2024-05-01 17:18:26,006:INFO: explainerdashboard: Not installed +2024-05-01 17:18:26,006:INFO: autoviz: Not installed +2024-05-01 17:18:26,007:INFO: fairlearn: Not installed +2024-05-01 17:18:26,007:INFO: deepchecks: Not installed +2024-05-01 17:18:26,007:INFO: xgboost: 1.6.2 +2024-05-01 17:18:26,007:INFO: catboost: Not installed +2024-05-01 17:18:26,007:INFO: kmodes: Not installed +2024-05-01 17:18:26,008:INFO: mlxtend: Not installed +2024-05-01 17:18:26,008:INFO: statsforecast: Not installed +2024-05-01 17:18:26,008:INFO: tune_sklearn: Not installed +2024-05-01 17:18:26,009:INFO: ray: Not installed +2024-05-01 17:18:26,009:INFO: hyperopt: Not installed +2024-05-01 17:18:26,009:INFO: optuna: 3.6.1 +2024-05-01 17:18:26,009:INFO: skopt: Not installed +2024-05-01 17:18:26,009:INFO: mlflow: Not installed +2024-05-01 17:18:26,009:INFO: gradio: Not installed +2024-05-01 17:18:26,009:INFO: fastapi: Not installed +2024-05-01 17:18:26,009:INFO: uvicorn: Not installed +2024-05-01 17:18:26,009:INFO: m2cgen: Not installed +2024-05-01 17:18:26,010:INFO: evidently: Not installed +2024-05-01 17:18:26,010:INFO: fugue: Not installed +2024-05-01 17:18:26,010:INFO: streamlit: 1.33.0 +2024-05-01 17:18:26,010:INFO: prophet: 1.1.5 +2024-05-01 17:18:26,010:INFO:None +2024-05-01 17:18:26,010:INFO:Set up data. +2024-05-01 17:18:26,025:INFO:Set up folding strategy. +2024-05-01 17:18:26,026:INFO:Set up train/test split. +2024-05-01 17:18:26,053:INFO:Set up index. +2024-05-01 17:18:26,054:INFO:Assigning column types. +2024-05-01 17:18:26,061:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-05-01 17:18:26,061:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 17:18:26,073:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 17:18:26,086:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 17:18:26,267:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:18:26,423:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:18:26,424:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:18:27,025:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:18:27,027:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 17:18:27,053:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 17:18:27,076:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 17:18:27,335:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:18:27,523:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:18:27,526:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:18:27,543:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:18:27,545:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-05-01 17:18:27,570:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 17:18:27,601:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 17:18:27,897:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:18:28,130:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:18:28,135:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:18:28,152:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:18:28,178:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 17:18:28,197:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 17:18:28,395:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:18:28,624:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:18:28,625:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:18:28,637:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:18:28,638:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-05-01 17:18:28,691:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 17:18:28,906:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:18:29,114:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:18:29,115:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:18:29,123:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:18:29,156:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 17:18:29,360:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:18:29,602:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:18:29,604:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:18:29,615:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:18:29,616:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-05-01 17:18:30,027:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:18:30,229:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:18:30,230:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:18:30,241:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:18:30,472:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:18:30,700:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:18:30,702:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:18:30,711:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:18:30,712:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-05-01 17:18:30,935:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:18:31,080:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:18:31,090:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:18:31,394:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:18:31,537:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:18:31,546:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:18:31,547:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-05-01 17:18:31,946:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:18:31,959:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:18:32,335:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:18:32,344:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:18:32,363:INFO:Preparing preprocessing pipeline... +2024-05-01 17:18:32,363:INFO:Set up simple imputation. +2024-05-01 17:18:32,364:INFO:Set up feature normalization. +2024-05-01 17:18:32,435:INFO:Finished creating preprocessing pipeline. +2024-05-01 17:18:32,444:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\Jason\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWrapper(transformer=MinMaxScaler()))]) +2024-05-01 17:18:32,445:INFO:Creating final display dataframe. +2024-05-01 17:18:32,647:INFO:Setup _display_container: Description Value +0 Session id 3621 +1 Target Daily_Sales +2 Target type Regression +3 Original data shape (6435, 10) +4 Transformed data shape (6435, 10) +5 Transformed train set shape (4504, 10) +6 Transformed test set shape (1931, 10) +7 Numeric features 9 +8 Preprocess True +9 Imputation type simple +10 Numeric imputation mean +11 Categorical imputation mode +12 Normalize True +13 Normalize method minmax +14 Fold Generator KFold +15 Fold Number 10 +16 CPU Jobs -1 +17 Use GPU False +18 Log Experiment False +19 Experiment Name reg-default-name +20 USI 3baa +2024-05-01 17:18:33,140:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:18:33,148:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:18:33,560:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:18:33,574:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:18:33,595:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour. + warnings.warn( + +2024-05-01 17:18:33,596:INFO:setup() successfully completed in 7.84s............... +2024-05-01 17:18:56,785:INFO:Initializing compare_models() +2024-05-01 17:18:56,786:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-05-01 17:18:56,786:INFO:Checking exceptions +2024-05-01 17:18:56,790:INFO:Preparing display monitor +2024-05-01 17:18:56,883:INFO:Initializing Linear Regression +2024-05-01 17:18:56,883:INFO:Total runtime is 0.0 minutes +2024-05-01 17:18:56,896:INFO:SubProcess create_model() called ================================== +2024-05-01 17:18:56,896:INFO:Initializing create_model() +2024-05-01 17:18:56,897:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:18:56,897:INFO:Checking exceptions +2024-05-01 17:18:56,897:INFO:Importing libraries +2024-05-01 17:18:56,897:INFO:Copying training dataset +2024-05-01 17:18:56,911:INFO:Defining folds +2024-05-01 17:18:56,912:INFO:Declaring metric variables +2024-05-01 17:18:56,938:INFO:Importing untrained model +2024-05-01 17:18:56,950:INFO:Linear Regression Imported successfully +2024-05-01 17:18:56,981:INFO:Starting cross validation +2024-05-01 17:18:57,005:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:19:11,497:INFO:Calculating mean and std +2024-05-01 17:19:11,502:INFO:Creating metrics dataframe +2024-05-01 17:19:11,515:INFO:Uploading results into container +2024-05-01 17:19:11,517:INFO:Uploading model into container now +2024-05-01 17:19:11,518:INFO:_master_model_container: 1 +2024-05-01 17:19:11,519:INFO:_display_container: 2 +2024-05-01 17:19:11,520:INFO:LinearRegression(n_jobs=-1) +2024-05-01 17:19:11,521:INFO:create_model() successfully completed...................................... +2024-05-01 17:19:11,855:INFO:SubProcess create_model() end ================================== +2024-05-01 17:19:11,855:INFO:Creating metrics dataframe +2024-05-01 17:19:11,894:INFO:Initializing Lasso Regression +2024-05-01 17:19:11,894:INFO:Total runtime is 0.25017905632654824 minutes +2024-05-01 17:19:11,909:INFO:SubProcess create_model() called ================================== +2024-05-01 17:19:11,910:INFO:Initializing create_model() +2024-05-01 17:19:11,910:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:19:11,911:INFO:Checking exceptions +2024-05-01 17:19:11,911:INFO:Importing libraries +2024-05-01 17:19:11,911:INFO:Copying training dataset +2024-05-01 17:19:11,941:INFO:Defining folds +2024-05-01 17:19:11,941:INFO:Declaring metric variables +2024-05-01 17:19:11,966:INFO:Importing untrained model +2024-05-01 17:19:11,985:INFO:Lasso Regression Imported successfully +2024-05-01 17:19:12,035:INFO:Starting cross validation +2024-05-01 17:19:12,040:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:19:12,434:INFO:Calculating mean and std +2024-05-01 17:19:12,438:INFO:Creating metrics dataframe +2024-05-01 17:19:12,455:INFO:Uploading results into container +2024-05-01 17:19:12,456:INFO:Uploading model into container now +2024-05-01 17:19:12,457:INFO:_master_model_container: 2 +2024-05-01 17:19:12,457:INFO:_display_container: 2 +2024-05-01 17:19:12,458:INFO:Lasso(random_state=3621) +2024-05-01 17:19:12,458:INFO:create_model() successfully completed...................................... +2024-05-01 17:19:12,737:INFO:SubProcess create_model() end ================================== +2024-05-01 17:19:12,737:INFO:Creating metrics dataframe +2024-05-01 17:19:12,767:INFO:Initializing Ridge Regression +2024-05-01 17:19:12,768:INFO:Total runtime is 0.26474883159001666 minutes +2024-05-01 17:19:12,778:INFO:SubProcess create_model() called ================================== +2024-05-01 17:19:12,779:INFO:Initializing create_model() +2024-05-01 17:19:12,779:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:19:12,780:INFO:Checking exceptions +2024-05-01 17:19:12,780:INFO:Importing libraries +2024-05-01 17:19:12,780:INFO:Copying training dataset +2024-05-01 17:19:12,796:INFO:Defining folds +2024-05-01 17:19:12,797:INFO:Declaring metric variables +2024-05-01 17:19:12,813:INFO:Importing untrained model +2024-05-01 17:19:12,828:INFO:Ridge Regression Imported successfully +2024-05-01 17:19:12,875:INFO:Starting cross validation +2024-05-01 17:19:12,884:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:19:13,258:INFO:Calculating mean and std +2024-05-01 17:19:13,262:INFO:Creating metrics dataframe +2024-05-01 17:19:13,279:INFO:Uploading results into container +2024-05-01 17:19:13,281:INFO:Uploading model into container now +2024-05-01 17:19:13,281:INFO:_master_model_container: 3 +2024-05-01 17:19:13,282:INFO:_display_container: 2 +2024-05-01 17:19:13,282:INFO:Ridge(random_state=3621) +2024-05-01 17:19:13,282:INFO:create_model() successfully completed...................................... +2024-05-01 17:19:13,541:INFO:SubProcess create_model() end ================================== +2024-05-01 17:19:13,542:INFO:Creating metrics dataframe +2024-05-01 17:19:13,565:INFO:Initializing Elastic Net +2024-05-01 17:19:13,566:INFO:Total runtime is 0.2780197978019714 minutes +2024-05-01 17:19:13,576:INFO:SubProcess create_model() called ================================== +2024-05-01 17:19:13,577:INFO:Initializing create_model() +2024-05-01 17:19:13,578:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:19:13,578:INFO:Checking exceptions +2024-05-01 17:19:13,578:INFO:Importing libraries +2024-05-01 17:19:13,579:INFO:Copying training dataset +2024-05-01 17:19:13,603:INFO:Defining folds +2024-05-01 17:19:13,603:INFO:Declaring metric variables +2024-05-01 17:19:13,619:INFO:Importing untrained model +2024-05-01 17:19:13,638:INFO:Elastic Net Imported successfully +2024-05-01 17:19:13,668:INFO:Starting cross validation +2024-05-01 17:19:13,676:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:19:14,068:INFO:Calculating mean and std +2024-05-01 17:19:14,074:INFO:Creating metrics dataframe +2024-05-01 17:19:14,087:INFO:Uploading results into container +2024-05-01 17:19:14,089:INFO:Uploading model into container now +2024-05-01 17:19:14,092:INFO:_master_model_container: 4 +2024-05-01 17:19:14,093:INFO:_display_container: 2 +2024-05-01 17:19:14,095:INFO:ElasticNet(random_state=3621) +2024-05-01 17:19:14,095:INFO:create_model() successfully completed...................................... +2024-05-01 17:19:14,425:INFO:SubProcess create_model() end ================================== +2024-05-01 17:19:14,426:INFO:Creating metrics dataframe +2024-05-01 17:19:14,462:INFO:Initializing Least Angle Regression +2024-05-01 17:19:14,462:INFO:Total runtime is 0.29298117160797116 minutes +2024-05-01 17:19:14,478:INFO:SubProcess create_model() called ================================== +2024-05-01 17:19:14,480:INFO:Initializing create_model() +2024-05-01 17:19:14,480:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:19:14,481:INFO:Checking exceptions +2024-05-01 17:19:14,481:INFO:Importing libraries +2024-05-01 17:19:14,481:INFO:Copying training dataset +2024-05-01 17:19:14,509:INFO:Defining folds +2024-05-01 17:19:14,510:INFO:Declaring metric variables +2024-05-01 17:19:14,524:INFO:Importing untrained model +2024-05-01 17:19:14,558:INFO:Least Angle Regression Imported successfully +2024-05-01 17:19:14,588:INFO:Starting cross validation +2024-05-01 17:19:14,592:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:19:15,276:INFO:Calculating mean and std +2024-05-01 17:19:15,281:INFO:Creating metrics dataframe +2024-05-01 17:19:15,298:INFO:Uploading results into container +2024-05-01 17:19:15,303:INFO:Uploading model into container now +2024-05-01 17:19:15,304:INFO:_master_model_container: 5 +2024-05-01 17:19:15,304:INFO:_display_container: 2 +2024-05-01 17:19:15,305:INFO:Lars(random_state=3621) +2024-05-01 17:19:15,305:INFO:create_model() successfully completed...................................... +2024-05-01 17:19:15,665:INFO:SubProcess create_model() end ================================== +2024-05-01 17:19:15,666:INFO:Creating metrics dataframe +2024-05-01 17:19:15,720:INFO:Initializing Lasso Least Angle Regression +2024-05-01 17:19:15,764:INFO:Total runtime is 0.3146776994069417 minutes +2024-05-01 17:19:15,790:INFO:SubProcess create_model() called ================================== +2024-05-01 17:19:15,791:INFO:Initializing create_model() +2024-05-01 17:19:15,792:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:19:15,792:INFO:Checking exceptions +2024-05-01 17:19:15,793:INFO:Importing libraries +2024-05-01 17:19:15,793:INFO:Copying training dataset +2024-05-01 17:19:15,842:INFO:Defining folds +2024-05-01 17:19:15,843:INFO:Declaring metric variables +2024-05-01 17:19:15,862:INFO:Importing untrained model +2024-05-01 17:19:15,886:INFO:Lasso Least Angle Regression Imported successfully +2024-05-01 17:19:15,912:INFO:Starting cross validation +2024-05-01 17:19:15,917:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:19:16,326:INFO:Calculating mean and std +2024-05-01 17:19:16,330:INFO:Creating metrics dataframe +2024-05-01 17:19:16,345:INFO:Uploading results into container +2024-05-01 17:19:16,347:INFO:Uploading model into container now +2024-05-01 17:19:16,349:INFO:_master_model_container: 6 +2024-05-01 17:19:16,349:INFO:_display_container: 2 +2024-05-01 17:19:16,350:INFO:LassoLars(random_state=3621) +2024-05-01 17:19:16,350:INFO:create_model() successfully completed...................................... +2024-05-01 17:19:16,659:INFO:SubProcess create_model() end ================================== +2024-05-01 17:19:16,661:INFO:Creating metrics dataframe +2024-05-01 17:19:16,693:INFO:Initializing Orthogonal Matching Pursuit +2024-05-01 17:19:16,693:INFO:Total runtime is 0.33016327619552605 minutes +2024-05-01 17:19:16,703:INFO:SubProcess create_model() called ================================== +2024-05-01 17:19:16,704:INFO:Initializing create_model() +2024-05-01 17:19:16,704:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:19:16,705:INFO:Checking exceptions +2024-05-01 17:19:16,705:INFO:Importing libraries +2024-05-01 17:19:16,705:INFO:Copying training dataset +2024-05-01 17:19:16,724:INFO:Defining folds +2024-05-01 17:19:16,724:INFO:Declaring metric variables +2024-05-01 17:19:16,747:INFO:Importing untrained model +2024-05-01 17:19:16,760:INFO:Orthogonal Matching Pursuit Imported successfully +2024-05-01 17:19:16,799:INFO:Starting cross validation +2024-05-01 17:19:16,803:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:19:17,177:INFO:Calculating mean and std +2024-05-01 17:19:17,187:INFO:Creating metrics dataframe +2024-05-01 17:19:17,198:INFO:Uploading results into container +2024-05-01 17:19:17,200:INFO:Uploading model into container now +2024-05-01 17:19:17,202:INFO:_master_model_container: 7 +2024-05-01 17:19:17,202:INFO:_display_container: 2 +2024-05-01 17:19:17,204:INFO:OrthogonalMatchingPursuit() +2024-05-01 17:19:17,204:INFO:create_model() successfully completed...................................... +2024-05-01 17:19:17,457:INFO:SubProcess create_model() end ================================== +2024-05-01 17:19:17,457:INFO:Creating metrics dataframe +2024-05-01 17:19:17,483:INFO:Initializing Bayesian Ridge +2024-05-01 17:19:17,483:INFO:Total runtime is 0.3433342774709065 minutes +2024-05-01 17:19:17,495:INFO:SubProcess create_model() called ================================== +2024-05-01 17:19:17,496:INFO:Initializing create_model() +2024-05-01 17:19:17,496:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:19:17,496:INFO:Checking exceptions +2024-05-01 17:19:17,497:INFO:Importing libraries +2024-05-01 17:19:17,497:INFO:Copying training dataset +2024-05-01 17:19:17,515:INFO:Defining folds +2024-05-01 17:19:17,515:INFO:Declaring metric variables +2024-05-01 17:19:17,529:INFO:Importing untrained model +2024-05-01 17:19:17,546:INFO:Bayesian Ridge Imported successfully +2024-05-01 17:19:17,577:INFO:Starting cross validation +2024-05-01 17:19:17,585:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:19:17,979:INFO:Calculating mean and std +2024-05-01 17:19:17,983:INFO:Creating metrics dataframe +2024-05-01 17:19:17,998:INFO:Uploading results into container +2024-05-01 17:19:17,999:INFO:Uploading model into container now +2024-05-01 17:19:18,001:INFO:_master_model_container: 8 +2024-05-01 17:19:18,002:INFO:_display_container: 2 +2024-05-01 17:19:18,003:INFO:BayesianRidge() +2024-05-01 17:19:18,003:INFO:create_model() successfully completed...................................... +2024-05-01 17:19:18,279:INFO:SubProcess create_model() end ================================== +2024-05-01 17:19:18,279:INFO:Creating metrics dataframe +2024-05-01 17:19:18,308:INFO:Initializing Passive Aggressive Regressor +2024-05-01 17:19:18,308:INFO:Total runtime is 0.3570802211761474 minutes +2024-05-01 17:19:18,319:INFO:SubProcess create_model() called ================================== +2024-05-01 17:19:18,319:INFO:Initializing create_model() +2024-05-01 17:19:18,319:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:19:18,319:INFO:Checking exceptions +2024-05-01 17:19:18,320:INFO:Importing libraries +2024-05-01 17:19:18,320:INFO:Copying training dataset +2024-05-01 17:19:18,338:INFO:Defining folds +2024-05-01 17:19:18,338:INFO:Declaring metric variables +2024-05-01 17:19:18,353:INFO:Importing untrained model +2024-05-01 17:19:18,372:INFO:Passive Aggressive Regressor Imported successfully +2024-05-01 17:19:18,471:INFO:Starting cross validation +2024-05-01 17:19:18,474:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:19:19,902:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:19:19,914:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:19:19,921:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:19:19,928:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:19:21,279:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:19:21,289:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:19:21,314:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:19:21,321:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:19:22,332:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:19:22,342:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:19:22,372:INFO:Calculating mean and std +2024-05-01 17:19:22,374:INFO:Creating metrics dataframe +2024-05-01 17:19:22,391:INFO:Uploading results into container +2024-05-01 17:19:22,393:INFO:Uploading model into container now +2024-05-01 17:19:22,395:INFO:_master_model_container: 9 +2024-05-01 17:19:22,395:INFO:_display_container: 2 +2024-05-01 17:19:22,397:INFO:PassiveAggressiveRegressor(random_state=3621) +2024-05-01 17:19:22,397:INFO:create_model() successfully completed...................................... +2024-05-01 17:19:22,647:INFO:SubProcess create_model() end ================================== +2024-05-01 17:19:22,647:INFO:Creating metrics dataframe +2024-05-01 17:19:22,685:INFO:Initializing Huber Regressor +2024-05-01 17:19:22,685:INFO:Total runtime is 0.4300294001897176 minutes +2024-05-01 17:19:22,699:INFO:SubProcess create_model() called ================================== +2024-05-01 17:19:22,700:INFO:Initializing create_model() +2024-05-01 17:19:22,701:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:19:22,701:INFO:Checking exceptions +2024-05-01 17:19:22,702:INFO:Importing libraries +2024-05-01 17:19:22,703:INFO:Copying training dataset +2024-05-01 17:19:22,728:INFO:Defining folds +2024-05-01 17:19:22,728:INFO:Declaring metric variables +2024-05-01 17:19:22,742:INFO:Importing untrained model +2024-05-01 17:19:22,764:INFO:Huber Regressor Imported successfully +2024-05-01 17:19:22,807:INFO:Starting cross validation +2024-05-01 17:19:22,815:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:19:23,468:INFO:Calculating mean and std +2024-05-01 17:19:23,471:INFO:Creating metrics dataframe +2024-05-01 17:19:23,487:INFO:Uploading results into container +2024-05-01 17:19:23,488:INFO:Uploading model into container now +2024-05-01 17:19:23,489:INFO:_master_model_container: 10 +2024-05-01 17:19:23,489:INFO:_display_container: 2 +2024-05-01 17:19:23,489:INFO:HuberRegressor() +2024-05-01 17:19:23,490:INFO:create_model() successfully completed...................................... +2024-05-01 17:19:23,955:INFO:SubProcess create_model() end ================================== +2024-05-01 17:19:23,956:INFO:Creating metrics dataframe +2024-05-01 17:19:24,001:INFO:Initializing K Neighbors Regressor +2024-05-01 17:19:24,002:INFO:Total runtime is 0.45196787913640335 minutes +2024-05-01 17:19:24,011:INFO:SubProcess create_model() called ================================== +2024-05-01 17:19:24,012:INFO:Initializing create_model() +2024-05-01 17:19:24,012:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:19:24,012:INFO:Checking exceptions +2024-05-01 17:19:24,013:INFO:Importing libraries +2024-05-01 17:19:24,013:INFO:Copying training dataset +2024-05-01 17:19:24,028:INFO:Defining folds +2024-05-01 17:19:24,028:INFO:Declaring metric variables +2024-05-01 17:19:24,044:INFO:Importing untrained model +2024-05-01 17:19:24,061:INFO:K Neighbors Regressor Imported successfully +2024-05-01 17:19:24,097:INFO:Starting cross validation +2024-05-01 17:19:24,101:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:19:24,796:INFO:Calculating mean and std +2024-05-01 17:19:24,805:INFO:Creating metrics dataframe +2024-05-01 17:19:24,822:INFO:Uploading results into container +2024-05-01 17:19:24,825:INFO:Uploading model into container now +2024-05-01 17:19:24,827:INFO:_master_model_container: 11 +2024-05-01 17:19:24,827:INFO:_display_container: 2 +2024-05-01 17:19:24,828:INFO:KNeighborsRegressor(n_jobs=-1) +2024-05-01 17:19:24,828:INFO:create_model() successfully completed...................................... +2024-05-01 17:19:25,108:INFO:SubProcess create_model() end ================================== +2024-05-01 17:19:25,108:INFO:Creating metrics dataframe +2024-05-01 17:19:25,138:INFO:Initializing Decision Tree Regressor +2024-05-01 17:19:25,138:INFO:Total runtime is 0.4709088206291198 minutes +2024-05-01 17:19:25,150:INFO:SubProcess create_model() called ================================== +2024-05-01 17:19:25,152:INFO:Initializing create_model() +2024-05-01 17:19:25,152:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:19:25,152:INFO:Checking exceptions +2024-05-01 17:19:25,153:INFO:Importing libraries +2024-05-01 17:19:25,153:INFO:Copying training dataset +2024-05-01 17:19:25,172:INFO:Defining folds +2024-05-01 17:19:25,172:INFO:Declaring metric variables +2024-05-01 17:19:25,187:INFO:Importing untrained model +2024-05-01 17:19:25,202:INFO:Decision Tree Regressor Imported successfully +2024-05-01 17:19:25,229:INFO:Starting cross validation +2024-05-01 17:19:25,232:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:19:26,058:INFO:Calculating mean and std +2024-05-01 17:19:26,063:INFO:Creating metrics dataframe +2024-05-01 17:19:26,077:INFO:Uploading results into container +2024-05-01 17:19:26,079:INFO:Uploading model into container now +2024-05-01 17:19:26,084:INFO:_master_model_container: 12 +2024-05-01 17:19:26,085:INFO:_display_container: 2 +2024-05-01 17:19:26,088:INFO:DecisionTreeRegressor(random_state=3621) +2024-05-01 17:19:26,088:INFO:create_model() successfully completed...................................... +2024-05-01 17:19:26,366:INFO:SubProcess create_model() end ================================== +2024-05-01 17:19:26,366:INFO:Creating metrics dataframe +2024-05-01 17:19:26,419:INFO:Initializing Random Forest Regressor +2024-05-01 17:19:26,420:INFO:Total runtime is 0.49227225780487055 minutes +2024-05-01 17:19:26,434:INFO:SubProcess create_model() called ================================== +2024-05-01 17:19:26,435:INFO:Initializing create_model() +2024-05-01 17:19:26,435:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:19:26,436:INFO:Checking exceptions +2024-05-01 17:19:26,436:INFO:Importing libraries +2024-05-01 17:19:26,436:INFO:Copying training dataset +2024-05-01 17:19:26,460:INFO:Defining folds +2024-05-01 17:19:26,461:INFO:Declaring metric variables +2024-05-01 17:19:26,473:INFO:Importing untrained model +2024-05-01 17:19:26,492:INFO:Random Forest Regressor Imported successfully +2024-05-01 17:19:26,547:INFO:Starting cross validation +2024-05-01 17:19:26,550:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:20:09,302:INFO:Calculating mean and std +2024-05-01 17:20:09,313:INFO:Creating metrics dataframe +2024-05-01 17:20:09,469:INFO:Uploading results into container +2024-05-01 17:20:09,505:INFO:Uploading model into container now +2024-05-01 17:20:09,515:INFO:_master_model_container: 13 +2024-05-01 17:20:09,516:INFO:_display_container: 2 +2024-05-01 17:20:09,520:INFO:RandomForestRegressor(n_jobs=-1, random_state=3621) +2024-05-01 17:20:09,522:INFO:create_model() successfully completed...................................... +2024-05-01 17:20:10,784:INFO:SubProcess create_model() end ================================== +2024-05-01 17:20:10,785:INFO:Creating metrics dataframe +2024-05-01 17:20:10,954:INFO:Initializing Extra Trees Regressor +2024-05-01 17:20:10,955:INFO:Total runtime is 1.23452566464742 minutes +2024-05-01 17:20:11,039:INFO:SubProcess create_model() called ================================== +2024-05-01 17:20:11,043:INFO:Initializing create_model() +2024-05-01 17:20:11,044:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:20:11,045:INFO:Checking exceptions +2024-05-01 17:20:11,046:INFO:Importing libraries +2024-05-01 17:20:11,047:INFO:Copying training dataset +2024-05-01 17:20:11,549:INFO:Defining folds +2024-05-01 17:20:11,551:INFO:Declaring metric variables +2024-05-01 17:20:11,708:INFO:Importing untrained model +2024-05-01 17:20:11,777:INFO:Extra Trees Regressor Imported successfully +2024-05-01 17:20:11,985:INFO:Starting cross validation +2024-05-01 17:20:12,005:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:21:09,432:INFO:Calculating mean and std +2024-05-01 17:21:09,450:INFO:Creating metrics dataframe +2024-05-01 17:21:09,573:INFO:Uploading results into container +2024-05-01 17:21:09,587:INFO:Uploading model into container now +2024-05-01 17:21:09,592:INFO:_master_model_container: 14 +2024-05-01 17:21:09,593:INFO:_display_container: 2 +2024-05-01 17:21:09,615:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=3621) +2024-05-01 17:21:09,616:INFO:create_model() successfully completed...................................... +2024-05-01 17:21:10,878:INFO:SubProcess create_model() end ================================== +2024-05-01 17:21:10,880:INFO:Creating metrics dataframe +2024-05-01 17:21:11,050:INFO:Initializing AdaBoost Regressor +2024-05-01 17:21:11,051:INFO:Total runtime is 2.236125644048055 minutes +2024-05-01 17:21:11,188:INFO:SubProcess create_model() called ================================== +2024-05-01 17:21:11,192:INFO:Initializing create_model() +2024-05-01 17:21:11,192:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:21:11,193:INFO:Checking exceptions +2024-05-01 17:21:11,193:INFO:Importing libraries +2024-05-01 17:21:11,194:INFO:Copying training dataset +2024-05-01 17:21:11,510:INFO:Defining folds +2024-05-01 17:21:11,517:INFO:Declaring metric variables +2024-05-01 17:21:11,620:INFO:Importing untrained model +2024-05-01 17:21:11,692:INFO:AdaBoost Regressor Imported successfully +2024-05-01 17:21:11,887:INFO:Starting cross validation +2024-05-01 17:21:11,915:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:21:25,008:INFO:Calculating mean and std +2024-05-01 17:21:25,021:INFO:Creating metrics dataframe +2024-05-01 17:21:25,059:INFO:Uploading results into container +2024-05-01 17:21:25,072:INFO:Uploading model into container now +2024-05-01 17:21:25,076:INFO:_master_model_container: 15 +2024-05-01 17:21:25,096:INFO:_display_container: 2 +2024-05-01 17:21:25,145:INFO:AdaBoostRegressor(random_state=3621) +2024-05-01 17:21:25,147:INFO:create_model() successfully completed...................................... +2024-05-01 17:21:26,379:INFO:SubProcess create_model() end ================================== +2024-05-01 17:21:26,380:INFO:Creating metrics dataframe +2024-05-01 17:21:26,565:INFO:Initializing Gradient Boosting Regressor +2024-05-01 17:21:26,568:INFO:Total runtime is 2.4947446783383684 minutes +2024-05-01 17:21:26,613:INFO:SubProcess create_model() called ================================== +2024-05-01 17:21:26,617:INFO:Initializing create_model() +2024-05-01 17:21:26,618:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:21:26,619:INFO:Checking exceptions +2024-05-01 17:21:26,620:INFO:Importing libraries +2024-05-01 17:21:26,621:INFO:Copying training dataset +2024-05-01 17:21:26,762:INFO:Defining folds +2024-05-01 17:21:26,763:INFO:Declaring metric variables +2024-05-01 17:21:26,803:INFO:Importing untrained model +2024-05-01 17:21:27,009:INFO:Gradient Boosting Regressor Imported successfully +2024-05-01 17:21:27,412:INFO:Starting cross validation +2024-05-01 17:21:27,528:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:21:55,554:INFO:Calculating mean and std +2024-05-01 17:21:55,615:INFO:Creating metrics dataframe +2024-05-01 17:21:55,757:INFO:Uploading results into container +2024-05-01 17:21:55,768:INFO:Uploading model into container now +2024-05-01 17:21:55,778:INFO:_master_model_container: 16 +2024-05-01 17:21:55,779:INFO:_display_container: 2 +2024-05-01 17:21:55,803:INFO:GradientBoostingRegressor(random_state=3621) +2024-05-01 17:21:55,804:INFO:create_model() successfully completed...................................... +2024-05-01 17:21:57,284:INFO:SubProcess create_model() end ================================== +2024-05-01 17:21:57,286:INFO:Creating metrics dataframe +2024-05-01 17:21:57,560:INFO:Initializing Extreme Gradient Boosting +2024-05-01 17:21:57,561:INFO:Total runtime is 3.0112920681635535 minutes +2024-05-01 17:21:57,611:INFO:SubProcess create_model() called ================================== +2024-05-01 17:21:57,626:INFO:Initializing create_model() +2024-05-01 17:21:57,627:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:21:57,628:INFO:Checking exceptions +2024-05-01 17:21:57,628:INFO:Importing libraries +2024-05-01 17:21:57,630:INFO:Copying training dataset +2024-05-01 17:21:58,102:INFO:Defining folds +2024-05-01 17:21:58,103:INFO:Declaring metric variables +2024-05-01 17:21:58,235:INFO:Importing untrained model +2024-05-01 17:21:58,360:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 17:21:58,591:INFO:Starting cross validation +2024-05-01 17:21:58,600:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:22:27,291:INFO:Calculating mean and std +2024-05-01 17:22:27,305:INFO:Creating metrics dataframe +2024-05-01 17:22:27,465:INFO:Uploading results into container +2024-05-01 17:22:27,480:INFO:Uploading model into container now +2024-05-01 17:22:27,491:INFO:_master_model_container: 17 +2024-05-01 17:22:27,493:INFO:_display_container: 2 +2024-05-01 17:22:27,517: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, predictor=None, random_state=3621, + reg_alpha=None, reg_lambda=None, ...) +2024-05-01 17:22:27,518:INFO:create_model() successfully completed...................................... +2024-05-01 17:22:29,027:INFO:SubProcess create_model() end ================================== +2024-05-01 17:22:29,028:INFO:Creating metrics dataframe +2024-05-01 17:22:29,219:INFO:Initializing Light Gradient Boosting Machine +2024-05-01 17:22:29,220:INFO:Total runtime is 3.5389399290084835 minutes +2024-05-01 17:22:29,305:INFO:SubProcess create_model() called ================================== +2024-05-01 17:22:29,308:INFO:Initializing create_model() +2024-05-01 17:22:29,310:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:22:29,311:INFO:Checking exceptions +2024-05-01 17:22:29,311:INFO:Importing libraries +2024-05-01 17:22:29,312:INFO:Copying training dataset +2024-05-01 17:22:29,745:INFO:Defining folds +2024-05-01 17:22:29,750:INFO:Declaring metric variables +2024-05-01 17:22:29,953:INFO:Importing untrained model +2024-05-01 17:22:30,127:INFO:Light Gradient Boosting Machine Imported successfully +2024-05-01 17:22:30,369:INFO:Starting cross validation +2024-05-01 17:22:30,378:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:22:42,689:INFO:Calculating mean and std +2024-05-01 17:22:42,702:INFO:Creating metrics dataframe +2024-05-01 17:22:42,889:INFO:Uploading results into container +2024-05-01 17:22:42,905:INFO:Uploading model into container now +2024-05-01 17:22:42,923:INFO:_master_model_container: 18 +2024-05-01 17:22:42,925:INFO:_display_container: 2 +2024-05-01 17:22:43,018:INFO:LGBMRegressor(n_jobs=-1, random_state=3621) +2024-05-01 17:22:43,019:INFO:create_model() successfully completed...................................... +2024-05-01 17:22:44,396:INFO:SubProcess create_model() end ================================== +2024-05-01 17:22:44,397:INFO:Creating metrics dataframe +2024-05-01 17:22:44,605:INFO:Initializing Dummy Regressor +2024-05-01 17:22:44,606:INFO:Total runtime is 3.7953701098759964 minutes +2024-05-01 17:22:44,661:INFO:SubProcess create_model() called ================================== +2024-05-01 17:22:44,671:INFO:Initializing create_model() +2024-05-01 17:22:44,672:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:22:44,673:INFO:Checking exceptions +2024-05-01 17:22:44,673:INFO:Importing libraries +2024-05-01 17:22:44,674:INFO:Copying training dataset +2024-05-01 17:22:45,026:INFO:Defining folds +2024-05-01 17:22:45,029:INFO:Declaring metric variables +2024-05-01 17:22:45,285:INFO:Importing untrained model +2024-05-01 17:22:45,461:INFO:Dummy Regressor Imported successfully +2024-05-01 17:22:45,727:INFO:Starting cross validation +2024-05-01 17:22:45,737:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:22:48,414:INFO:Calculating mean and std +2024-05-01 17:22:48,426:INFO:Creating metrics dataframe +2024-05-01 17:22:48,502:INFO:Uploading results into container +2024-05-01 17:22:48,510:INFO:Uploading model into container now +2024-05-01 17:22:48,515:INFO:_master_model_container: 19 +2024-05-01 17:22:48,516:INFO:_display_container: 2 +2024-05-01 17:22:48,518:INFO:DummyRegressor() +2024-05-01 17:22:48,520:INFO:create_model() successfully completed...................................... +2024-05-01 17:22:49,848:INFO:SubProcess create_model() end ================================== +2024-05-01 17:22:49,849:INFO:Creating metrics dataframe +2024-05-01 17:22:50,493:INFO:Initializing create_model() +2024-05-01 17:22:50,494:INFO:create_model(self=, estimator=LGBMRegressor(n_jobs=-1, random_state=3621), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:22:50,495:INFO:Checking exceptions +2024-05-01 17:22:50,672:INFO:Importing libraries +2024-05-01 17:22:50,673:INFO:Copying training dataset +2024-05-01 17:22:51,181:INFO:Defining folds +2024-05-01 17:22:51,189:INFO:Declaring metric variables +2024-05-01 17:22:51,202:INFO:Importing untrained model +2024-05-01 17:22:51,212:INFO:Declaring custom model +2024-05-01 17:22:51,404:INFO:Light Gradient Boosting Machine Imported successfully +2024-05-01 17:22:51,529:INFO:Cross validation set to False +2024-05-01 17:22:51,549:INFO:Fitting Model +2024-05-01 17:22:52,579:INFO:[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.021114 seconds. +2024-05-01 17:22:52,580:INFO:You can set `force_col_wise=true` to remove the overhead. +2024-05-01 17:22:52,581:INFO:[LightGBM] [Info] Total Bins 1099 +2024-05-01 17:22:52,589:INFO:[LightGBM] [Info] Number of data points in the train set: 4504, number of used features: 9 +2024-05-01 17:22:52,593:INFO:[LightGBM] [Info] Start training from score 1045141.614516 +2024-05-01 17:22:56,988:INFO:LGBMRegressor(n_jobs=-1, random_state=3621) +2024-05-01 17:22:56,989:INFO:create_model() successfully completed...................................... +2024-05-01 17:23:01,366:INFO:_master_model_container: 19 +2024-05-01 17:23:01,374:INFO:_display_container: 2 +2024-05-01 17:23:01,377:INFO:LGBMRegressor(n_jobs=-1, random_state=3621) +2024-05-01 17:23:01,378:INFO:compare_models() successfully completed...................................... +2024-05-01 17:23:12,106:INFO:Initializing create_model() +2024-05-01 17:23:12,107:INFO:create_model(self=, estimator=xgboost, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:23:12,107:INFO:Checking exceptions +2024-05-01 17:23:12,331:INFO:Importing libraries +2024-05-01 17:23:12,337:INFO:Copying training dataset +2024-05-01 17:23:12,583:INFO:Defining folds +2024-05-01 17:23:12,585:INFO:Declaring metric variables +2024-05-01 17:23:13,047:INFO:Importing untrained model +2024-05-01 17:23:13,295:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 17:23:13,813:INFO:Starting cross validation +2024-05-01 17:23:13,821:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:23:48,102:INFO:Calculating mean and std +2024-05-01 17:23:48,121:INFO:Creating metrics dataframe +2024-05-01 17:23:48,269:INFO:Finalizing model +2024-05-01 17:23:52,500:INFO:Uploading results into container +2024-05-01 17:23:52,619:INFO:Uploading model into container now +2024-05-01 17:23:52,996:INFO:_master_model_container: 20 +2024-05-01 17:23:53,000:INFO:_display_container: 3 +2024-05-01 17:23:53,093: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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 17:23:53,094:INFO:create_model() successfully completed...................................... +2024-05-01 17:24:01,882:INFO:Initializing evaluate_model() +2024-05-01 17:24:01,883:INFO:evaluate_model(self=, estimator=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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) +2024-05-01 17:24:02,196:INFO:Initializing plot_model() +2024-05-01 17:24:02,197:INFO:plot_model(self=, estimator=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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...), plot=pipeline, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-05-01 17:24:02,198:INFO:Checking exceptions +2024-05-01 17:24:02,486:INFO:Preloading libraries +2024-05-01 17:24:02,705:INFO:Copying training dataset +2024-05-01 17:24:02,706:INFO:Plot type: pipeline +2024-05-01 17:24:08,083:INFO:Visual Rendered Successfully +2024-05-01 17:24:10,073:INFO:plot_model() successfully completed...................................... +2024-05-01 17:24:18,196:INFO:Initializing tune_model() +2024-05-01 17:24:18,197:INFO:tune_model(self=, estimator=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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=scikit-learn, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-05-01 17:24:18,197:INFO:Checking exceptions +2024-05-01 17:24:18,395:INFO:Copying training dataset +2024-05-01 17:24:18,490:INFO:Checking base model +2024-05-01 17:24:18,492:INFO:Base model : Extreme Gradient Boosting +2024-05-01 17:24:18,799:INFO:Declaring metric variables +2024-05-01 17:24:18,993:INFO:Defining Hyperparameters +2024-05-01 17:24:23,735:INFO:Tuning with n_jobs=-1 +2024-05-01 17:24:23,736:INFO:Initializing RandomizedSearchCV +2024-05-01 17:29:11,533:INFO:best_params: {'actual_estimator__subsample': 0.7, 'actual_estimator__scale_pos_weight': 36.0, 'actual_estimator__reg_lambda': 0.001, 'actual_estimator__reg_alpha': 0.0001, 'actual_estimator__n_estimators': 190, 'actual_estimator__min_child_weight': 3, 'actual_estimator__max_depth': 6, 'actual_estimator__learning_rate': 0.3, 'actual_estimator__colsample_bytree': 0.9} +2024-05-01 17:29:11,541:INFO:Hyperparameter search completed +2024-05-01 17:29:11,542:INFO:SubProcess create_model() called ================================== +2024-05-01 17:29:11,555:INFO:Initializing create_model() +2024-05-01 17:29:11,560:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'subsample': 0.7, 'scale_pos_weight': 36.0, 'reg_lambda': 0.001, 'reg_alpha': 0.0001, 'n_estimators': 190, 'min_child_weight': 3, 'max_depth': 6, 'learning_rate': 0.3, 'colsample_bytree': 0.9}) +2024-05-01 17:29:11,596:INFO:Checking exceptions +2024-05-01 17:29:11,597:INFO:Importing libraries +2024-05-01 17:29:11,598:INFO:Copying training dataset +2024-05-01 17:29:11,662:INFO:Defining folds +2024-05-01 17:29:11,663:INFO:Declaring metric variables +2024-05-01 17:29:11,736:INFO:Importing untrained model +2024-05-01 17:29:11,737:INFO:Declaring custom model +2024-05-01 17:29:11,817:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 17:29:12,013:INFO:Starting cross validation +2024-05-01 17:29:12,042:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:29:47,530:INFO:Calculating mean and std +2024-05-01 17:29:47,542:INFO:Creating metrics dataframe +2024-05-01 17:29:47,649:INFO:Finalizing model +2024-05-01 17:29:53,253:INFO:Uploading results into container +2024-05-01 17:29:53,258:INFO:Uploading model into container now +2024-05-01 17:29:53,262:INFO:_master_model_container: 21 +2024-05-01 17:29:53,263:INFO:_display_container: 4 +2024-05-01 17:29:53,335:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.9, + 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=0.3, max_bin=256, max_cat_to_onehot=4, + max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=3, + missing=nan, monotone_constraints='()', n_estimators=190, + n_jobs=-1, num_parallel_tree=1, predictor='auto', + random_state=3621, reg_alpha=0.0001, reg_lambda=0.001, ...) +2024-05-01 17:29:53,336:INFO:create_model() successfully completed...................................... +2024-05-01 17:29:54,000:INFO:SubProcess create_model() end ================================== +2024-05-01 17:29:54,000:INFO:choose_better activated +2024-05-01 17:29:54,037:INFO:SubProcess create_model() called ================================== +2024-05-01 17:29:54,072:INFO:Initializing create_model() +2024-05-01 17:29:54,073:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:29:54,074:INFO:Checking exceptions +2024-05-01 17:29:54,102:INFO:Importing libraries +2024-05-01 17:29:54,103:INFO:Copying training dataset +2024-05-01 17:29:54,161:INFO:Defining folds +2024-05-01 17:29:54,161:INFO:Declaring metric variables +2024-05-01 17:29:54,164:INFO:Importing untrained model +2024-05-01 17:29:54,165:INFO:Declaring custom model +2024-05-01 17:29:54,192:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 17:29:54,195:INFO:Starting cross validation +2024-05-01 17:29:54,203:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:30:14,830:INFO:Calculating mean and std +2024-05-01 17:30:14,835:INFO:Creating metrics dataframe +2024-05-01 17:30:14,856:INFO:Finalizing model +2024-05-01 17:30:17,784:INFO:Uploading results into container +2024-05-01 17:30:17,788:INFO:Uploading model into container now +2024-05-01 17:30:17,791:INFO:_master_model_container: 22 +2024-05-01 17:30:17,791:INFO:_display_container: 5 +2024-05-01 17:30:17,816: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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 17:30:17,817:INFO:create_model() successfully completed...................................... +2024-05-01 17:30:18,421:INFO:SubProcess create_model() end ================================== +2024-05-01 17:30:18,445: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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...) result for R2 is 0.9763 +2024-05-01 17:30:18,469:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.9, + 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=0.3, max_bin=256, max_cat_to_onehot=4, + max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=3, + missing=nan, monotone_constraints='()', n_estimators=190, + n_jobs=-1, num_parallel_tree=1, predictor='auto', + random_state=3621, reg_alpha=0.0001, reg_lambda=0.001, ...) result for R2 is 0.9701 +2024-05-01 17:30:18,495: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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...) is best model +2024-05-01 17:30:18,495:INFO:choose_better completed +2024-05-01 17:30:18,498:INFO:Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one). +2024-05-01 17:30:18,624:INFO:_master_model_container: 22 +2024-05-01 17:30:18,626:INFO:_display_container: 4 +2024-05-01 17:30:18,653: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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 17:30:18,654:INFO:tune_model() successfully completed...................................... +2024-05-01 17:33:29,881:INFO:Initializing predict_model() +2024-05-01 17:33:29,881:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001A966F07880>) +2024-05-01 17:33:29,882:INFO:Checking exceptions +2024-05-01 17:33:29,883:INFO:Preloading libraries +2024-05-01 17:33:29,895:INFO:Set up data. +2024-05-01 17:33:29,945:INFO:Set up index. +2024-05-01 17:40:49,909:INFO:Initializing predict_model() +2024-05-01 17:40:49,910:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001A967161800>) +2024-05-01 17:40:49,910:INFO:Checking exceptions +2024-05-01 17:40:49,911:INFO:Preloading libraries +2024-05-01 17:41:22,932:INFO:Initializing predict_model() +2024-05-01 17:41:22,933:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001A968C8A0C0>) +2024-05-01 17:41:22,934:INFO:Checking exceptions +2024-05-01 17:41:22,935:INFO:Preloading libraries +2024-05-01 17:43:54,017:INFO:Initializing predict_model() +2024-05-01 17:43:54,017:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001A968DFA0C0>) +2024-05-01 17:43:54,018:INFO:Checking exceptions +2024-05-01 17:43:54,018:INFO:Preloading libraries +2024-05-01 17:44:16,096:INFO:Initializing predict_model() +2024-05-01 17:44:16,097:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001A966F07CE0>) +2024-05-01 17:44:16,097:INFO:Checking exceptions +2024-05-01 17:44:16,097:INFO:Preloading libraries +2024-05-01 17:44:16,102:INFO:Set up data. +2024-05-01 17:44:16,115:INFO:Set up index. +2024-05-01 17:44:53,917:INFO:Initializing predict_model() +2024-05-01 17:44:53,918:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001A968DFB7E0>) +2024-05-01 17:44:53,918:INFO:Checking exceptions +2024-05-01 17:44:53,918:INFO:Preloading libraries +2024-05-01 17:44:53,921:INFO:Set up data. +2024-05-01 17:44:53,933:INFO:Set up index. +2024-05-01 17:45:12,796:INFO:Initializing predict_model() +2024-05-01 17:45:12,797:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001A968DFACA0>) +2024-05-01 17:45:12,797:INFO:Checking exceptions +2024-05-01 17:45:12,798:INFO:Preloading libraries +2024-05-01 17:45:12,803:INFO:Set up data. +2024-05-01 17:45:12,814:INFO:Set up index. +2024-05-01 17:47:46,649:INFO:Initializing predict_model() +2024-05-01 17:47:46,649:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001A968DFB2E0>) +2024-05-01 17:47:46,650:INFO:Checking exceptions +2024-05-01 17:47:46,650:INFO:Preloading libraries +2024-05-01 17:47:46,653:INFO:Set up data. +2024-05-01 17:47:46,665:INFO:Set up index. +2024-05-01 17:51:48,604:INFO:Initializing predict_model() +2024-05-01 17:51:48,604:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=3621, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001A968DFA160>) +2024-05-01 17:51:48,604:INFO:Checking exceptions +2024-05-01 17:51:48,604:INFO:Preloading libraries +2024-05-01 17:51:48,609:INFO:Set up data. +2024-05-01 17:51:48,620:INFO:Set up index. +2024-05-01 17:54:04,945:INFO:PyCaret RegressionExperiment +2024-05-01 17:54:04,946:INFO:Logging name: reg-default-name +2024-05-01 17:54:04,946:INFO:ML Usecase: MLUsecase.REGRESSION +2024-05-01 17:54:04,946:INFO:version 3.3.0 +2024-05-01 17:54:04,947:INFO:Initializing setup() +2024-05-01 17:54:04,947:INFO:self.USI: c342 +2024-05-01 17:54:04,947:INFO:self._variable_keys: {'data', '_ml_usecase', 'exp_name_log', 'y_test', 'X', '_available_plots', 'X_train', 'USI', 'html_param', 'X_test', 'exp_id', 'fold_generator', 'log_plots_param', 'pipeline', 'gpu_n_jobs_param', 'idx', 'n_jobs_param', 'logging_param', 'fold_shuffle_param', 'target_param', 'transform_target_param', 'memory', 'y', 'y_train', 'gpu_param', 'seed', 'fold_groups_param'} +2024-05-01 17:54:04,947:INFO:Checking environment +2024-05-01 17:54:04,947:INFO:python_version: 3.11.0 +2024-05-01 17:54:04,948:INFO:python_build: ('main', 'Oct 24 2022 18:26:48') +2024-05-01 17:54:04,948:INFO:machine: AMD64 +2024-05-01 17:54:04,949:INFO:platform: Windows-10-10.0.22000-SP0 +2024-05-01 17:54:04,958:INFO:Memory: svmem(total=8467492864, available=2266263552, percent=73.2, used=6201229312, free=2266263552) +2024-05-01 17:54:04,958:INFO:Physical Core: 2 +2024-05-01 17:54:04,959:INFO:Logical Core: 4 +2024-05-01 17:54:04,959:INFO:Checking libraries +2024-05-01 17:54:04,959:INFO:System: +2024-05-01 17:54:04,959:INFO: python: 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] +2024-05-01 17:54:04,959:INFO:executable: c:\Users\Jason\Documents\DataB3\.venv\Scripts\python.exe +2024-05-01 17:54:04,959:INFO: machine: Windows-10-10.0.22000-SP0 +2024-05-01 17:54:04,959:INFO:PyCaret required dependencies: +2024-05-01 17:54:04,960:INFO: pip: 24.0 +2024-05-01 17:54:04,960:INFO: setuptools: 65.5.0 +2024-05-01 17:54:04,960:INFO: pycaret: 3.3.0 +2024-05-01 17:54:04,960:INFO: IPython: 8.23.0 +2024-05-01 17:54:04,960:INFO: ipywidgets: 8.1.2 +2024-05-01 17:54:04,960:INFO: tqdm: 4.66.2 +2024-05-01 17:54:04,960:INFO: numpy: 1.24.4 +2024-05-01 17:54:04,960:INFO: pandas: 1.5.3 +2024-05-01 17:54:04,960:INFO: jinja2: 3.1.3 +2024-05-01 17:54:04,960:INFO: scipy: 1.11.4 +2024-05-01 17:54:04,960:INFO: joblib: 1.3.2 +2024-05-01 17:54:04,961:INFO: sklearn: 1.4.1.post1 +2024-05-01 17:54:04,961:INFO: pyod: 1.1.3 +2024-05-01 17:54:04,961:INFO: imblearn: 0.12.2 +2024-05-01 17:54:04,961:INFO: category_encoders: 2.6.3 +2024-05-01 17:54:04,961:INFO: lightgbm: 4.3.0 +2024-05-01 17:54:04,961:INFO: numba: 0.59.1 +2024-05-01 17:54:04,961:INFO: requests: 2.31.0 +2024-05-01 17:54:04,961:INFO: matplotlib: 3.8.3 +2024-05-01 17:54:04,961:INFO: scikitplot: 0.3.7 +2024-05-01 17:54:04,961:INFO: yellowbrick: 1.5 +2024-05-01 17:54:04,961:INFO: plotly: 5.20.0 +2024-05-01 17:54:04,962:INFO: plotly-resampler: Not installed +2024-05-01 17:54:04,962:INFO: kaleido: 0.2.1 +2024-05-01 17:54:04,962:INFO: schemdraw: 0.15 +2024-05-01 17:54:04,962:INFO: statsmodels: 0.14.1 +2024-05-01 17:54:04,962:INFO: sktime: 0.28.0 +2024-05-01 17:54:04,962:INFO: tbats: 1.1.3 +2024-05-01 17:54:04,962:INFO: pmdarima: 2.0.4 +2024-05-01 17:54:04,962:INFO: psutil: 5.9.8 +2024-05-01 17:54:04,962:INFO: markupsafe: 2.1.5 +2024-05-01 17:54:04,962:INFO: pickle5: Not installed +2024-05-01 17:54:04,962:INFO: cloudpickle: 3.0.0 +2024-05-01 17:54:04,963:INFO: deprecation: 2.1.0 +2024-05-01 17:54:04,963:INFO: xxhash: 3.4.1 +2024-05-01 17:54:04,963:INFO: wurlitzer: Not installed +2024-05-01 17:54:04,963:INFO:PyCaret optional dependencies: +2024-05-01 17:54:04,963:INFO: shap: Not installed +2024-05-01 17:54:04,963:INFO: interpret: Not installed +2024-05-01 17:54:04,963:INFO: umap: Not installed +2024-05-01 17:54:04,963:INFO: ydata_profiling: 4.7.0 +2024-05-01 17:54:04,963:INFO: explainerdashboard: Not installed +2024-05-01 17:54:04,963:INFO: autoviz: Not installed +2024-05-01 17:54:04,964:INFO: fairlearn: Not installed +2024-05-01 17:54:04,964:INFO: deepchecks: Not installed +2024-05-01 17:54:04,964:INFO: xgboost: 1.6.2 +2024-05-01 17:54:04,964:INFO: catboost: Not installed +2024-05-01 17:54:04,964:INFO: kmodes: Not installed +2024-05-01 17:54:04,964:INFO: mlxtend: Not installed +2024-05-01 17:54:04,964:INFO: statsforecast: Not installed +2024-05-01 17:54:04,964:INFO: tune_sklearn: Not installed +2024-05-01 17:54:04,964:INFO: ray: Not installed +2024-05-01 17:54:04,964:INFO: hyperopt: Not installed +2024-05-01 17:54:04,964:INFO: optuna: 3.6.1 +2024-05-01 17:54:04,964:INFO: skopt: Not installed +2024-05-01 17:54:04,965:INFO: mlflow: Not installed +2024-05-01 17:54:04,965:INFO: gradio: Not installed +2024-05-01 17:54:04,965:INFO: fastapi: Not installed +2024-05-01 17:54:04,965:INFO: uvicorn: Not installed +2024-05-01 17:54:04,965:INFO: m2cgen: Not installed +2024-05-01 17:54:04,965:INFO: evidently: Not installed +2024-05-01 17:54:04,965:INFO: fugue: Not installed +2024-05-01 17:54:04,965:INFO: streamlit: 1.33.0 +2024-05-01 17:54:04,965:INFO: prophet: 1.1.5 +2024-05-01 17:54:04,965:INFO:None +2024-05-01 17:54:04,966:INFO:Set up data. +2024-05-01 17:54:04,979:INFO:Set up folding strategy. +2024-05-01 17:54:04,980:INFO:Set up train/test split. +2024-05-01 17:54:04,984:INFO:Set up index. +2024-05-01 17:54:04,984:INFO:Assigning column types. +2024-05-01 17:54:04,995:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-05-01 17:54:04,995:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 17:54:05,006:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 17:54:05,022:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 17:54:05,209:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:54:05,401:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:54:05,402:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:54:05,412:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:54:05,414:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 17:54:05,427:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 17:54:05,447:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 17:54:05,617:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:54:05,760:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:54:05,761:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:54:05,769:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:54:05,769:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-05-01 17:54:05,786:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 17:54:05,802:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 17:54:05,975:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:54:06,144:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:54:06,145:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:54:06,153:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:54:06,169:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 17:54:06,184:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 17:54:06,389:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:54:06,537:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:54:06,540:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:54:06,548:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:54:06,548:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-05-01 17:54:06,579:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 17:54:06,763:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:54:06,911:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:54:06,912:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:54:06,921:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:54:06,955:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 17:54:07,151:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:54:07,290:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:54:07,291:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:54:07,300:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:54:07,301:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-05-01 17:54:07,513:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:54:07,654:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:54:07,655:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:54:07,664:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:54:07,854:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:54:08,010:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 17:54:08,011:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:54:08,019:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:54:08,020:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-05-01 17:54:08,228:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:54:08,382:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:54:08,390:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:54:08,592:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 17:54:08,731:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:54:08,737:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:54:08,738:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-05-01 17:54:09,083:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:54:09,092:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:54:09,454:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:54:09,462:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:54:09,464:INFO:Preparing preprocessing pipeline... +2024-05-01 17:54:09,464:INFO:Set up simple imputation. +2024-05-01 17:54:09,465:INFO:Set up feature normalization. +2024-05-01 17:54:09,529:INFO:Finished creating preprocessing pipeline. +2024-05-01 17:54:09,538:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\Jason\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWrapper(transformer=MinMaxScaler()))]) +2024-05-01 17:54:09,538:INFO:Creating final display dataframe. +2024-05-01 17:54:09,687:INFO:Setup _display_container: Description Value +0 Session id 342 +1 Target Daily_Sales +2 Target type Regression +3 Original data shape (6435, 7) +4 Transformed data shape (6435, 7) +5 Transformed train set shape (4504, 7) +6 Transformed test set shape (1931, 7) +7 Numeric features 6 +8 Preprocess True +9 Imputation type simple +10 Numeric imputation mean +11 Categorical imputation mode +12 Normalize True +13 Normalize method minmax +14 Fold Generator KFold +15 Fold Number 10 +16 CPU Jobs -1 +17 Use GPU False +18 Log Experiment False +19 Experiment Name reg-default-name +20 USI c342 +2024-05-01 17:54:10,093:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:54:10,104:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:54:10,525:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 17:54:10,549:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 17:54:10,551:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour. + warnings.warn( + +2024-05-01 17:54:10,553:INFO:setup() successfully completed in 5.98s............... +2024-05-01 17:54:10,640:INFO:Initializing compare_models() +2024-05-01 17:54:10,641:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-05-01 17:54:10,641:INFO:Checking exceptions +2024-05-01 17:54:10,650:INFO:Preparing display monitor +2024-05-01 17:54:10,803:INFO:Initializing Linear Regression +2024-05-01 17:54:10,804:INFO:Total runtime is 1.6657511393229165e-05 minutes +2024-05-01 17:54:10,813:INFO:SubProcess create_model() called ================================== +2024-05-01 17:54:10,813:INFO:Initializing create_model() +2024-05-01 17:54:10,814:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:54:10,814:INFO:Checking exceptions +2024-05-01 17:54:10,814:INFO:Importing libraries +2024-05-01 17:54:10,814:INFO:Copying training dataset +2024-05-01 17:54:10,848:INFO:Defining folds +2024-05-01 17:54:10,849:INFO:Declaring metric variables +2024-05-01 17:54:10,870:INFO:Importing untrained model +2024-05-01 17:54:10,888:INFO:Linear Regression Imported successfully +2024-05-01 17:54:10,908:INFO:Starting cross validation +2024-05-01 17:54:10,910:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:54:23,802:INFO:Calculating mean and std +2024-05-01 17:54:23,807:INFO:Creating metrics dataframe +2024-05-01 17:54:23,821:INFO:Uploading results into container +2024-05-01 17:54:23,823:INFO:Uploading model into container now +2024-05-01 17:54:23,824:INFO:_master_model_container: 1 +2024-05-01 17:54:23,825:INFO:_display_container: 2 +2024-05-01 17:54:23,825:INFO:LinearRegression(n_jobs=-1) +2024-05-01 17:54:23,826:INFO:create_model() successfully completed...................................... +2024-05-01 17:54:24,185:INFO:SubProcess create_model() end ================================== +2024-05-01 17:54:24,186:INFO:Creating metrics dataframe +2024-05-01 17:54:24,216:INFO:Initializing Lasso Regression +2024-05-01 17:54:24,216:INFO:Total runtime is 0.22355105876922607 minutes +2024-05-01 17:54:24,230:INFO:SubProcess create_model() called ================================== +2024-05-01 17:54:24,231:INFO:Initializing create_model() +2024-05-01 17:54:24,231:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:54:24,231:INFO:Checking exceptions +2024-05-01 17:54:24,232:INFO:Importing libraries +2024-05-01 17:54:24,232:INFO:Copying training dataset +2024-05-01 17:54:24,252:INFO:Defining folds +2024-05-01 17:54:24,252:INFO:Declaring metric variables +2024-05-01 17:54:24,268:INFO:Importing untrained model +2024-05-01 17:54:24,282:INFO:Lasso Regression Imported successfully +2024-05-01 17:54:24,307:INFO:Starting cross validation +2024-05-01 17:54:24,310:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:54:25,266:INFO:Calculating mean and std +2024-05-01 17:54:25,271:INFO:Creating metrics dataframe +2024-05-01 17:54:25,289:INFO:Uploading results into container +2024-05-01 17:54:25,291:INFO:Uploading model into container now +2024-05-01 17:54:25,293:INFO:_master_model_container: 2 +2024-05-01 17:54:25,293:INFO:_display_container: 2 +2024-05-01 17:54:25,294:INFO:Lasso(random_state=342) +2024-05-01 17:54:25,295:INFO:create_model() successfully completed...................................... +2024-05-01 17:54:25,757:INFO:SubProcess create_model() end ================================== +2024-05-01 17:54:25,758:INFO:Creating metrics dataframe +2024-05-01 17:54:25,801:INFO:Initializing Ridge Regression +2024-05-01 17:54:25,801:INFO:Total runtime is 0.24995979070663452 minutes +2024-05-01 17:54:25,817:INFO:SubProcess create_model() called ================================== +2024-05-01 17:54:25,818:INFO:Initializing create_model() +2024-05-01 17:54:25,818:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:54:25,819:INFO:Checking exceptions +2024-05-01 17:54:25,819:INFO:Importing libraries +2024-05-01 17:54:25,819:INFO:Copying training dataset +2024-05-01 17:54:25,840:INFO:Defining folds +2024-05-01 17:54:25,840:INFO:Declaring metric variables +2024-05-01 17:54:25,857:INFO:Importing untrained model +2024-05-01 17:54:25,872:INFO:Ridge Regression Imported successfully +2024-05-01 17:54:25,898:INFO:Starting cross validation +2024-05-01 17:54:25,901:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:54:26,396:INFO:Calculating mean and std +2024-05-01 17:54:26,401:INFO:Creating metrics dataframe +2024-05-01 17:54:26,415:INFO:Uploading results into container +2024-05-01 17:54:26,417:INFO:Uploading model into container now +2024-05-01 17:54:26,418:INFO:_master_model_container: 3 +2024-05-01 17:54:26,418:INFO:_display_container: 2 +2024-05-01 17:54:26,419:INFO:Ridge(random_state=342) +2024-05-01 17:54:26,419:INFO:create_model() successfully completed...................................... +2024-05-01 17:54:26,772:INFO:SubProcess create_model() end ================================== +2024-05-01 17:54:26,773:INFO:Creating metrics dataframe +2024-05-01 17:54:26,807:INFO:Initializing Elastic Net +2024-05-01 17:54:26,807:INFO:Total runtime is 0.2667275349299113 minutes +2024-05-01 17:54:26,822:INFO:SubProcess create_model() called ================================== +2024-05-01 17:54:26,822:INFO:Initializing create_model() +2024-05-01 17:54:26,823:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:54:26,823:INFO:Checking exceptions +2024-05-01 17:54:26,823:INFO:Importing libraries +2024-05-01 17:54:26,824:INFO:Copying training dataset +2024-05-01 17:54:26,846:INFO:Defining folds +2024-05-01 17:54:26,846:INFO:Declaring metric variables +2024-05-01 17:54:26,863:INFO:Importing untrained model +2024-05-01 17:54:26,877:INFO:Elastic Net Imported successfully +2024-05-01 17:54:26,900:INFO:Starting cross validation +2024-05-01 17:54:26,904:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:54:27,250:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.583e+11, tolerance: 1.277e+11 + model = cd_fast.enet_coordinate_descent( + +2024-05-01 17:54:27,305:INFO:Calculating mean and std +2024-05-01 17:54:27,311:INFO:Creating metrics dataframe +2024-05-01 17:54:27,324:INFO:Uploading results into container +2024-05-01 17:54:27,326:INFO:Uploading model into container now +2024-05-01 17:54:27,327:INFO:_master_model_container: 4 +2024-05-01 17:54:27,328:INFO:_display_container: 2 +2024-05-01 17:54:27,329:INFO:ElasticNet(random_state=342) +2024-05-01 17:54:27,329:INFO:create_model() successfully completed...................................... +2024-05-01 17:54:27,664:INFO:SubProcess create_model() end ================================== +2024-05-01 17:54:27,665:INFO:Creating metrics dataframe +2024-05-01 17:54:27,689:INFO:Initializing Least Angle Regression +2024-05-01 17:54:27,689:INFO:Total runtime is 0.2814304709434509 minutes +2024-05-01 17:54:27,701:INFO:SubProcess create_model() called ================================== +2024-05-01 17:54:27,702:INFO:Initializing create_model() +2024-05-01 17:54:27,702:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:54:27,702:INFO:Checking exceptions +2024-05-01 17:54:27,703:INFO:Importing libraries +2024-05-01 17:54:27,703:INFO:Copying training dataset +2024-05-01 17:54:27,713:INFO:Defining folds +2024-05-01 17:54:27,714:INFO:Declaring metric variables +2024-05-01 17:54:27,730:INFO:Importing untrained model +2024-05-01 17:54:27,741:INFO:Least Angle Regression Imported successfully +2024-05-01 17:54:27,762:INFO:Starting cross validation +2024-05-01 17:54:27,766:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:54:28,175:INFO:Calculating mean and std +2024-05-01 17:54:28,180:INFO:Creating metrics dataframe +2024-05-01 17:54:28,194:INFO:Uploading results into container +2024-05-01 17:54:28,196:INFO:Uploading model into container now +2024-05-01 17:54:28,197:INFO:_master_model_container: 5 +2024-05-01 17:54:28,198:INFO:_display_container: 2 +2024-05-01 17:54:28,200:INFO:Lars(random_state=342) +2024-05-01 17:54:28,201:INFO:create_model() successfully completed...................................... +2024-05-01 17:54:28,558:INFO:SubProcess create_model() end ================================== +2024-05-01 17:54:28,558:INFO:Creating metrics dataframe +2024-05-01 17:54:28,584:INFO:Initializing Lasso Least Angle Regression +2024-05-01 17:54:28,585:INFO:Total runtime is 0.2963499069213867 minutes +2024-05-01 17:54:28,627:INFO:SubProcess create_model() called ================================== +2024-05-01 17:54:28,628:INFO:Initializing create_model() +2024-05-01 17:54:28,628:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:54:28,628:INFO:Checking exceptions +2024-05-01 17:54:28,628:INFO:Importing libraries +2024-05-01 17:54:28,628:INFO:Copying training dataset +2024-05-01 17:54:28,685:INFO:Defining folds +2024-05-01 17:54:28,686:INFO:Declaring metric variables +2024-05-01 17:54:28,700:INFO:Importing untrained model +2024-05-01 17:54:28,721:INFO:Lasso Least Angle Regression Imported successfully +2024-05-01 17:54:28,744:INFO:Starting cross validation +2024-05-01 17:54:28,747:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:54:29,227:INFO:Calculating mean and std +2024-05-01 17:54:29,230:INFO:Creating metrics dataframe +2024-05-01 17:54:29,239:INFO:Uploading results into container +2024-05-01 17:54:29,240:INFO:Uploading model into container now +2024-05-01 17:54:29,242:INFO:_master_model_container: 6 +2024-05-01 17:54:29,243:INFO:_display_container: 2 +2024-05-01 17:54:29,244:INFO:LassoLars(random_state=342) +2024-05-01 17:54:29,245:INFO:create_model() successfully completed...................................... +2024-05-01 17:54:29,563:INFO:SubProcess create_model() end ================================== +2024-05-01 17:54:29,564:INFO:Creating metrics dataframe +2024-05-01 17:54:29,599:INFO:Initializing Orthogonal Matching Pursuit +2024-05-01 17:54:29,599:INFO:Total runtime is 0.31326748927434284 minutes +2024-05-01 17:54:29,611:INFO:SubProcess create_model() called ================================== +2024-05-01 17:54:29,612:INFO:Initializing create_model() +2024-05-01 17:54:29,612:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:54:29,612:INFO:Checking exceptions +2024-05-01 17:54:29,612:INFO:Importing libraries +2024-05-01 17:54:29,613:INFO:Copying training dataset +2024-05-01 17:54:29,623:INFO:Defining folds +2024-05-01 17:54:29,623:INFO:Declaring metric variables +2024-05-01 17:54:29,637:INFO:Importing untrained model +2024-05-01 17:54:29,646:INFO:Orthogonal Matching Pursuit Imported successfully +2024-05-01 17:54:29,665:INFO:Starting cross validation +2024-05-01 17:54:29,669:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:54:30,022:INFO:Calculating mean and std +2024-05-01 17:54:30,026:INFO:Creating metrics dataframe +2024-05-01 17:54:30,043:INFO:Uploading results into container +2024-05-01 17:54:30,046:INFO:Uploading model into container now +2024-05-01 17:54:30,047:INFO:_master_model_container: 7 +2024-05-01 17:54:30,048:INFO:_display_container: 2 +2024-05-01 17:54:30,052:INFO:OrthogonalMatchingPursuit() +2024-05-01 17:54:30,053:INFO:create_model() successfully completed...................................... +2024-05-01 17:54:30,445:INFO:SubProcess create_model() end ================================== +2024-05-01 17:54:30,445:INFO:Creating metrics dataframe +2024-05-01 17:54:30,487:INFO:Initializing Bayesian Ridge +2024-05-01 17:54:30,487:INFO:Total runtime is 0.32807046969731646 minutes +2024-05-01 17:54:30,501:INFO:SubProcess create_model() called ================================== +2024-05-01 17:54:30,502:INFO:Initializing create_model() +2024-05-01 17:54:30,502:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:54:30,503:INFO:Checking exceptions +2024-05-01 17:54:30,503:INFO:Importing libraries +2024-05-01 17:54:30,503:INFO:Copying training dataset +2024-05-01 17:54:30,520:INFO:Defining folds +2024-05-01 17:54:30,521:INFO:Declaring metric variables +2024-05-01 17:54:30,536:INFO:Importing untrained model +2024-05-01 17:54:30,548:INFO:Bayesian Ridge Imported successfully +2024-05-01 17:54:30,574:INFO:Starting cross validation +2024-05-01 17:54:30,577:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:54:30,948:INFO:Calculating mean and std +2024-05-01 17:54:30,954:INFO:Creating metrics dataframe +2024-05-01 17:54:30,966:INFO:Uploading results into container +2024-05-01 17:54:30,968:INFO:Uploading model into container now +2024-05-01 17:54:30,971:INFO:_master_model_container: 8 +2024-05-01 17:54:30,971:INFO:_display_container: 2 +2024-05-01 17:54:30,978:INFO:BayesianRidge() +2024-05-01 17:54:30,979:INFO:create_model() successfully completed...................................... +2024-05-01 17:54:31,308:INFO:SubProcess create_model() end ================================== +2024-05-01 17:54:31,309:INFO:Creating metrics dataframe +2024-05-01 17:54:31,353:INFO:Initializing Passive Aggressive Regressor +2024-05-01 17:54:31,353:INFO:Total runtime is 0.34250695308049517 minutes +2024-05-01 17:54:31,367:INFO:SubProcess create_model() called ================================== +2024-05-01 17:54:31,368:INFO:Initializing create_model() +2024-05-01 17:54:31,368:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:54:31,369:INFO:Checking exceptions +2024-05-01 17:54:31,370:INFO:Importing libraries +2024-05-01 17:54:31,370:INFO:Copying training dataset +2024-05-01 17:54:31,387:INFO:Defining folds +2024-05-01 17:54:31,387:INFO:Declaring metric variables +2024-05-01 17:54:31,402:INFO:Importing untrained model +2024-05-01 17:54:31,415:INFO:Passive Aggressive Regressor Imported successfully +2024-05-01 17:54:31,439:INFO:Starting cross validation +2024-05-01 17:54:31,442:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:54:32,870:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:54:32,878:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:54:32,908:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:54:32,953:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:54:34,189:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:54:34,239:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:54:34,261:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:54:34,284:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:54:35,345:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:54:35,372:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 17:54:35,405:INFO:Calculating mean and std +2024-05-01 17:54:35,408:INFO:Creating metrics dataframe +2024-05-01 17:54:35,418:INFO:Uploading results into container +2024-05-01 17:54:35,420:INFO:Uploading model into container now +2024-05-01 17:54:35,422:INFO:_master_model_container: 9 +2024-05-01 17:54:35,422:INFO:_display_container: 2 +2024-05-01 17:54:35,424:INFO:PassiveAggressiveRegressor(random_state=342) +2024-05-01 17:54:35,425:INFO:create_model() successfully completed...................................... +2024-05-01 17:54:35,734:INFO:SubProcess create_model() end ================================== +2024-05-01 17:54:35,734:INFO:Creating metrics dataframe +2024-05-01 17:54:35,778:INFO:Initializing Huber Regressor +2024-05-01 17:54:35,779:INFO:Total runtime is 0.4162639260292053 minutes +2024-05-01 17:54:35,791:INFO:SubProcess create_model() called ================================== +2024-05-01 17:54:35,792:INFO:Initializing create_model() +2024-05-01 17:54:35,793:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:54:35,793:INFO:Checking exceptions +2024-05-01 17:54:35,794:INFO:Importing libraries +2024-05-01 17:54:35,794:INFO:Copying training dataset +2024-05-01 17:54:35,811:INFO:Defining folds +2024-05-01 17:54:35,812:INFO:Declaring metric variables +2024-05-01 17:54:35,829:INFO:Importing untrained model +2024-05-01 17:54:35,840:INFO:Huber Regressor Imported successfully +2024-05-01 17:54:35,863:INFO:Starting cross validation +2024-05-01 17:54:35,867:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:54:36,387:INFO:Calculating mean and std +2024-05-01 17:54:36,390:INFO:Creating metrics dataframe +2024-05-01 17:54:36,404:INFO:Uploading results into container +2024-05-01 17:54:36,406:INFO:Uploading model into container now +2024-05-01 17:54:36,407:INFO:_master_model_container: 10 +2024-05-01 17:54:36,408:INFO:_display_container: 2 +2024-05-01 17:54:36,408:INFO:HuberRegressor() +2024-05-01 17:54:36,408:INFO:create_model() successfully completed...................................... +2024-05-01 17:54:36,763:INFO:SubProcess create_model() end ================================== +2024-05-01 17:54:36,764:INFO:Creating metrics dataframe +2024-05-01 17:54:36,799:INFO:Initializing K Neighbors Regressor +2024-05-01 17:54:36,799:INFO:Total runtime is 0.43326470057169597 minutes +2024-05-01 17:54:36,812:INFO:SubProcess create_model() called ================================== +2024-05-01 17:54:36,813:INFO:Initializing create_model() +2024-05-01 17:54:36,813:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:54:36,813:INFO:Checking exceptions +2024-05-01 17:54:36,814:INFO:Importing libraries +2024-05-01 17:54:36,814:INFO:Copying training dataset +2024-05-01 17:54:36,829:INFO:Defining folds +2024-05-01 17:54:36,829:INFO:Declaring metric variables +2024-05-01 17:54:36,842:INFO:Importing untrained model +2024-05-01 17:54:36,852:INFO:K Neighbors Regressor Imported successfully +2024-05-01 17:54:36,870:INFO:Starting cross validation +2024-05-01 17:54:36,874:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:54:37,346:INFO:Calculating mean and std +2024-05-01 17:54:37,349:INFO:Creating metrics dataframe +2024-05-01 17:54:37,356:INFO:Uploading results into container +2024-05-01 17:54:37,357:INFO:Uploading model into container now +2024-05-01 17:54:37,358:INFO:_master_model_container: 11 +2024-05-01 17:54:37,358:INFO:_display_container: 2 +2024-05-01 17:54:37,359:INFO:KNeighborsRegressor(n_jobs=-1) +2024-05-01 17:54:37,360:INFO:create_model() successfully completed...................................... +2024-05-01 17:54:37,622:INFO:SubProcess create_model() end ================================== +2024-05-01 17:54:37,623:INFO:Creating metrics dataframe +2024-05-01 17:54:37,643:INFO:Initializing Decision Tree Regressor +2024-05-01 17:54:37,644:INFO:Total runtime is 0.44734315474828085 minutes +2024-05-01 17:54:37,652:INFO:SubProcess create_model() called ================================== +2024-05-01 17:54:37,653:INFO:Initializing create_model() +2024-05-01 17:54:37,653:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:54:37,654:INFO:Checking exceptions +2024-05-01 17:54:37,654:INFO:Importing libraries +2024-05-01 17:54:37,654:INFO:Copying training dataset +2024-05-01 17:54:37,664:INFO:Defining folds +2024-05-01 17:54:37,664:INFO:Declaring metric variables +2024-05-01 17:54:37,675:INFO:Importing untrained model +2024-05-01 17:54:37,687:INFO:Decision Tree Regressor Imported successfully +2024-05-01 17:54:37,703:INFO:Starting cross validation +2024-05-01 17:54:37,709:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:54:38,287:INFO:Calculating mean and std +2024-05-01 17:54:38,291:INFO:Creating metrics dataframe +2024-05-01 17:54:38,303:INFO:Uploading results into container +2024-05-01 17:54:38,304:INFO:Uploading model into container now +2024-05-01 17:54:38,305:INFO:_master_model_container: 12 +2024-05-01 17:54:38,305:INFO:_display_container: 2 +2024-05-01 17:54:38,306:INFO:DecisionTreeRegressor(random_state=342) +2024-05-01 17:54:38,306:INFO:create_model() successfully completed...................................... +2024-05-01 17:54:38,687:INFO:SubProcess create_model() end ================================== +2024-05-01 17:54:38,687:INFO:Creating metrics dataframe +2024-05-01 17:54:38,724:INFO:Initializing Random Forest Regressor +2024-05-01 17:54:38,725:INFO:Total runtime is 0.46536831855773925 minutes +2024-05-01 17:54:38,778:INFO:SubProcess create_model() called ================================== +2024-05-01 17:54:38,779:INFO:Initializing create_model() +2024-05-01 17:54:38,779:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:54:38,779:INFO:Checking exceptions +2024-05-01 17:54:38,779:INFO:Importing libraries +2024-05-01 17:54:38,779:INFO:Copying training dataset +2024-05-01 17:54:38,859:INFO:Defining folds +2024-05-01 17:54:38,862:INFO:Declaring metric variables +2024-05-01 17:54:38,875:INFO:Importing untrained model +2024-05-01 17:54:38,918:INFO:Random Forest Regressor Imported successfully +2024-05-01 17:54:38,947:INFO:Starting cross validation +2024-05-01 17:54:38,950:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:54:57,532:INFO:Calculating mean and std +2024-05-01 17:54:57,536:INFO:Creating metrics dataframe +2024-05-01 17:54:57,557:INFO:Uploading results into container +2024-05-01 17:54:57,559:INFO:Uploading model into container now +2024-05-01 17:54:57,560:INFO:_master_model_container: 13 +2024-05-01 17:54:57,561:INFO:_display_container: 2 +2024-05-01 17:54:57,562:INFO:RandomForestRegressor(n_jobs=-1, random_state=342) +2024-05-01 17:54:57,562:INFO:create_model() successfully completed...................................... +2024-05-01 17:54:57,975:INFO:SubProcess create_model() end ================================== +2024-05-01 17:54:57,976:INFO:Creating metrics dataframe +2024-05-01 17:54:58,026:INFO:Initializing Extra Trees Regressor +2024-05-01 17:54:58,027:INFO:Total runtime is 0.7870699008305868 minutes +2024-05-01 17:54:58,041:INFO:SubProcess create_model() called ================================== +2024-05-01 17:54:58,042:INFO:Initializing create_model() +2024-05-01 17:54:58,042:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:54:58,045:INFO:Checking exceptions +2024-05-01 17:54:58,046:INFO:Importing libraries +2024-05-01 17:54:58,047:INFO:Copying training dataset +2024-05-01 17:54:58,065:INFO:Defining folds +2024-05-01 17:54:58,065:INFO:Declaring metric variables +2024-05-01 17:54:58,082:INFO:Importing untrained model +2024-05-01 17:54:58,098:INFO:Extra Trees Regressor Imported successfully +2024-05-01 17:54:58,127:INFO:Starting cross validation +2024-05-01 17:54:58,131:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:55:08,055:INFO:Calculating mean and std +2024-05-01 17:55:08,061:INFO:Creating metrics dataframe +2024-05-01 17:55:08,074:INFO:Uploading results into container +2024-05-01 17:55:08,076:INFO:Uploading model into container now +2024-05-01 17:55:08,078:INFO:_master_model_container: 14 +2024-05-01 17:55:08,078:INFO:_display_container: 2 +2024-05-01 17:55:08,079:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=342) +2024-05-01 17:55:08,080:INFO:create_model() successfully completed...................................... +2024-05-01 17:55:08,483:INFO:SubProcess create_model() end ================================== +2024-05-01 17:55:08,483:INFO:Creating metrics dataframe +2024-05-01 17:55:08,519:INFO:Initializing AdaBoost Regressor +2024-05-01 17:55:08,519:INFO:Total runtime is 0.9619365453720093 minutes +2024-05-01 17:55:08,528:INFO:SubProcess create_model() called ================================== +2024-05-01 17:55:08,529:INFO:Initializing create_model() +2024-05-01 17:55:08,529:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:55:08,530:INFO:Checking exceptions +2024-05-01 17:55:08,530:INFO:Importing libraries +2024-05-01 17:55:08,530:INFO:Copying training dataset +2024-05-01 17:55:08,544:INFO:Defining folds +2024-05-01 17:55:08,544:INFO:Declaring metric variables +2024-05-01 17:55:08,560:INFO:Importing untrained model +2024-05-01 17:55:08,572:INFO:AdaBoost Regressor Imported successfully +2024-05-01 17:55:08,591:INFO:Starting cross validation +2024-05-01 17:55:08,595:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:55:10,731:INFO:Calculating mean and std +2024-05-01 17:55:10,751:INFO:Creating metrics dataframe +2024-05-01 17:55:10,765:INFO:Uploading results into container +2024-05-01 17:55:10,767:INFO:Uploading model into container now +2024-05-01 17:55:10,769:INFO:_master_model_container: 15 +2024-05-01 17:55:10,769:INFO:_display_container: 2 +2024-05-01 17:55:10,770:INFO:AdaBoostRegressor(random_state=342) +2024-05-01 17:55:10,770:INFO:create_model() successfully completed...................................... +2024-05-01 17:55:11,147:INFO:SubProcess create_model() end ================================== +2024-05-01 17:55:11,147:INFO:Creating metrics dataframe +2024-05-01 17:55:11,201:INFO:Initializing Gradient Boosting Regressor +2024-05-01 17:55:11,202:INFO:Total runtime is 1.0066540519396465 minutes +2024-05-01 17:55:11,219:INFO:SubProcess create_model() called ================================== +2024-05-01 17:55:11,220:INFO:Initializing create_model() +2024-05-01 17:55:11,220:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:55:11,220:INFO:Checking exceptions +2024-05-01 17:55:11,220:INFO:Importing libraries +2024-05-01 17:55:11,221:INFO:Copying training dataset +2024-05-01 17:55:11,244:INFO:Defining folds +2024-05-01 17:55:11,244:INFO:Declaring metric variables +2024-05-01 17:55:11,262:INFO:Importing untrained model +2024-05-01 17:55:11,276:INFO:Gradient Boosting Regressor Imported successfully +2024-05-01 17:55:11,302:INFO:Starting cross validation +2024-05-01 17:55:11,306:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:55:17,143:INFO:Calculating mean and std +2024-05-01 17:55:17,164:INFO:Creating metrics dataframe +2024-05-01 17:55:17,182:INFO:Uploading results into container +2024-05-01 17:55:17,184:INFO:Uploading model into container now +2024-05-01 17:55:17,185:INFO:_master_model_container: 16 +2024-05-01 17:55:17,186:INFO:_display_container: 2 +2024-05-01 17:55:17,187:INFO:GradientBoostingRegressor(random_state=342) +2024-05-01 17:55:17,188:INFO:create_model() successfully completed...................................... +2024-05-01 17:55:17,685:INFO:SubProcess create_model() end ================================== +2024-05-01 17:55:17,685:INFO:Creating metrics dataframe +2024-05-01 17:55:17,732:INFO:Initializing Extreme Gradient Boosting +2024-05-01 17:55:17,732:INFO:Total runtime is 1.1154787778854371 minutes +2024-05-01 17:55:17,746:INFO:SubProcess create_model() called ================================== +2024-05-01 17:55:17,747:INFO:Initializing create_model() +2024-05-01 17:55:17,747:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:55:17,748:INFO:Checking exceptions +2024-05-01 17:55:17,748:INFO:Importing libraries +2024-05-01 17:55:17,748:INFO:Copying training dataset +2024-05-01 17:55:17,765:INFO:Defining folds +2024-05-01 17:55:17,766:INFO:Declaring metric variables +2024-05-01 17:55:17,779:INFO:Importing untrained model +2024-05-01 17:55:17,793:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 17:55:17,812:INFO:Starting cross validation +2024-05-01 17:55:17,815:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:55:24,533:INFO:Calculating mean and std +2024-05-01 17:55:24,536:INFO:Creating metrics dataframe +2024-05-01 17:55:24,548:INFO:Uploading results into container +2024-05-01 17:55:24,551:INFO:Uploading model into container now +2024-05-01 17:55:24,552:INFO:_master_model_container: 17 +2024-05-01 17:55:24,553:INFO:_display_container: 2 +2024-05-01 17:55:24,556: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, predictor=None, random_state=342, + reg_alpha=None, reg_lambda=None, ...) +2024-05-01 17:55:24,557:INFO:create_model() successfully completed...................................... +2024-05-01 17:55:24,886:INFO:SubProcess create_model() end ================================== +2024-05-01 17:55:24,887:INFO:Creating metrics dataframe +2024-05-01 17:55:24,947:INFO:Initializing Light Gradient Boosting Machine +2024-05-01 17:55:24,948:INFO:Total runtime is 1.2357511520385744 minutes +2024-05-01 17:55:24,961:INFO:SubProcess create_model() called ================================== +2024-05-01 17:55:24,962:INFO:Initializing create_model() +2024-05-01 17:55:24,962:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:55:24,962:INFO:Checking exceptions +2024-05-01 17:55:24,963:INFO:Importing libraries +2024-05-01 17:55:24,963:INFO:Copying training dataset +2024-05-01 17:55:24,976:INFO:Defining folds +2024-05-01 17:55:24,976:INFO:Declaring metric variables +2024-05-01 17:55:24,993:INFO:Importing untrained model +2024-05-01 17:55:25,004:INFO:Light Gradient Boosting Machine Imported successfully +2024-05-01 17:55:25,024:INFO:Starting cross validation +2024-05-01 17:55:25,029:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:55:27,408:INFO:Calculating mean and std +2024-05-01 17:55:27,412:INFO:Creating metrics dataframe +2024-05-01 17:55:27,422:INFO:Uploading results into container +2024-05-01 17:55:27,426:INFO:Uploading model into container now +2024-05-01 17:55:27,428:INFO:_master_model_container: 18 +2024-05-01 17:55:27,428:INFO:_display_container: 2 +2024-05-01 17:55:27,429:INFO:LGBMRegressor(n_jobs=-1, random_state=342) +2024-05-01 17:55:27,429:INFO:create_model() successfully completed...................................... +2024-05-01 17:55:27,757:INFO:SubProcess create_model() end ================================== +2024-05-01 17:55:27,757:INFO:Creating metrics dataframe +2024-05-01 17:55:27,817:INFO:Initializing Dummy Regressor +2024-05-01 17:55:27,817:INFO:Total runtime is 1.283573623498281 minutes +2024-05-01 17:55:27,830:INFO:SubProcess create_model() called ================================== +2024-05-01 17:55:27,831:INFO:Initializing create_model() +2024-05-01 17:55:27,831:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:55:27,831:INFO:Checking exceptions +2024-05-01 17:55:27,832:INFO:Importing libraries +2024-05-01 17:55:27,832:INFO:Copying training dataset +2024-05-01 17:55:27,848:INFO:Defining folds +2024-05-01 17:55:27,848:INFO:Declaring metric variables +2024-05-01 17:55:27,864:INFO:Importing untrained model +2024-05-01 17:55:27,883:INFO:Dummy Regressor Imported successfully +2024-05-01 17:55:27,912:INFO:Starting cross validation +2024-05-01 17:55:27,916:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:55:28,218:INFO:Calculating mean and std +2024-05-01 17:55:28,221:INFO:Creating metrics dataframe +2024-05-01 17:55:28,237:INFO:Uploading results into container +2024-05-01 17:55:28,238:INFO:Uploading model into container now +2024-05-01 17:55:28,239:INFO:_master_model_container: 19 +2024-05-01 17:55:28,239:INFO:_display_container: 2 +2024-05-01 17:55:28,239:INFO:DummyRegressor() +2024-05-01 17:55:28,240:INFO:create_model() successfully completed...................................... +2024-05-01 17:55:28,611:INFO:SubProcess create_model() end ================================== +2024-05-01 17:55:28,612:INFO:Creating metrics dataframe +2024-05-01 17:55:28,714:INFO:Initializing create_model() +2024-05-01 17:55:28,714:INFO:create_model(self=, 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, predictor=None, random_state=342, + reg_alpha=None, reg_lambda=None, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:55:28,715:INFO:Checking exceptions +2024-05-01 17:55:28,720:INFO:Importing libraries +2024-05-01 17:55:28,721:INFO:Copying training dataset +2024-05-01 17:55:28,739:INFO:Defining folds +2024-05-01 17:55:28,739:INFO:Declaring metric variables +2024-05-01 17:55:28,741:INFO:Importing untrained model +2024-05-01 17:55:28,741:INFO:Declaring custom model +2024-05-01 17:55:28,747:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 17:55:28,750:INFO:Cross validation set to False +2024-05-01 17:55:28,750:INFO:Fitting Model +2024-05-01 17:55:29,804: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, predictor='auto', random_state=342, + reg_alpha=0, reg_lambda=1, ...) +2024-05-01 17:55:29,804:INFO:create_model() successfully completed...................................... +2024-05-01 17:55:30,337:INFO:_master_model_container: 19 +2024-05-01 17:55:30,338:INFO:_display_container: 2 +2024-05-01 17:55:30,348: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, predictor='auto', random_state=342, + reg_alpha=0, reg_lambda=1, ...) +2024-05-01 17:55:30,349:INFO:compare_models() successfully completed...................................... +2024-05-01 17:55:30,474:INFO:Initializing create_model() +2024-05-01 17:55:30,475:INFO:create_model(self=, estimator=xgboost, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 17:55:30,475:INFO:Checking exceptions +2024-05-01 17:55:30,528:INFO:Importing libraries +2024-05-01 17:55:30,530:INFO:Copying training dataset +2024-05-01 17:55:30,554:INFO:Defining folds +2024-05-01 17:55:30,554:INFO:Declaring metric variables +2024-05-01 17:55:30,590:INFO:Importing untrained model +2024-05-01 17:55:30,621:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 17:55:30,647:INFO:Starting cross validation +2024-05-01 17:55:30,650:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 17:55:36,975:INFO:Calculating mean and std +2024-05-01 17:55:36,978:INFO:Creating metrics dataframe +2024-05-01 17:55:36,996:INFO:Finalizing model +2024-05-01 17:55:37,992:INFO:Uploading results into container +2024-05-01 17:55:37,996:INFO:Uploading model into container now +2024-05-01 17:55:38,036:INFO:_master_model_container: 20 +2024-05-01 17:55:38,037:INFO:_display_container: 3 +2024-05-01 17:55:38,050: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, predictor='auto', random_state=342, + reg_alpha=0, reg_lambda=1, ...) +2024-05-01 17:55:38,050:INFO:create_model() successfully completed...................................... +2024-05-01 17:55:38,505:INFO:Initializing evaluate_model() +2024-05-01 17:55:38,505:INFO:evaluate_model(self=, estimator=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, predictor='auto', random_state=342, + reg_alpha=0, reg_lambda=1, ...), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) +2024-05-01 17:55:38,545:INFO:Initializing plot_model() +2024-05-01 17:55:38,545:INFO:plot_model(self=, estimator=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, predictor='auto', random_state=342, + reg_alpha=0, reg_lambda=1, ...), plot=pipeline, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-05-01 17:55:38,546:INFO:Checking exceptions +2024-05-01 17:55:38,567:INFO:Preloading libraries +2024-05-01 17:55:38,618:INFO:Copying training dataset +2024-05-01 17:55:38,619:INFO:Plot type: pipeline +2024-05-01 17:55:38,968:INFO:Visual Rendered Successfully +2024-05-01 17:55:39,290:INFO:plot_model() successfully completed...................................... +2024-05-01 17:55:39,391:INFO:Initializing tune_model() +2024-05-01 17:55:39,391:INFO:tune_model(self=, estimator=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, predictor='auto', random_state=342, + reg_alpha=0, reg_lambda=1, ...), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=optuna, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-05-01 17:55:39,391:INFO:Checking exceptions +2024-05-01 17:55:39,391:INFO:Soft dependency imported: optuna: 3.6.1 +2024-05-01 17:55:42,450:INFO:Copying training dataset +2024-05-01 17:55:42,531:INFO:Checking base model +2024-05-01 17:55:42,534:INFO:Base model : Extreme Gradient Boosting +2024-05-01 17:55:42,639:INFO:Declaring metric variables +2024-05-01 17:55:42,681:INFO:Defining Hyperparameters +2024-05-01 17:55:44,856:INFO:Tuning with n_jobs=-1 +2024-05-01 17:55:45,047:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\optuna\samplers\_tpe\sampler.py:319: ExperimentalWarning: ``multivariate`` option is an experimental feature. The interface can change in the future. + warnings.warn( + +2024-05-01 17:55:45,048:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\optuna\samplers\_tpe\sampler.py:338: ExperimentalWarning: ``constant_liar`` option is an experimental feature. The interface can change in the future. + warnings.warn( + +2024-05-01 17:55:45,054:INFO:Initializing optuna.integration.OptunaSearchCV +2024-05-01 17:55:45,293:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:2458: ExperimentalWarning: OptunaSearchCV is experimental (supported from v0.17.0). The interface can change in the future. + model_grid = optuna.integration.OptunaSearchCV( # type: ignore + +2024-05-01 18:03:45,183:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 18:03:45,185:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 18:03:45,185:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 18:03:45,186:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 18:03:48,511:INFO:PyCaret RegressionExperiment +2024-05-01 18:03:48,512:INFO:Logging name: reg-default-name +2024-05-01 18:03:48,513:INFO:ML Usecase: MLUsecase.REGRESSION +2024-05-01 18:03:48,514:INFO:version 3.3.0 +2024-05-01 18:03:48,515:INFO:Initializing setup() +2024-05-01 18:03:48,515:INFO:self.USI: b442 +2024-05-01 18:03:48,516:INFO:self._variable_keys: {'gpu_param', 'n_jobs_param', 'X', 'y', 'html_param', 'exp_name_log', 'logging_param', 'X_train', 'y_train', 'pipeline', 'memory', 'idx', 'fold_shuffle_param', 'fold_groups_param', 'seed', 'y_test', 'fold_generator', 'gpu_n_jobs_param', '_available_plots', 'USI', 'log_plots_param', 'transform_target_param', '_ml_usecase', 'data', 'X_test', 'target_param', 'exp_id'} +2024-05-01 18:03:48,516:INFO:Checking environment +2024-05-01 18:03:48,517:INFO:python_version: 3.11.0 +2024-05-01 18:03:48,517:INFO:python_build: ('main', 'Oct 24 2022 18:26:48') +2024-05-01 18:03:48,518:INFO:machine: AMD64 +2024-05-01 18:03:48,518:INFO:platform: Windows-10-10.0.22000-SP0 +2024-05-01 18:03:48,713:INFO:Memory: svmem(total=8467492864, available=2323566592, percent=72.6, used=6143926272, free=2323566592) +2024-05-01 18:03:48,715:INFO:Physical Core: 2 +2024-05-01 18:03:48,717:INFO:Logical Core: 4 +2024-05-01 18:03:48,718:INFO:Checking libraries +2024-05-01 18:03:48,718:INFO:System: +2024-05-01 18:03:48,719:INFO: python: 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] +2024-05-01 18:03:48,720:INFO:executable: c:\Users\Jason\Documents\DataB3\.venv\Scripts\python.exe +2024-05-01 18:03:48,720:INFO: machine: Windows-10-10.0.22000-SP0 +2024-05-01 18:03:48,721:INFO:PyCaret required dependencies: +2024-05-01 18:03:49,040:INFO: pip: 24.0 +2024-05-01 18:03:49,041:INFO: setuptools: 65.5.0 +2024-05-01 18:03:49,042:INFO: pycaret: 3.3.0 +2024-05-01 18:03:49,042:INFO: IPython: 8.23.0 +2024-05-01 18:03:49,043:INFO: ipywidgets: 8.1.2 +2024-05-01 18:03:49,044:INFO: tqdm: 4.66.2 +2024-05-01 18:03:49,045:INFO: numpy: 1.24.4 +2024-05-01 18:03:49,046:INFO: pandas: 1.5.3 +2024-05-01 18:03:49,046:INFO: jinja2: 3.1.3 +2024-05-01 18:03:49,047:INFO: scipy: 1.11.4 +2024-05-01 18:03:49,048:INFO: joblib: 1.3.2 +2024-05-01 18:03:49,048:INFO: sklearn: 1.4.1.post1 +2024-05-01 18:03:49,049:INFO: pyod: 1.1.3 +2024-05-01 18:03:49,058:INFO: imblearn: 0.12.2 +2024-05-01 18:03:49,059:INFO: category_encoders: 2.6.3 +2024-05-01 18:03:49,060:INFO: lightgbm: 4.3.0 +2024-05-01 18:03:49,060:INFO: numba: 0.59.1 +2024-05-01 18:03:49,061:INFO: requests: 2.31.0 +2024-05-01 18:03:49,062:INFO: matplotlib: 3.8.3 +2024-05-01 18:03:49,063:INFO: scikitplot: 0.3.7 +2024-05-01 18:03:49,063:INFO: yellowbrick: 1.5 +2024-05-01 18:03:49,064:INFO: plotly: 5.20.0 +2024-05-01 18:03:49,064:INFO: plotly-resampler: Not installed +2024-05-01 18:03:49,065:INFO: kaleido: 0.2.1 +2024-05-01 18:03:49,066:INFO: schemdraw: 0.15 +2024-05-01 18:03:49,067:INFO: statsmodels: 0.14.1 +2024-05-01 18:03:49,067:INFO: sktime: 0.28.0 +2024-05-01 18:03:49,068:INFO: tbats: 1.1.3 +2024-05-01 18:03:49,068:INFO: pmdarima: 2.0.4 +2024-05-01 18:03:49,069:INFO: psutil: 5.9.8 +2024-05-01 18:03:49,069:INFO: markupsafe: 2.1.5 +2024-05-01 18:03:49,069:INFO: pickle5: Not installed +2024-05-01 18:03:49,070:INFO: cloudpickle: 3.0.0 +2024-05-01 18:03:49,071:INFO: deprecation: 2.1.0 +2024-05-01 18:03:49,072:INFO: xxhash: 3.4.1 +2024-05-01 18:03:49,072:INFO: wurlitzer: Not installed +2024-05-01 18:03:49,073:INFO:PyCaret optional dependencies: +2024-05-01 18:03:49,235:INFO: shap: Not installed +2024-05-01 18:03:49,236:INFO: interpret: Not installed +2024-05-01 18:03:49,236:INFO: umap: Not installed +2024-05-01 18:03:49,237:INFO: ydata_profiling: 4.7.0 +2024-05-01 18:03:49,237:INFO: explainerdashboard: Not installed +2024-05-01 18:03:49,238:INFO: autoviz: Not installed +2024-05-01 18:03:49,238:INFO: fairlearn: Not installed +2024-05-01 18:03:49,239:INFO: deepchecks: Not installed +2024-05-01 18:03:49,239:INFO: xgboost: 1.6.2 +2024-05-01 18:03:49,241:INFO: catboost: Not installed +2024-05-01 18:03:49,242:INFO: kmodes: Not installed +2024-05-01 18:03:49,242:INFO: mlxtend: Not installed +2024-05-01 18:03:49,244:INFO: statsforecast: Not installed +2024-05-01 18:03:49,244:INFO: tune_sklearn: Not installed +2024-05-01 18:03:49,245:INFO: ray: Not installed +2024-05-01 18:03:49,246:INFO: hyperopt: Not installed +2024-05-01 18:03:49,247:INFO: optuna: 3.6.1 +2024-05-01 18:03:49,247:INFO: skopt: Not installed +2024-05-01 18:03:49,248:INFO: mlflow: Not installed +2024-05-01 18:03:49,248:INFO: gradio: Not installed +2024-05-01 18:03:49,249:INFO: fastapi: Not installed +2024-05-01 18:03:49,249:INFO: uvicorn: Not installed +2024-05-01 18:03:49,250:INFO: m2cgen: Not installed +2024-05-01 18:03:49,251:INFO: evidently: Not installed +2024-05-01 18:03:49,252:INFO: fugue: Not installed +2024-05-01 18:03:49,253:INFO: streamlit: 1.33.0 +2024-05-01 18:03:49,253:INFO: prophet: 1.1.5 +2024-05-01 18:03:49,254:INFO:None +2024-05-01 18:03:49,254:INFO:Set up data. +2024-05-01 18:03:49,340:INFO:Set up folding strategy. +2024-05-01 18:03:49,341:INFO:Set up train/test split. +2024-05-01 18:03:49,404:INFO:Set up index. +2024-05-01 18:03:49,406:INFO:Assigning column types. +2024-05-01 18:03:49,454:INFO:Engine successfully changes for model 'lr' to 'sklearn'. +2024-05-01 18:03:49,456:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 18:03:49,549:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 18:03:49,654:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 18:03:50,976:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 18:03:52,012:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 18:03:52,019:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 18:03:53,350:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 18:03:53,356:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None. +2024-05-01 18:03:53,456:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 18:03:53,550:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 18:03:54,946:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 18:03:55,915:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 18:03:55,920:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 18:03:55,970:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 18:03:55,975:INFO:Engine successfully changes for model 'lasso' to 'sklearn'. +2024-05-01 18:03:56,074:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 18:03:56,218:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 18:03:57,316:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 18:03:58,138:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 18:03:58,150:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 18:03:58,191:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 18:03:58,273:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None. +2024-05-01 18:03:58,358:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 18:03:59,470:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 18:04:00,392:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 18:04:00,399:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 18:04:00,449:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 18:04:00,457:INFO:Engine successfully changes for model 'ridge' to 'sklearn'. +2024-05-01 18:04:00,645:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 18:04:01,851:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 18:04:02,707:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 18:04:02,714:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 18:04:02,761:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 18:04:02,938:INFO:Engine for model 'en' has not been set explicitly, hence returning None. +2024-05-01 18:04:04,005:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 18:04:04,754:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 18:04:04,761:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 18:04:04,795:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 18:04:04,799:INFO:Engine successfully changes for model 'en' to 'sklearn'. +2024-05-01 18:04:05,730:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 18:04:06,494:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 18:04:06,497:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 18:04:06,536:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 18:04:07,488:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 18:04:08,104:INFO:Engine for model 'knn' has not been set explicitly, hence returning None. +2024-05-01 18:04:08,114:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 18:04:08,157:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 18:04:08,159:INFO:Engine successfully changes for model 'knn' to 'sklearn'. +2024-05-01 18:04:09,066:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 18:04:09,747:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 18:04:09,802:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 18:04:10,770:INFO:Engine for model 'svm' has not been set explicitly, hence returning None. +2024-05-01 18:04:11,439:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 18:04:11,473:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 18:04:11,474:INFO:Engine successfully changes for model 'svm' to 'sklearn'. +2024-05-01 18:04:13,083:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 18:04:13,136:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 18:04:14,775:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 18:04:14,808:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 18:04:14,818:INFO:Preparing preprocessing pipeline... +2024-05-01 18:04:14,818:INFO:Set up simple imputation. +2024-05-01 18:04:14,819:INFO:Set up feature normalization. +2024-05-01 18:04:15,030:INFO:Finished creating preprocessing pipeline. +2024-05-01 18:04:15,141:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\Jason\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWrapper(transformer=MinMaxScaler()))]) +2024-05-01 18:04:15,142:INFO:Creating final display dataframe. +2024-05-01 18:04:16,499:INFO:Setup _display_container: Description Value +0 Session id 8701 +1 Target Daily_Sales +2 Target type Regression +3 Original data shape (6435, 10) +4 Transformed data shape (6435, 10) +5 Transformed train set shape (4504, 10) +6 Transformed test set shape (1931, 10) +7 Numeric features 9 +8 Preprocess True +9 Imputation type simple +10 Numeric imputation mean +11 Categorical imputation mode +12 Normalize True +13 Normalize method minmax +14 Fold Generator KFold +15 Fold Number 10 +16 CPU Jobs -1 +17 Use GPU False +18 Log Experiment False +19 Experiment Name reg-default-name +20 USI b442 +2024-05-01 18:04:18,916:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 18:04:18,960:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 18:04:20,656:INFO:Soft dependency imported: xgboost: 1.6.2 +2024-05-01 18:04:20,689:WARNING: +'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install. +Alternately, you can install this by running `pip install pycaret[models]` +2024-05-01 18:04:20,714:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\metrics.py:51: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour. + warnings.warn( + +2024-05-01 18:04:20,715:INFO:setup() successfully completed in 32.47s............... +2024-05-01 18:04:20,907:INFO:Initializing compare_models() +2024-05-01 18:04:20,907:INFO:compare_models(self=, include=None, exclude=None, fold=None, round=4, cross_validation=True, sort=R2, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': , 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'R2', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': }) +2024-05-01 18:04:20,908:INFO:Checking exceptions +2024-05-01 18:04:20,925:INFO:Preparing display monitor +2024-05-01 18:04:21,475:INFO:Initializing Linear Regression +2024-05-01 18:04:21,476:INFO:Total runtime is 1.662572224934896e-05 minutes +2024-05-01 18:04:21,588:INFO:SubProcess create_model() called ================================== +2024-05-01 18:04:21,591:INFO:Initializing create_model() +2024-05-01 18:04:21,593:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:04:21,593:INFO:Checking exceptions +2024-05-01 18:04:21,594:INFO:Importing libraries +2024-05-01 18:04:21,595:INFO:Copying training dataset +2024-05-01 18:04:21,660:INFO:Defining folds +2024-05-01 18:04:21,661:INFO:Declaring metric variables +2024-05-01 18:04:21,702:INFO:Importing untrained model +2024-05-01 18:04:21,744:INFO:Linear Regression Imported successfully +2024-05-01 18:04:21,847:INFO:Starting cross validation +2024-05-01 18:04:21,946:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:05:05,511:INFO:Calculating mean and std +2024-05-01 18:05:05,528:INFO:Creating metrics dataframe +2024-05-01 18:05:05,591:INFO:Uploading results into container +2024-05-01 18:05:05,596:INFO:Uploading model into container now +2024-05-01 18:05:05,599:INFO:_master_model_container: 1 +2024-05-01 18:05:05,600:INFO:_display_container: 2 +2024-05-01 18:05:05,602:INFO:LinearRegression(n_jobs=-1) +2024-05-01 18:05:05,603:INFO:create_model() successfully completed...................................... +2024-05-01 18:05:06,014:INFO:SubProcess create_model() end ================================== +2024-05-01 18:05:06,015:INFO:Creating metrics dataframe +2024-05-01 18:05:06,109:INFO:Initializing Lasso Regression +2024-05-01 18:05:06,110:INFO:Total runtime is 0.7439212759335836 minutes +2024-05-01 18:05:06,143:INFO:SubProcess create_model() called ================================== +2024-05-01 18:05:06,146:INFO:Initializing create_model() +2024-05-01 18:05:06,152:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:05:06,153:INFO:Checking exceptions +2024-05-01 18:05:06,153:INFO:Importing libraries +2024-05-01 18:05:06,154:INFO:Copying training dataset +2024-05-01 18:05:06,214:INFO:Defining folds +2024-05-01 18:05:06,215:INFO:Declaring metric variables +2024-05-01 18:05:06,250:INFO:Importing untrained model +2024-05-01 18:05:06,289:INFO:Lasso Regression Imported successfully +2024-05-01 18:05:06,358:INFO:Starting cross validation +2024-05-01 18:05:06,372:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:05:07,551:INFO:Calculating mean and std +2024-05-01 18:05:07,565:INFO:Creating metrics dataframe +2024-05-01 18:05:07,602:INFO:Uploading results into container +2024-05-01 18:05:07,612:INFO:Uploading model into container now +2024-05-01 18:05:07,615:INFO:_master_model_container: 2 +2024-05-01 18:05:07,616:INFO:_display_container: 2 +2024-05-01 18:05:07,619:INFO:Lasso(random_state=8701) +2024-05-01 18:05:07,619:INFO:create_model() successfully completed...................................... +2024-05-01 18:05:08,004:INFO:SubProcess create_model() end ================================== +2024-05-01 18:05:08,005:INFO:Creating metrics dataframe +2024-05-01 18:05:08,107:INFO:Initializing Ridge Regression +2024-05-01 18:05:08,108:INFO:Total runtime is 0.7772158145904541 minutes +2024-05-01 18:05:08,141:INFO:SubProcess create_model() called ================================== +2024-05-01 18:05:08,143:INFO:Initializing create_model() +2024-05-01 18:05:08,144:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:05:08,145:INFO:Checking exceptions +2024-05-01 18:05:08,146:INFO:Importing libraries +2024-05-01 18:05:08,147:INFO:Copying training dataset +2024-05-01 18:05:08,202:INFO:Defining folds +2024-05-01 18:05:08,203:INFO:Declaring metric variables +2024-05-01 18:05:08,256:INFO:Importing untrained model +2024-05-01 18:05:08,294:INFO:Ridge Regression Imported successfully +2024-05-01 18:05:08,368:INFO:Starting cross validation +2024-05-01 18:05:08,378:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:05:09,463:INFO:Calculating mean and std +2024-05-01 18:05:09,476:INFO:Creating metrics dataframe +2024-05-01 18:05:09,534:INFO:Uploading results into container +2024-05-01 18:05:09,538:INFO:Uploading model into container now +2024-05-01 18:05:09,549:INFO:_master_model_container: 3 +2024-05-01 18:05:09,550:INFO:_display_container: 2 +2024-05-01 18:05:09,552:INFO:Ridge(random_state=8701) +2024-05-01 18:05:09,552:INFO:create_model() successfully completed...................................... +2024-05-01 18:05:09,922:INFO:SubProcess create_model() end ================================== +2024-05-01 18:05:09,923:INFO:Creating metrics dataframe +2024-05-01 18:05:10,017:INFO:Initializing Elastic Net +2024-05-01 18:05:10,018:INFO:Total runtime is 0.8090362111727396 minutes +2024-05-01 18:05:10,051:INFO:SubProcess create_model() called ================================== +2024-05-01 18:05:10,053:INFO:Initializing create_model() +2024-05-01 18:05:10,055:INFO:create_model(self=, estimator=en, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:05:10,060:INFO:Checking exceptions +2024-05-01 18:05:10,062:INFO:Importing libraries +2024-05-01 18:05:10,063:INFO:Copying training dataset +2024-05-01 18:05:10,123:INFO:Defining folds +2024-05-01 18:05:10,124:INFO:Declaring metric variables +2024-05-01 18:05:10,169:INFO:Importing untrained model +2024-05-01 18:05:10,206:INFO:Elastic Net Imported successfully +2024-05-01 18:05:10,270:INFO:Starting cross validation +2024-05-01 18:05:10,280:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:05:11,426:INFO:Calculating mean and std +2024-05-01 18:05:11,443:INFO:Creating metrics dataframe +2024-05-01 18:05:11,484:INFO:Uploading results into container +2024-05-01 18:05:11,489:INFO:Uploading model into container now +2024-05-01 18:05:11,497:INFO:_master_model_container: 4 +2024-05-01 18:05:11,498:INFO:_display_container: 2 +2024-05-01 18:05:11,501:INFO:ElasticNet(random_state=8701) +2024-05-01 18:05:11,501:INFO:create_model() successfully completed...................................... +2024-05-01 18:05:11,872:INFO:SubProcess create_model() end ================================== +2024-05-01 18:05:11,873:INFO:Creating metrics dataframe +2024-05-01 18:05:11,961:INFO:Initializing Least Angle Regression +2024-05-01 18:05:11,962:INFO:Total runtime is 0.8414560198783874 minutes +2024-05-01 18:05:11,996:INFO:SubProcess create_model() called ================================== +2024-05-01 18:05:11,999:INFO:Initializing create_model() +2024-05-01 18:05:11,999:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:05:12,000:INFO:Checking exceptions +2024-05-01 18:05:12,001:INFO:Importing libraries +2024-05-01 18:05:12,002:INFO:Copying training dataset +2024-05-01 18:05:12,068:INFO:Defining folds +2024-05-01 18:05:12,070:INFO:Declaring metric variables +2024-05-01 18:05:12,109:INFO:Importing untrained model +2024-05-01 18:05:12,145:INFO:Least Angle Regression Imported successfully +2024-05-01 18:05:12,224:INFO:Starting cross validation +2024-05-01 18:05:12,233:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:05:13,323:INFO:Calculating mean and std +2024-05-01 18:05:13,335:INFO:Creating metrics dataframe +2024-05-01 18:05:13,393:INFO:Uploading results into container +2024-05-01 18:05:13,397:INFO:Uploading model into container now +2024-05-01 18:05:13,401:INFO:_master_model_container: 5 +2024-05-01 18:05:13,401:INFO:_display_container: 2 +2024-05-01 18:05:13,404:INFO:Lars(random_state=8701) +2024-05-01 18:05:13,404:INFO:create_model() successfully completed...................................... +2024-05-01 18:05:14,193:INFO:SubProcess create_model() end ================================== +2024-05-01 18:05:14,194:INFO:Creating metrics dataframe +2024-05-01 18:05:14,408:INFO:Initializing Lasso Least Angle Regression +2024-05-01 18:05:14,409:INFO:Total runtime is 0.8822346607844034 minutes +2024-05-01 18:05:14,441:INFO:SubProcess create_model() called ================================== +2024-05-01 18:05:14,444:INFO:Initializing create_model() +2024-05-01 18:05:14,446:INFO:create_model(self=, estimator=llar, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:05:14,447:INFO:Checking exceptions +2024-05-01 18:05:14,448:INFO:Importing libraries +2024-05-01 18:05:14,449:INFO:Copying training dataset +2024-05-01 18:05:14,502:INFO:Defining folds +2024-05-01 18:05:14,504:INFO:Declaring metric variables +2024-05-01 18:05:14,549:INFO:Importing untrained model +2024-05-01 18:05:14,591:INFO:Lasso Least Angle Regression Imported successfully +2024-05-01 18:05:14,663:INFO:Starting cross validation +2024-05-01 18:05:14,676:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:05:15,926:INFO:Calculating mean and std +2024-05-01 18:05:15,938:INFO:Creating metrics dataframe +2024-05-01 18:05:15,982:INFO:Uploading results into container +2024-05-01 18:05:15,988:INFO:Uploading model into container now +2024-05-01 18:05:15,990:INFO:_master_model_container: 6 +2024-05-01 18:05:15,991:INFO:_display_container: 2 +2024-05-01 18:05:15,996:INFO:LassoLars(random_state=8701) +2024-05-01 18:05:15,996:INFO:create_model() successfully completed...................................... +2024-05-01 18:05:16,353:INFO:SubProcess create_model() end ================================== +2024-05-01 18:05:16,354:INFO:Creating metrics dataframe +2024-05-01 18:05:16,453:INFO:Initializing Orthogonal Matching Pursuit +2024-05-01 18:05:16,455:INFO:Total runtime is 0.9163117249806721 minutes +2024-05-01 18:05:16,491:INFO:SubProcess create_model() called ================================== +2024-05-01 18:05:16,493:INFO:Initializing create_model() +2024-05-01 18:05:16,494:INFO:create_model(self=, estimator=omp, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:05:16,495:INFO:Checking exceptions +2024-05-01 18:05:16,495:INFO:Importing libraries +2024-05-01 18:05:16,504:INFO:Copying training dataset +2024-05-01 18:05:16,567:INFO:Defining folds +2024-05-01 18:05:16,568:INFO:Declaring metric variables +2024-05-01 18:05:16,626:INFO:Importing untrained model +2024-05-01 18:05:16,785:INFO:Orthogonal Matching Pursuit Imported successfully +2024-05-01 18:05:16,860:INFO:Starting cross validation +2024-05-01 18:05:16,868:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:05:18,123:INFO:Calculating mean and std +2024-05-01 18:05:18,135:INFO:Creating metrics dataframe +2024-05-01 18:05:18,188:INFO:Uploading results into container +2024-05-01 18:05:18,196:INFO:Uploading model into container now +2024-05-01 18:05:18,198:INFO:_master_model_container: 7 +2024-05-01 18:05:18,199:INFO:_display_container: 2 +2024-05-01 18:05:18,201:INFO:OrthogonalMatchingPursuit() +2024-05-01 18:05:18,201:INFO:create_model() successfully completed...................................... +2024-05-01 18:05:18,550:INFO:SubProcess create_model() end ================================== +2024-05-01 18:05:18,551:INFO:Creating metrics dataframe +2024-05-01 18:05:18,670:INFO:Initializing Bayesian Ridge +2024-05-01 18:05:18,671:INFO:Total runtime is 0.9532606403032938 minutes +2024-05-01 18:05:18,700:INFO:SubProcess create_model() called ================================== +2024-05-01 18:05:18,702:INFO:Initializing create_model() +2024-05-01 18:05:18,703:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:05:18,704:INFO:Checking exceptions +2024-05-01 18:05:18,704:INFO:Importing libraries +2024-05-01 18:05:18,705:INFO:Copying training dataset +2024-05-01 18:05:18,763:INFO:Defining folds +2024-05-01 18:05:18,764:INFO:Declaring metric variables +2024-05-01 18:05:18,801:INFO:Importing untrained model +2024-05-01 18:05:18,842:INFO:Bayesian Ridge Imported successfully +2024-05-01 18:05:18,915:INFO:Starting cross validation +2024-05-01 18:05:18,923:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:05:20,060:INFO:Calculating mean and std +2024-05-01 18:05:20,070:INFO:Creating metrics dataframe +2024-05-01 18:05:20,127:INFO:Uploading results into container +2024-05-01 18:05:20,133:INFO:Uploading model into container now +2024-05-01 18:05:20,136:INFO:_master_model_container: 8 +2024-05-01 18:05:20,137:INFO:_display_container: 2 +2024-05-01 18:05:20,139:INFO:BayesianRidge() +2024-05-01 18:05:20,140:INFO:create_model() successfully completed...................................... +2024-05-01 18:05:20,582:INFO:SubProcess create_model() end ================================== +2024-05-01 18:05:20,583:INFO:Creating metrics dataframe +2024-05-01 18:05:20,730:INFO:Initializing Passive Aggressive Regressor +2024-05-01 18:05:20,731:INFO:Total runtime is 0.9875953356424967 minutes +2024-05-01 18:05:20,764:INFO:SubProcess create_model() called ================================== +2024-05-01 18:05:20,767:INFO:Initializing create_model() +2024-05-01 18:05:20,768:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:05:20,768:INFO:Checking exceptions +2024-05-01 18:05:20,769:INFO:Importing libraries +2024-05-01 18:05:20,772:INFO:Copying training dataset +2024-05-01 18:05:20,831:INFO:Defining folds +2024-05-01 18:05:20,833:INFO:Declaring metric variables +2024-05-01 18:05:20,887:INFO:Importing untrained model +2024-05-01 18:05:20,928:INFO:Passive Aggressive Regressor Imported successfully +2024-05-01 18:05:21,022:INFO:Starting cross validation +2024-05-01 18:05:21,031:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:05:26,789:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 18:05:26,800:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 18:05:26,847:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 18:05:26,856:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 18:05:31,969:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 18:05:32,052:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 18:05:32,078:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 18:05:32,164:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 18:05:35,115:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 18:05:35,196:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\sklearn\linear_model\_stochastic_gradient.py:1575: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. + warnings.warn( + +2024-05-01 18:05:35,296:INFO:Calculating mean and std +2024-05-01 18:05:35,311:INFO:Creating metrics dataframe +2024-05-01 18:05:35,352:INFO:Uploading results into container +2024-05-01 18:05:35,356:INFO:Uploading model into container now +2024-05-01 18:05:35,359:INFO:_master_model_container: 9 +2024-05-01 18:05:35,359:INFO:_display_container: 2 +2024-05-01 18:05:35,362:INFO:PassiveAggressiveRegressor(random_state=8701) +2024-05-01 18:05:35,362:INFO:create_model() successfully completed...................................... +2024-05-01 18:05:35,737:INFO:SubProcess create_model() end ================================== +2024-05-01 18:05:35,738:INFO:Creating metrics dataframe +2024-05-01 18:05:35,860:INFO:Initializing Huber Regressor +2024-05-01 18:05:35,861:INFO:Total runtime is 1.239771052201589 minutes +2024-05-01 18:05:35,903:INFO:SubProcess create_model() called ================================== +2024-05-01 18:05:35,905:INFO:Initializing create_model() +2024-05-01 18:05:35,906:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:05:35,907:INFO:Checking exceptions +2024-05-01 18:05:35,907:INFO:Importing libraries +2024-05-01 18:05:35,908:INFO:Copying training dataset +2024-05-01 18:05:35,988:INFO:Defining folds +2024-05-01 18:05:36,001:INFO:Declaring metric variables +2024-05-01 18:05:36,061:INFO:Importing untrained model +2024-05-01 18:05:36,117:INFO:Huber Regressor Imported successfully +2024-05-01 18:05:36,218:INFO:Starting cross validation +2024-05-01 18:05:36,225:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:05:38,138:INFO:Calculating mean and std +2024-05-01 18:05:38,148:INFO:Creating metrics dataframe +2024-05-01 18:05:38,188:INFO:Uploading results into container +2024-05-01 18:05:38,192:INFO:Uploading model into container now +2024-05-01 18:05:38,194:INFO:_master_model_container: 10 +2024-05-01 18:05:38,195:INFO:_display_container: 2 +2024-05-01 18:05:38,197:INFO:HuberRegressor() +2024-05-01 18:05:38,198:INFO:create_model() successfully completed...................................... +2024-05-01 18:05:38,570:INFO:SubProcess create_model() end ================================== +2024-05-01 18:05:38,570:INFO:Creating metrics dataframe +2024-05-01 18:05:38,690:INFO:Initializing K Neighbors Regressor +2024-05-01 18:05:38,691:INFO:Total runtime is 1.2869105060895285 minutes +2024-05-01 18:05:38,734:INFO:SubProcess create_model() called ================================== +2024-05-01 18:05:38,736:INFO:Initializing create_model() +2024-05-01 18:05:38,736:INFO:create_model(self=, estimator=knn, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:05:38,737:INFO:Checking exceptions +2024-05-01 18:05:38,737:INFO:Importing libraries +2024-05-01 18:05:38,738:INFO:Copying training dataset +2024-05-01 18:05:38,818:INFO:Defining folds +2024-05-01 18:05:38,819:INFO:Declaring metric variables +2024-05-01 18:05:38,866:INFO:Importing untrained model +2024-05-01 18:05:38,911:INFO:K Neighbors Regressor Imported successfully +2024-05-01 18:05:38,983:INFO:Starting cross validation +2024-05-01 18:05:38,997:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:05:40,944:INFO:Calculating mean and std +2024-05-01 18:05:40,955:INFO:Creating metrics dataframe +2024-05-01 18:05:41,000:INFO:Uploading results into container +2024-05-01 18:05:41,005:INFO:Uploading model into container now +2024-05-01 18:05:41,008:INFO:_master_model_container: 11 +2024-05-01 18:05:41,008:INFO:_display_container: 2 +2024-05-01 18:05:41,011:INFO:KNeighborsRegressor(n_jobs=-1) +2024-05-01 18:05:41,013:INFO:create_model() successfully completed...................................... +2024-05-01 18:05:41,397:INFO:SubProcess create_model() end ================================== +2024-05-01 18:05:41,398:INFO:Creating metrics dataframe +2024-05-01 18:05:41,538:INFO:Initializing Decision Tree Regressor +2024-05-01 18:05:41,540:INFO:Total runtime is 1.3343938628832501 minutes +2024-05-01 18:05:41,574:INFO:SubProcess create_model() called ================================== +2024-05-01 18:05:41,577:INFO:Initializing create_model() +2024-05-01 18:05:41,578:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:05:41,579:INFO:Checking exceptions +2024-05-01 18:05:41,580:INFO:Importing libraries +2024-05-01 18:05:41,582:INFO:Copying training dataset +2024-05-01 18:05:41,640:INFO:Defining folds +2024-05-01 18:05:41,646:INFO:Declaring metric variables +2024-05-01 18:05:41,687:INFO:Importing untrained model +2024-05-01 18:05:41,756:INFO:Decision Tree Regressor Imported successfully +2024-05-01 18:05:41,867:INFO:Starting cross validation +2024-05-01 18:05:41,881:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:05:44,400:INFO:Calculating mean and std +2024-05-01 18:05:44,417:INFO:Creating metrics dataframe +2024-05-01 18:05:44,458:INFO:Uploading results into container +2024-05-01 18:05:44,465:INFO:Uploading model into container now +2024-05-01 18:05:44,479:INFO:_master_model_container: 12 +2024-05-01 18:05:44,480:INFO:_display_container: 2 +2024-05-01 18:05:44,483:INFO:DecisionTreeRegressor(random_state=8701) +2024-05-01 18:05:44,484:INFO:create_model() successfully completed...................................... +2024-05-01 18:05:44,951:INFO:SubProcess create_model() end ================================== +2024-05-01 18:05:44,952:INFO:Creating metrics dataframe +2024-05-01 18:05:45,106:INFO:Initializing Random Forest Regressor +2024-05-01 18:05:45,107:INFO:Total runtime is 1.393873949845632 minutes +2024-05-01 18:05:45,143:INFO:SubProcess create_model() called ================================== +2024-05-01 18:05:45,145:INFO:Initializing create_model() +2024-05-01 18:05:45,146:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:05:45,147:INFO:Checking exceptions +2024-05-01 18:05:45,149:INFO:Importing libraries +2024-05-01 18:05:45,151:INFO:Copying training dataset +2024-05-01 18:05:45,209:INFO:Defining folds +2024-05-01 18:05:45,210:INFO:Declaring metric variables +2024-05-01 18:05:45,262:INFO:Importing untrained model +2024-05-01 18:05:45,309:INFO:Random Forest Regressor Imported successfully +2024-05-01 18:05:45,418:INFO:Starting cross validation +2024-05-01 18:05:45,427:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:06:55,481:INFO:Calculating mean and std +2024-05-01 18:06:55,495:INFO:Creating metrics dataframe +2024-05-01 18:06:55,525:INFO:Uploading results into container +2024-05-01 18:06:55,528:INFO:Uploading model into container now +2024-05-01 18:06:55,531:INFO:_master_model_container: 13 +2024-05-01 18:06:55,532:INFO:_display_container: 2 +2024-05-01 18:06:55,536:INFO:RandomForestRegressor(n_jobs=-1, random_state=8701) +2024-05-01 18:06:55,537:INFO:create_model() successfully completed...................................... +2024-05-01 18:06:55,930:INFO:SubProcess create_model() end ================================== +2024-05-01 18:06:55,931:INFO:Creating metrics dataframe +2024-05-01 18:06:56,080:INFO:Initializing Extra Trees Regressor +2024-05-01 18:06:56,081:INFO:Total runtime is 2.5767602205276487 minutes +2024-05-01 18:06:56,118:INFO:SubProcess create_model() called ================================== +2024-05-01 18:06:56,121:INFO:Initializing create_model() +2024-05-01 18:06:56,123:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:06:56,124:INFO:Checking exceptions +2024-05-01 18:06:56,124:INFO:Importing libraries +2024-05-01 18:06:56,125:INFO:Copying training dataset +2024-05-01 18:06:56,176:INFO:Defining folds +2024-05-01 18:06:56,177:INFO:Declaring metric variables +2024-05-01 18:06:56,218:INFO:Importing untrained model +2024-05-01 18:06:56,267:INFO:Extra Trees Regressor Imported successfully +2024-05-01 18:06:56,344:INFO:Starting cross validation +2024-05-01 18:06:56,356:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:07:35,089:INFO:Calculating mean and std +2024-05-01 18:07:35,099:INFO:Creating metrics dataframe +2024-05-01 18:07:35,147:INFO:Uploading results into container +2024-05-01 18:07:35,151:INFO:Uploading model into container now +2024-05-01 18:07:35,154:INFO:_master_model_container: 14 +2024-05-01 18:07:35,155:INFO:_display_container: 2 +2024-05-01 18:07:35,157:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=8701) +2024-05-01 18:07:35,158:INFO:create_model() successfully completed...................................... +2024-05-01 18:07:35,588:INFO:SubProcess create_model() end ================================== +2024-05-01 18:07:35,589:INFO:Creating metrics dataframe +2024-05-01 18:07:35,732:INFO:Initializing AdaBoost Regressor +2024-05-01 18:07:35,733:INFO:Total runtime is 3.2376306851704912 minutes +2024-05-01 18:07:35,785:INFO:SubProcess create_model() called ================================== +2024-05-01 18:07:35,787:INFO:Initializing create_model() +2024-05-01 18:07:35,789:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:07:35,790:INFO:Checking exceptions +2024-05-01 18:07:35,790:INFO:Importing libraries +2024-05-01 18:07:35,791:INFO:Copying training dataset +2024-05-01 18:07:35,866:INFO:Defining folds +2024-05-01 18:07:35,867:INFO:Declaring metric variables +2024-05-01 18:07:35,919:INFO:Importing untrained model +2024-05-01 18:07:35,958:INFO:AdaBoost Regressor Imported successfully +2024-05-01 18:07:36,079:INFO:Starting cross validation +2024-05-01 18:07:36,088:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:07:46,481:INFO:Calculating mean and std +2024-05-01 18:07:46,491:INFO:Creating metrics dataframe +2024-05-01 18:07:46,525:INFO:Uploading results into container +2024-05-01 18:07:46,530:INFO:Uploading model into container now +2024-05-01 18:07:46,533:INFO:_master_model_container: 15 +2024-05-01 18:07:46,535:INFO:_display_container: 2 +2024-05-01 18:07:46,538:INFO:AdaBoostRegressor(random_state=8701) +2024-05-01 18:07:46,539:INFO:create_model() successfully completed...................................... +2024-05-01 18:07:46,954:INFO:SubProcess create_model() end ================================== +2024-05-01 18:07:46,955:INFO:Creating metrics dataframe +2024-05-01 18:07:47,087:INFO:Initializing Gradient Boosting Regressor +2024-05-01 18:07:47,088:INFO:Total runtime is 3.426882747809092 minutes +2024-05-01 18:07:47,125:INFO:SubProcess create_model() called ================================== +2024-05-01 18:07:47,127:INFO:Initializing create_model() +2024-05-01 18:07:47,128:INFO:create_model(self=, estimator=gbr, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:07:47,129:INFO:Checking exceptions +2024-05-01 18:07:47,129:INFO:Importing libraries +2024-05-01 18:07:47,130:INFO:Copying training dataset +2024-05-01 18:07:47,191:INFO:Defining folds +2024-05-01 18:07:47,192:INFO:Declaring metric variables +2024-05-01 18:07:47,232:INFO:Importing untrained model +2024-05-01 18:07:47,270:INFO:Gradient Boosting Regressor Imported successfully +2024-05-01 18:07:47,336:INFO:Starting cross validation +2024-05-01 18:07:47,348:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:08:06,451:INFO:Calculating mean and std +2024-05-01 18:08:06,463:INFO:Creating metrics dataframe +2024-05-01 18:08:06,503:INFO:Uploading results into container +2024-05-01 18:08:06,507:INFO:Uploading model into container now +2024-05-01 18:08:06,510:INFO:_master_model_container: 16 +2024-05-01 18:08:06,511:INFO:_display_container: 2 +2024-05-01 18:08:06,515:INFO:GradientBoostingRegressor(random_state=8701) +2024-05-01 18:08:06,515:INFO:create_model() successfully completed...................................... +2024-05-01 18:08:06,875:INFO:SubProcess create_model() end ================================== +2024-05-01 18:08:06,876:INFO:Creating metrics dataframe +2024-05-01 18:08:07,013:INFO:Initializing Extreme Gradient Boosting +2024-05-01 18:08:07,014:INFO:Total runtime is 3.7589842081069946 minutes +2024-05-01 18:08:07,046:INFO:SubProcess create_model() called ================================== +2024-05-01 18:08:07,049:INFO:Initializing create_model() +2024-05-01 18:08:07,050:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:08:07,050:INFO:Checking exceptions +2024-05-01 18:08:07,060:INFO:Importing libraries +2024-05-01 18:08:07,061:INFO:Copying training dataset +2024-05-01 18:08:07,120:INFO:Defining folds +2024-05-01 18:08:07,121:INFO:Declaring metric variables +2024-05-01 18:08:07,164:INFO:Importing untrained model +2024-05-01 18:08:07,209:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 18:08:07,276:INFO:Starting cross validation +2024-05-01 18:08:07,326:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:08:27,614:INFO:Calculating mean and std +2024-05-01 18:08:27,625:INFO:Creating metrics dataframe +2024-05-01 18:08:27,669:INFO:Uploading results into container +2024-05-01 18:08:27,674:INFO:Uploading model into container now +2024-05-01 18:08:27,677:INFO:_master_model_container: 17 +2024-05-01 18:08:27,678:INFO:_display_container: 2 +2024-05-01 18:08:27,687: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, predictor=None, random_state=8701, + reg_alpha=None, reg_lambda=None, ...) +2024-05-01 18:08:27,687:INFO:create_model() successfully completed...................................... +2024-05-01 18:08:28,085:INFO:SubProcess create_model() end ================================== +2024-05-01 18:08:28,086:INFO:Creating metrics dataframe +2024-05-01 18:08:28,249:INFO:Initializing Light Gradient Boosting Machine +2024-05-01 18:08:28,250:INFO:Total runtime is 4.11291739543279 minutes +2024-05-01 18:08:28,291:INFO:SubProcess create_model() called ================================== +2024-05-01 18:08:28,293:INFO:Initializing create_model() +2024-05-01 18:08:28,295:INFO:create_model(self=, 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, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:08:28,296:INFO:Checking exceptions +2024-05-01 18:08:28,296:INFO:Importing libraries +2024-05-01 18:08:28,297:INFO:Copying training dataset +2024-05-01 18:08:28,367:INFO:Defining folds +2024-05-01 18:08:28,368:INFO:Declaring metric variables +2024-05-01 18:08:28,414:INFO:Importing untrained model +2024-05-01 18:08:28,464:INFO:Light Gradient Boosting Machine Imported successfully +2024-05-01 18:08:28,576:INFO:Starting cross validation +2024-05-01 18:08:28,593:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:08:37,930:INFO:Calculating mean and std +2024-05-01 18:08:37,940:INFO:Creating metrics dataframe +2024-05-01 18:08:37,967:INFO:Uploading results into container +2024-05-01 18:08:37,994:INFO:Uploading model into container now +2024-05-01 18:08:37,996:INFO:_master_model_container: 18 +2024-05-01 18:08:37,997:INFO:_display_container: 2 +2024-05-01 18:08:38,000:INFO:LGBMRegressor(n_jobs=-1, random_state=8701) +2024-05-01 18:08:38,001:INFO:create_model() successfully completed...................................... +2024-05-01 18:08:38,365:INFO:SubProcess create_model() end ================================== +2024-05-01 18:08:38,365:INFO:Creating metrics dataframe +2024-05-01 18:08:38,528:INFO:Initializing Dummy Regressor +2024-05-01 18:08:38,529:INFO:Total runtime is 4.284226361910502 minutes +2024-05-01 18:08:38,561:INFO:SubProcess create_model() called ================================== +2024-05-01 18:08:38,563:INFO:Initializing create_model() +2024-05-01 18:08:38,564:INFO:create_model(self=, estimator=dummy, fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:08:38,565:INFO:Checking exceptions +2024-05-01 18:08:38,566:INFO:Importing libraries +2024-05-01 18:08:38,568:INFO:Copying training dataset +2024-05-01 18:08:38,658:INFO:Defining folds +2024-05-01 18:08:38,659:INFO:Declaring metric variables +2024-05-01 18:08:38,701:INFO:Importing untrained model +2024-05-01 18:08:38,818:INFO:Dummy Regressor Imported successfully +2024-05-01 18:08:38,930:INFO:Starting cross validation +2024-05-01 18:08:38,938:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:08:39,998:INFO:Calculating mean and std +2024-05-01 18:08:40,011:INFO:Creating metrics dataframe +2024-05-01 18:08:40,043:INFO:Uploading results into container +2024-05-01 18:08:40,047:INFO:Uploading model into container now +2024-05-01 18:08:40,054:INFO:_master_model_container: 19 +2024-05-01 18:08:40,055:INFO:_display_container: 2 +2024-05-01 18:08:40,056:INFO:DummyRegressor() +2024-05-01 18:08:40,057:INFO:create_model() successfully completed...................................... +2024-05-01 18:08:40,472:INFO:SubProcess create_model() end ================================== +2024-05-01 18:08:40,472:INFO:Creating metrics dataframe +2024-05-01 18:08:40,735:INFO:Initializing create_model() +2024-05-01 18:08:40,736:INFO:create_model(self=, 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, predictor=None, random_state=8701, + reg_alpha=None, reg_lambda=None, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=False, predict=False, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:08:40,736:INFO:Checking exceptions +2024-05-01 18:08:40,752:INFO:Importing libraries +2024-05-01 18:08:40,753:INFO:Copying training dataset +2024-05-01 18:08:40,815:INFO:Defining folds +2024-05-01 18:08:40,816:INFO:Declaring metric variables +2024-05-01 18:08:40,818:INFO:Importing untrained model +2024-05-01 18:08:40,820:INFO:Declaring custom model +2024-05-01 18:08:40,833:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 18:08:40,840:INFO:Cross validation set to False +2024-05-01 18:08:40,841:INFO:Fitting Model +2024-05-01 18:08:48,094: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, predictor='auto', + random_state=8701, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 18:08:48,095:INFO:create_model() successfully completed...................................... +2024-05-01 18:08:49,113:INFO:_master_model_container: 19 +2024-05-01 18:08:49,114:INFO:_display_container: 2 +2024-05-01 18:08:49,139: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, predictor='auto', + random_state=8701, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 18:08:49,141:INFO:compare_models() successfully completed...................................... +2024-05-01 18:08:49,510:INFO:Initializing create_model() +2024-05-01 18:08:49,511:INFO:create_model(self=, estimator=xgboost, fold=None, round=4, cross_validation=True, predict=True, fit_kwargs=None, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=True, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:08:49,512:INFO:Checking exceptions +2024-05-01 18:08:49,847:INFO:Importing libraries +2024-05-01 18:08:49,848:INFO:Copying training dataset +2024-05-01 18:08:50,133:INFO:Defining folds +2024-05-01 18:08:50,134:INFO:Declaring metric variables +2024-05-01 18:08:50,186:INFO:Importing untrained model +2024-05-01 18:08:50,233:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 18:08:50,304:INFO:Starting cross validation +2024-05-01 18:08:50,311:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:09:12,389:INFO:Calculating mean and std +2024-05-01 18:09:12,400:INFO:Creating metrics dataframe +2024-05-01 18:09:12,461:INFO:Finalizing model +2024-05-01 18:09:15,396:INFO:Uploading results into container +2024-05-01 18:09:15,410:INFO:Uploading model into container now +2024-05-01 18:09:15,516:INFO:_master_model_container: 20 +2024-05-01 18:09:15,517:INFO:_display_container: 3 +2024-05-01 18:09:15,550: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, predictor='auto', + random_state=8701, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 18:09:15,551:INFO:create_model() successfully completed...................................... +2024-05-01 18:09:16,191:INFO:Initializing evaluate_model() +2024-05-01 18:09:16,191:INFO:evaluate_model(self=, estimator=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, predictor='auto', + random_state=8701, reg_alpha=0, reg_lambda=1, ...), fold=None, fit_kwargs=None, plot_kwargs=None, feature_name=None, groups=None) +2024-05-01 18:09:16,475:INFO:Initializing plot_model() +2024-05-01 18:09:16,476:INFO:plot_model(self=, estimator=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, predictor='auto', + random_state=8701, reg_alpha=0, reg_lambda=1, ...), plot=pipeline, scale=1, save=False, fold=KFold(n_splits=10, random_state=None, shuffle=False), fit_kwargs={}, plot_kwargs=None, groups=None, feature_name=None, label=False, verbose=False, system=True, display=None, display_format=None) +2024-05-01 18:09:16,477:INFO:Checking exceptions +2024-05-01 18:09:16,636:INFO:Preloading libraries +2024-05-01 18:09:16,867:INFO:Copying training dataset +2024-05-01 18:09:16,887:INFO:Plot type: pipeline +2024-05-01 18:09:19,247:INFO:Visual Rendered Successfully +2024-05-01 18:09:19,606:INFO:plot_model() successfully completed...................................... +2024-05-01 18:09:19,881:INFO:Initializing tune_model() +2024-05-01 18:09:19,881:INFO:tune_model(self=, estimator=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, predictor='auto', + random_state=8701, reg_alpha=0, reg_lambda=1, ...), fold=None, round=4, n_iter=10, custom_grid=None, optimize=R2, custom_scorer=None, search_library=optuna, search_algorithm=None, early_stopping=False, early_stopping_max_iters=10, choose_better=True, fit_kwargs=None, groups=None, return_tuner=False, verbose=True, tuner_verbose=True, return_train_score=False, kwargs={}) +2024-05-01 18:09:19,882:INFO:Checking exceptions +2024-05-01 18:09:19,882:INFO:Soft dependency imported: optuna: 3.6.1 +2024-05-01 18:09:21,115:INFO:Copying training dataset +2024-05-01 18:09:21,146:INFO:Checking base model +2024-05-01 18:09:21,149:INFO:Base model : Extreme Gradient Boosting +2024-05-01 18:09:21,232:INFO:Declaring metric variables +2024-05-01 18:09:21,281:INFO:Defining Hyperparameters +2024-05-01 18:09:21,622:INFO:Tuning with n_jobs=-1 +2024-05-01 18:09:21,629:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\optuna\samplers\_tpe\sampler.py:319: ExperimentalWarning: ``multivariate`` option is an experimental feature. The interface can change in the future. + warnings.warn( + +2024-05-01 18:09:21,630:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\optuna\samplers\_tpe\sampler.py:338: ExperimentalWarning: ``constant_liar`` option is an experimental feature. The interface can change in the future. + warnings.warn( + +2024-05-01 18:09:21,636:INFO:Initializing optuna.integration.OptunaSearchCV +2024-05-01 18:09:21,705:WARNING:c:\Users\Jason\Documents\DataB3\.venv\Lib\site-packages\pycaret\internal\pycaret_experiment\supervised_experiment.py:2458: ExperimentalWarning: OptunaSearchCV is experimental (supported from v0.17.0). The interface can change in the future. + model_grid = optuna.integration.OptunaSearchCV( # type: ignore + +2024-05-01 18:12:05,255:INFO:best_params: {'actual_estimator__learning_rate': 0.03371762975320619, 'actual_estimator__n_estimators': 175, 'actual_estimator__subsample': 0.9809034281361135, 'actual_estimator__max_depth': 9, 'actual_estimator__colsample_bytree': 0.6947364927618733, 'actual_estimator__min_child_weight': 2, 'actual_estimator__reg_alpha': 8.011886918799259e-06, 'actual_estimator__reg_lambda': 2.380919467856936e-06, 'actual_estimator__scale_pos_weight': 27.416348430338704} +2024-05-01 18:12:05,276:INFO:Hyperparameter search completed +2024-05-01 18:12:05,277:INFO:SubProcess create_model() called ================================== +2024-05-01 18:12:05,300:INFO:Initializing create_model() +2024-05-01 18:12:05,300:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=8701, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=True, system=False, add_to_model_list=True, metrics=None, display=, model_only=True, return_train_score=False, error_score=0.0, kwargs={'learning_rate': 0.03371762975320619, 'n_estimators': 175, 'subsample': 0.9809034281361135, 'max_depth': 9, 'colsample_bytree': 0.6947364927618733, 'min_child_weight': 2, 'reg_alpha': 8.011886918799259e-06, 'reg_lambda': 2.380919467856936e-06, 'scale_pos_weight': 27.416348430338704}) +2024-05-01 18:12:05,301:INFO:Checking exceptions +2024-05-01 18:12:05,302:INFO:Importing libraries +2024-05-01 18:12:05,303:INFO:Copying training dataset +2024-05-01 18:12:05,346:INFO:Defining folds +2024-05-01 18:12:05,346:INFO:Declaring metric variables +2024-05-01 18:12:05,377:INFO:Importing untrained model +2024-05-01 18:12:05,378:INFO:Declaring custom model +2024-05-01 18:12:05,440:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 18:12:05,564:INFO:Starting cross validation +2024-05-01 18:12:05,574:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:12:46,462:INFO:Calculating mean and std +2024-05-01 18:12:46,474:INFO:Creating metrics dataframe +2024-05-01 18:12:46,531:INFO:Finalizing model +2024-05-01 18:12:53,739:INFO:Uploading results into container +2024-05-01 18:12:53,745:INFO:Uploading model into container now +2024-05-01 18:12:53,749:INFO:_master_model_container: 21 +2024-05-01 18:12:53,750:INFO:_display_container: 4 +2024-05-01 18:12:53,790:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, + colsample_bytree=0.6947364927618733, 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=0.03371762975320619, + max_bin=256, max_cat_to_onehot=4, max_delta_step=0, max_depth=9, + max_leaves=0, min_child_weight=2, missing=nan, + monotone_constraints='()', n_estimators=175, n_jobs=-1, + num_parallel_tree=1, predictor='auto', random_state=8701, + reg_alpha=8.011886918799259e-06, reg_lambda=2.380919467856936e-06, ...) +2024-05-01 18:12:53,791:INFO:create_model() successfully completed...................................... +2024-05-01 18:12:54,186:INFO:SubProcess create_model() end ================================== +2024-05-01 18:12:54,187:INFO:choose_better activated +2024-05-01 18:12:54,220:INFO:SubProcess create_model() called ================================== +2024-05-01 18:12:54,246:INFO:Initializing create_model() +2024-05-01 18:12:54,248:INFO:create_model(self=, estimator=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, predictor='auto', + random_state=8701, reg_alpha=0, reg_lambda=1, ...), fold=KFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=True, metrics=None, display=None, model_only=True, return_train_score=False, error_score=0.0, kwargs={}) +2024-05-01 18:12:54,249:INFO:Checking exceptions +2024-05-01 18:12:54,262:INFO:Importing libraries +2024-05-01 18:12:54,263:INFO:Copying training dataset +2024-05-01 18:12:54,302:INFO:Defining folds +2024-05-01 18:12:54,302:INFO:Declaring metric variables +2024-05-01 18:12:54,304:INFO:Importing untrained model +2024-05-01 18:12:54,304:INFO:Declaring custom model +2024-05-01 18:12:54,325:INFO:Extreme Gradient Boosting Imported successfully +2024-05-01 18:12:54,327:INFO:Starting cross validation +2024-05-01 18:12:54,335:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1 +2024-05-01 18:13:12,489:INFO:Calculating mean and std +2024-05-01 18:13:12,493:INFO:Creating metrics dataframe +2024-05-01 18:13:12,510:INFO:Finalizing model +2024-05-01 18:13:14,961:INFO:Uploading results into container +2024-05-01 18:13:14,966:INFO:Uploading model into container now +2024-05-01 18:13:14,968:INFO:_master_model_container: 22 +2024-05-01 18:13:14,969:INFO:_display_container: 5 +2024-05-01 18:13:14,994: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, predictor='auto', + random_state=8701, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 18:13:14,995:INFO:create_model() successfully completed...................................... +2024-05-01 18:13:15,338:INFO:SubProcess create_model() end ================================== +2024-05-01 18:13:15,356: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, predictor='auto', + random_state=8701, reg_alpha=0, reg_lambda=1, ...) result for R2 is 0.9757 +2024-05-01 18:13:15,377:INFO:XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None, + colsample_bylevel=1, colsample_bynode=1, + colsample_bytree=0.6947364927618733, 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=0.03371762975320619, + max_bin=256, max_cat_to_onehot=4, max_delta_step=0, max_depth=9, + max_leaves=0, min_child_weight=2, missing=nan, + monotone_constraints='()', n_estimators=175, n_jobs=-1, + num_parallel_tree=1, predictor='auto', random_state=8701, + reg_alpha=8.011886918799259e-06, reg_lambda=2.380919467856936e-06, ...) result for R2 is 0.9605 +2024-05-01 18:13:15,392: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, predictor='auto', + random_state=8701, reg_alpha=0, reg_lambda=1, ...) is best model +2024-05-01 18:13:15,393:INFO:choose_better completed +2024-05-01 18:13:15,395:INFO:Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one). +2024-05-01 18:13:15,505:INFO:_master_model_container: 22 +2024-05-01 18:13:15,506:INFO:_display_container: 4 +2024-05-01 18:13:15,533: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, predictor='auto', + random_state=8701, reg_alpha=0, reg_lambda=1, ...) +2024-05-01 18:13:15,534:INFO:tune_model() successfully completed...................................... +2024-05-01 18:13:17,657:INFO:Initializing predict_model() +2024-05-01 18:13:17,658:INFO:predict_model(self=, estimator=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, predictor='auto', + random_state=8701, reg_alpha=0, reg_lambda=1, ...), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x0000025F210A8720>) +2024-05-01 18:13:17,659:INFO:Checking exceptions +2024-05-01 18:13:17,660:INFO:Preloading libraries +2024-05-01 18:13:17,674:INFO:Set up data. +2024-05-01 18:13:17,774:INFO:Set up index. +2024-05-01 18:45:16,892:INFO:Initializing save_model() +2024-05-01 18:45:16,893:INFO:save_model(model=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, predictor='auto', + random_state=8701, reg_alpha=0, reg_lambda=1, ...), model_name=Walmart, prep_pipe_=Pipeline(memory=FastMemory(location=C:\Users\Jason\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWrapper(transformer=MinMaxScaler()))]), verbose=True, use_case=MLUsecase.REGRESSION, kwargs={}) +2024-05-01 18:45:16,893:INFO:Adding model into prep_pipe +2024-05-01 18:45:17,125:INFO:Walmart.pkl saved in current working directory +2024-05-01 18:45:18,913:INFO:Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]) +2024-05-01 18:45:18,914:INFO:save_model() successfully completed...................................... +2024-05-01 18:45:40,641:INFO:Initializing save_model() +2024-05-01 18:45:40,655:INFO:save_model(model=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, predictor='auto', + random_state=8701, reg_alpha=0, reg_lambda=1, ...), model_name=./models/Walmart, prep_pipe_=Pipeline(memory=FastMemory(location=C:\Users\Jason\AppData\Local\Temp\joblib), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWrapper(transformer=MinMaxScaler()))]), verbose=True, use_case=MLUsecase.REGRESSION, kwargs={}) +2024-05-01 18:45:40,842:INFO:Adding model into prep_pipe +2024-05-01 18:45:42,518:INFO:./models/Walmart.pkl saved in current working directory +2024-05-01 18:45:44,472:INFO:Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]) +2024-05-01 18:45:44,474:INFO:save_model() successfully completed...................................... +2024-05-01 18:49:10,360:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 18:49:10,361:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 18:49:10,362:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 18:49:10,362:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-01 18:50:12,567:INFO:Initializing load_model() +2024-05-01 18:50:12,568:INFO:load_model(model_name=./models/Walmart.pkl, platform=None, authentication=None, verbose=True) +2024-05-01 18:50:29,975:INFO:Initializing load_model() +2024-05-01 18:50:29,976:INFO:load_model(model_name=./models/Walmart.pkl, platform=None, authentication=None, verbose=True) +2024-05-01 18:50:35,759:INFO:Initializing load_model() +2024-05-01 18:50:35,760:INFO:load_model(model_name=./models/Walmart.pkl, platform=None, authentication=None, verbose=True) +2024-05-01 18:50:39,365:INFO:Initializing load_model() +2024-05-01 18:50:39,366:INFO:load_model(model_name=./models/Walmart.pkl, platform=None, authentication=None, verbose=True) +2024-05-01 18:50:44,765:INFO:Initializing load_model() +2024-05-01 18:50:44,766:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:52:13,701:INFO:Initializing load_model() +2024-05-01 18:52:13,702:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:52:59,933:INFO:Initializing load_model() +2024-05-01 18:52:59,934:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:53:00,215:INFO:Initializing predict_model() +2024-05-01 18:53:00,216:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6CF2E0>) +2024-05-01 18:53:00,217:INFO:Checking exceptions +2024-05-01 18:53:00,217:INFO:Preloading libraries +2024-05-01 18:53:00,248:INFO:Set up data. +2024-05-01 18:53:00,322:INFO:Set up index. +2024-05-01 18:53:09,804:INFO:Initializing load_model() +2024-05-01 18:53:09,804:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:53:10,524:INFO:Initializing predict_model() +2024-05-01 18:53:10,526:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D804400>) +2024-05-01 18:53:10,527:INFO:Checking exceptions +2024-05-01 18:53:10,528:INFO:Preloading libraries +2024-05-01 18:53:10,529:INFO:Set up data. +2024-05-01 18:53:10,564:INFO:Set up index. +2024-05-01 18:53:37,266:INFO:Initializing load_model() +2024-05-01 18:53:37,267:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:53:37,528:INFO:Initializing predict_model() +2024-05-01 18:53:37,528:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D577240>) +2024-05-01 18:53:37,529:INFO:Checking exceptions +2024-05-01 18:53:37,530:INFO:Preloading libraries +2024-05-01 18:53:37,531:INFO:Set up data. +2024-05-01 18:53:37,658:INFO:Set up index. +2024-05-01 18:53:40,351:INFO:Initializing load_model() +2024-05-01 18:53:40,352:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:53:40,818:INFO:Initializing predict_model() +2024-05-01 18:53:40,819:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD18E163E0>) +2024-05-01 18:53:40,820:INFO:Checking exceptions +2024-05-01 18:53:40,821:INFO:Preloading libraries +2024-05-01 18:53:40,824:INFO:Set up data. +2024-05-01 18:53:40,859:INFO:Set up index. +2024-05-01 18:53:44,401:INFO:Initializing load_model() +2024-05-01 18:53:44,402:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:53:44,624:INFO:Initializing predict_model() +2024-05-01 18:53:44,625:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D577060>) +2024-05-01 18:53:44,625:INFO:Checking exceptions +2024-05-01 18:53:44,626:INFO:Preloading libraries +2024-05-01 18:53:44,628:INFO:Set up data. +2024-05-01 18:53:44,662:INFO:Set up index. +2024-05-01 18:54:14,211:INFO:Initializing load_model() +2024-05-01 18:54:14,212:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:54:14,547:INFO:Initializing predict_model() +2024-05-01 18:54:14,547:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B5DA0>) +2024-05-01 18:54:14,548:INFO:Checking exceptions +2024-05-01 18:54:14,549:INFO:Preloading libraries +2024-05-01 18:54:14,550:INFO:Set up data. +2024-05-01 18:54:14,595:INFO:Set up index. +2024-05-01 18:54:19,122:INFO:Initializing load_model() +2024-05-01 18:54:19,123:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:54:20,177:INFO:Initializing predict_model() +2024-05-01 18:54:20,178:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B7060>) +2024-05-01 18:54:20,178:INFO:Checking exceptions +2024-05-01 18:54:20,187:INFO:Preloading libraries +2024-05-01 18:54:20,191:INFO:Set up data. +2024-05-01 18:54:20,228:INFO:Set up index. +2024-05-01 18:54:22,747:INFO:Initializing load_model() +2024-05-01 18:54:22,748:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:54:23,167:INFO:Initializing predict_model() +2024-05-01 18:54:23,167:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B6980>) +2024-05-01 18:54:23,168:INFO:Checking exceptions +2024-05-01 18:54:23,168:INFO:Preloading libraries +2024-05-01 18:54:23,170:INFO:Set up data. +2024-05-01 18:54:23,216:INFO:Set up index. +2024-05-01 18:54:27,954:INFO:Initializing load_model() +2024-05-01 18:54:27,955:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:54:28,981:INFO:Initializing predict_model() +2024-05-01 18:54:28,982:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD18EAF6A0>) +2024-05-01 18:54:28,982:INFO:Checking exceptions +2024-05-01 18:54:28,983:INFO:Preloading libraries +2024-05-01 18:54:28,985:INFO:Set up data. +2024-05-01 18:54:29,025:INFO:Set up index. +2024-05-01 18:54:32,680:INFO:Initializing load_model() +2024-05-01 18:54:32,680:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:54:33,641:INFO:Initializing predict_model() +2024-05-01 18:54:33,641:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D4F9120>) +2024-05-01 18:54:33,642:INFO:Checking exceptions +2024-05-01 18:54:33,643:INFO:Preloading libraries +2024-05-01 18:54:33,646:INFO:Set up data. +2024-05-01 18:54:33,734:INFO:Set up index. +2024-05-01 18:54:37,144:INFO:Initializing load_model() +2024-05-01 18:54:37,145:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:54:37,423:INFO:Initializing predict_model() +2024-05-01 18:54:37,424:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD14EFCFE0>) +2024-05-01 18:54:37,425:INFO:Checking exceptions +2024-05-01 18:54:37,425:INFO:Preloading libraries +2024-05-01 18:54:37,427:INFO:Set up data. +2024-05-01 18:54:37,454:INFO:Set up index. +2024-05-01 18:55:00,679:INFO:Initializing load_model() +2024-05-01 18:55:00,680:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:55:01,701:INFO:Initializing predict_model() +2024-05-01 18:55:01,702:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD00C38E00>) +2024-05-01 18:55:01,702:INFO:Checking exceptions +2024-05-01 18:55:01,703:INFO:Preloading libraries +2024-05-01 18:55:01,709:INFO:Set up data. +2024-05-01 18:55:01,892:INFO:Set up index. +2024-05-01 18:56:21,796:WARNING:C:\Users\Jason\Documents\DataB3\.venv\Streamlit\walpa\app.py:241: SyntaxWarning: "is not" with a literal. Did you mean "!="? + if form_data is not null: + +2024-05-01 18:56:21,965:INFO:Initializing load_model() +2024-05-01 18:56:21,966:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:56:22,221:INFO:Initializing predict_model() +2024-05-01 18:56:22,221:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B7D80>) +2024-05-01 18:56:22,222:INFO:Checking exceptions +2024-05-01 18:56:22,222:INFO:Preloading libraries +2024-05-01 18:56:22,224:INFO:Set up data. +2024-05-01 18:56:22,257:INFO:Set up index. +2024-05-01 18:58:57,026:INFO:Initializing load_model() +2024-05-01 18:58:57,026:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:58:57,093:INFO:Initializing predict_model() +2024-05-01 18:58:57,093:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B6DE0>) +2024-05-01 18:58:57,094:INFO:Checking exceptions +2024-05-01 18:58:57,094:INFO:Preloading libraries +2024-05-01 18:58:57,095:INFO:Set up data. +2024-05-01 18:58:57,104:INFO:Set up index. +2024-05-01 18:58:58,893:INFO:Initializing load_model() +2024-05-01 18:58:58,893:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:58:58,958:INFO:Initializing predict_model() +2024-05-01 18:58:58,958:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD192DD1C0>) +2024-05-01 18:58:58,959:INFO:Checking exceptions +2024-05-01 18:58:58,959:INFO:Preloading libraries +2024-05-01 18:58:58,959:INFO:Set up data. +2024-05-01 18:58:58,968:INFO:Set up index. +2024-05-01 18:59:40,981:INFO:Initializing load_model() +2024-05-01 18:59:40,981:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:59:41,057:INFO:Initializing predict_model() +2024-05-01 18:59:41,057:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD195DB920>) +2024-05-01 18:59:41,058:INFO:Checking exceptions +2024-05-01 18:59:41,058:INFO:Preloading libraries +2024-05-01 18:59:41,058:INFO:Set up data. +2024-05-01 18:59:41,067:INFO:Set up index. +2024-05-01 18:59:59,694:INFO:Initializing load_model() +2024-05-01 18:59:59,694:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 18:59:59,852:INFO:Initializing predict_model() +2024-05-01 18:59:59,852:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B65C0>) +2024-05-01 18:59:59,852:INFO:Checking exceptions +2024-05-01 18:59:59,853:INFO:Preloading libraries +2024-05-01 18:59:59,855:INFO:Set up data. +2024-05-01 18:59:59,898:INFO:Set up index. +2024-05-01 19:00:00,614:INFO:Initializing load_model() +2024-05-01 19:00:00,614:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:00,781:INFO:Initializing predict_model() +2024-05-01 19:00:00,782:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD19546200>) +2024-05-01 19:00:00,782:INFO:Checking exceptions +2024-05-01 19:00:00,784:INFO:Preloading libraries +2024-05-01 19:00:00,785:INFO:Set up data. +2024-05-01 19:00:00,803:INFO:Set up index. +2024-05-01 19:00:01,981:INFO:Initializing load_model() +2024-05-01 19:00:01,981:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:02,112:INFO:Initializing predict_model() +2024-05-01 19:00:02,115:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B7BA0>) +2024-05-01 19:00:02,119:INFO:Checking exceptions +2024-05-01 19:00:02,119:INFO:Preloading libraries +2024-05-01 19:00:02,120:INFO:Set up data. +2024-05-01 19:00:02,133:INFO:Set up index. +2024-05-01 19:00:03,224:INFO:Initializing load_model() +2024-05-01 19:00:03,224:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:03,335:INFO:Initializing predict_model() +2024-05-01 19:00:03,337:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD196859E0>) +2024-05-01 19:00:03,338:INFO:Checking exceptions +2024-05-01 19:00:03,339:INFO:Preloading libraries +2024-05-01 19:00:03,340:INFO:Set up data. +2024-05-01 19:00:03,355:INFO:Set up index. +2024-05-01 19:00:05,319:INFO:Initializing load_model() +2024-05-01 19:00:05,320:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:05,417:INFO:Initializing predict_model() +2024-05-01 19:00:05,417:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B6660>) +2024-05-01 19:00:05,417:INFO:Checking exceptions +2024-05-01 19:00:05,418:INFO:Preloading libraries +2024-05-01 19:00:05,419:INFO:Set up data. +2024-05-01 19:00:05,434:INFO:Set up index. +2024-05-01 19:00:05,852:INFO:Initializing load_model() +2024-05-01 19:00:05,852:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:05,952:INFO:Initializing predict_model() +2024-05-01 19:00:05,952:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B60C0>) +2024-05-01 19:00:05,952:INFO:Checking exceptions +2024-05-01 19:00:05,953:INFO:Preloading libraries +2024-05-01 19:00:05,953:INFO:Set up data. +2024-05-01 19:00:05,960:INFO:Set up index. +2024-05-01 19:00:13,383:INFO:Initializing load_model() +2024-05-01 19:00:13,383:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:13,569:INFO:Initializing predict_model() +2024-05-01 19:00:13,569:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B6480>) +2024-05-01 19:00:13,569:INFO:Checking exceptions +2024-05-01 19:00:13,570:INFO:Preloading libraries +2024-05-01 19:00:13,570:INFO:Set up data. +2024-05-01 19:00:13,581:INFO:Set up index. +2024-05-01 19:00:13,999:INFO:Initializing load_model() +2024-05-01 19:00:14,000:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:14,146:INFO:Initializing predict_model() +2024-05-01 19:00:14,162:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1956AD40>) +2024-05-01 19:00:14,163:INFO:Checking exceptions +2024-05-01 19:00:14,163:INFO:Preloading libraries +2024-05-01 19:00:14,177:INFO:Set up data. +2024-05-01 19:00:14,215:INFO:Set up index. +2024-05-01 19:00:15,585:INFO:Initializing load_model() +2024-05-01 19:00:15,585:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:15,682:INFO:Initializing predict_model() +2024-05-01 19:00:15,682:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD19546200>) +2024-05-01 19:00:15,682:INFO:Checking exceptions +2024-05-01 19:00:15,683:INFO:Preloading libraries +2024-05-01 19:00:15,683:INFO:Set up data. +2024-05-01 19:00:15,701:INFO:Set up index. +2024-05-01 19:00:18,568:INFO:Initializing load_model() +2024-05-01 19:00:18,568:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:18,678:INFO:Initializing predict_model() +2024-05-01 19:00:18,678:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD18E16200>) +2024-05-01 19:00:18,678:INFO:Checking exceptions +2024-05-01 19:00:18,678:INFO:Preloading libraries +2024-05-01 19:00:18,679:INFO:Set up data. +2024-05-01 19:00:18,684:INFO:Set up index. +2024-05-01 19:00:19,065:INFO:Initializing load_model() +2024-05-01 19:00:19,065:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:19,173:INFO:Initializing predict_model() +2024-05-01 19:00:19,173:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B7380>) +2024-05-01 19:00:19,174:INFO:Checking exceptions +2024-05-01 19:00:19,174:INFO:Preloading libraries +2024-05-01 19:00:19,175:INFO:Set up data. +2024-05-01 19:00:19,188:INFO:Set up index. +2024-05-01 19:00:19,978:INFO:Initializing load_model() +2024-05-01 19:00:19,979:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:20,080:INFO:Initializing predict_model() +2024-05-01 19:00:20,080:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1935D3A0>) +2024-05-01 19:00:20,081:INFO:Checking exceptions +2024-05-01 19:00:20,081:INFO:Preloading libraries +2024-05-01 19:00:20,082:INFO:Set up data. +2024-05-01 19:00:20,092:INFO:Set up index. +2024-05-01 19:00:20,653:INFO:Initializing load_model() +2024-05-01 19:00:20,653:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:20,749:INFO:Initializing predict_model() +2024-05-01 19:00:20,749:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD195DB9C0>) +2024-05-01 19:00:20,750:INFO:Checking exceptions +2024-05-01 19:00:20,750:INFO:Preloading libraries +2024-05-01 19:00:20,751:INFO:Set up data. +2024-05-01 19:00:20,777:INFO:Set up index. +2024-05-01 19:00:21,178:INFO:Initializing load_model() +2024-05-01 19:00:21,178:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:21,296:INFO:Initializing predict_model() +2024-05-01 19:00:21,296:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B77E0>) +2024-05-01 19:00:21,296:INFO:Checking exceptions +2024-05-01 19:00:21,297:INFO:Preloading libraries +2024-05-01 19:00:21,297:INFO:Set up data. +2024-05-01 19:00:21,312:INFO:Set up index. +2024-05-01 19:00:27,841:INFO:Initializing load_model() +2024-05-01 19:00:27,842:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:27,928:INFO:Initializing predict_model() +2024-05-01 19:00:27,929:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6CF920>) +2024-05-01 19:00:27,930:INFO:Checking exceptions +2024-05-01 19:00:27,930:INFO:Preloading libraries +2024-05-01 19:00:27,931:INFO:Set up data. +2024-05-01 19:00:27,956:INFO:Set up index. +2024-05-01 19:00:28,356:INFO:Initializing load_model() +2024-05-01 19:00:28,356:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:28,432:INFO:Initializing predict_model() +2024-05-01 19:00:28,432:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1956AAC0>) +2024-05-01 19:00:28,432:INFO:Checking exceptions +2024-05-01 19:00:28,432:INFO:Preloading libraries +2024-05-01 19:00:28,433:INFO:Set up data. +2024-05-01 19:00:28,443:INFO:Set up index. +2024-05-01 19:00:29,054:INFO:Initializing load_model() +2024-05-01 19:00:29,054:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:29,168:INFO:Initializing predict_model() +2024-05-01 19:00:29,168:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B7CE0>) +2024-05-01 19:00:29,168:INFO:Checking exceptions +2024-05-01 19:00:29,168:INFO:Preloading libraries +2024-05-01 19:00:29,169:INFO:Set up data. +2024-05-01 19:00:29,196:INFO:Set up index. +2024-05-01 19:00:29,777:INFO:Initializing load_model() +2024-05-01 19:00:29,777:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:29,871:INFO:Initializing predict_model() +2024-05-01 19:00:29,872:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B62A0>) +2024-05-01 19:00:29,872:INFO:Checking exceptions +2024-05-01 19:00:29,872:INFO:Preloading libraries +2024-05-01 19:00:29,872:INFO:Set up data. +2024-05-01 19:00:29,881:INFO:Set up index. +2024-05-01 19:00:33,976:INFO:Initializing load_model() +2024-05-01 19:00:33,976:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:34,045:INFO:Initializing predict_model() +2024-05-01 19:00:34,045:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1CE3BEC0>) +2024-05-01 19:00:34,045:INFO:Checking exceptions +2024-05-01 19:00:34,045:INFO:Preloading libraries +2024-05-01 19:00:34,046:INFO:Set up data. +2024-05-01 19:00:34,070:INFO:Set up index. +2024-05-01 19:00:34,515:INFO:Initializing load_model() +2024-05-01 19:00:34,517:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:34,638:INFO:Initializing predict_model() +2024-05-01 19:00:34,639:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD18E163E0>) +2024-05-01 19:00:34,654:INFO:Checking exceptions +2024-05-01 19:00:34,654:INFO:Preloading libraries +2024-05-01 19:00:34,655:INFO:Set up data. +2024-05-01 19:00:34,688:INFO:Set up index. +2024-05-01 19:00:35,089:INFO:Initializing load_model() +2024-05-01 19:00:35,090:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:35,186:INFO:Initializing predict_model() +2024-05-01 19:00:35,187:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1956A8E0>) +2024-05-01 19:00:35,187:INFO:Checking exceptions +2024-05-01 19:00:35,187:INFO:Preloading libraries +2024-05-01 19:00:35,188:INFO:Set up data. +2024-05-01 19:00:35,202:INFO:Set up index. +2024-05-01 19:00:35,890:INFO:Initializing load_model() +2024-05-01 19:00:35,890:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:35,979:INFO:Initializing predict_model() +2024-05-01 19:00:35,982:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD19546200>) +2024-05-01 19:00:35,986:INFO:Checking exceptions +2024-05-01 19:00:35,986:INFO:Preloading libraries +2024-05-01 19:00:35,987:INFO:Set up data. +2024-05-01 19:00:36,005:INFO:Set up index. +2024-05-01 19:00:36,904:INFO:Initializing load_model() +2024-05-01 19:00:36,904:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:37,000:INFO:Initializing predict_model() +2024-05-01 19:00:37,002:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D576E80>) +2024-05-01 19:00:37,003:INFO:Checking exceptions +2024-05-01 19:00:37,003:INFO:Preloading libraries +2024-05-01 19:00:37,006:INFO:Set up data. +2024-05-01 19:00:37,015:INFO:Set up index. +2024-05-01 19:00:37,414:INFO:Initializing load_model() +2024-05-01 19:00:37,414:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:37,535:INFO:Initializing predict_model() +2024-05-01 19:00:37,536:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD195DB920>) +2024-05-01 19:00:37,536:INFO:Checking exceptions +2024-05-01 19:00:37,537:INFO:Preloading libraries +2024-05-01 19:00:37,537:INFO:Set up data. +2024-05-01 19:00:37,546:INFO:Set up index. +2024-05-01 19:00:38,728:INFO:Initializing load_model() +2024-05-01 19:00:38,728:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:38,808:INFO:Initializing predict_model() +2024-05-01 19:00:38,808:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD19546200>) +2024-05-01 19:00:38,809:INFO:Checking exceptions +2024-05-01 19:00:38,809:INFO:Preloading libraries +2024-05-01 19:00:38,810:INFO:Set up data. +2024-05-01 19:00:38,829:INFO:Set up index. +2024-05-01 19:00:39,304:INFO:Initializing load_model() +2024-05-01 19:00:39,304:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:39,407:INFO:Initializing predict_model() +2024-05-01 19:00:39,407:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D575800>) +2024-05-01 19:00:39,412:INFO:Checking exceptions +2024-05-01 19:00:39,413:INFO:Preloading libraries +2024-05-01 19:00:39,413:INFO:Set up data. +2024-05-01 19:00:39,431:INFO:Set up index. +2024-05-01 19:00:40,247:INFO:Initializing load_model() +2024-05-01 19:00:40,247:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:40,322:INFO:Initializing predict_model() +2024-05-01 19:00:40,323:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D5767A0>) +2024-05-01 19:00:40,323:INFO:Checking exceptions +2024-05-01 19:00:40,323:INFO:Preloading libraries +2024-05-01 19:00:40,323:INFO:Set up data. +2024-05-01 19:00:40,346:INFO:Set up index. +2024-05-01 19:00:40,799:INFO:Initializing load_model() +2024-05-01 19:00:40,799:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:40,909:INFO:Initializing predict_model() +2024-05-01 19:00:40,909:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D577EC0>) +2024-05-01 19:00:40,909:INFO:Checking exceptions +2024-05-01 19:00:40,909:INFO:Preloading libraries +2024-05-01 19:00:40,910:INFO:Set up data. +2024-05-01 19:00:40,916:INFO:Set up index. +2024-05-01 19:00:43,450:INFO:Initializing load_model() +2024-05-01 19:00:43,450:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:43,551:INFO:Initializing predict_model() +2024-05-01 19:00:43,551:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD19546200>) +2024-05-01 19:00:43,552:INFO:Checking exceptions +2024-05-01 19:00:43,552:INFO:Preloading libraries +2024-05-01 19:00:43,554:INFO:Set up data. +2024-05-01 19:00:43,565:INFO:Set up index. +2024-05-01 19:00:44,200:INFO:Initializing load_model() +2024-05-01 19:00:44,200:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:44,315:INFO:Initializing predict_model() +2024-05-01 19:00:44,315:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD00C38E00>) +2024-05-01 19:00:44,315:INFO:Checking exceptions +2024-05-01 19:00:44,315:INFO:Preloading libraries +2024-05-01 19:00:44,317:INFO:Set up data. +2024-05-01 19:00:44,329:INFO:Set up index. +2024-05-01 19:00:45,018:INFO:Initializing load_model() +2024-05-01 19:00:45,018:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:45,129:INFO:Initializing predict_model() +2024-05-01 19:00:45,129:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D576B60>) +2024-05-01 19:00:45,129:INFO:Checking exceptions +2024-05-01 19:00:45,130:INFO:Preloading libraries +2024-05-01 19:00:45,130:INFO:Set up data. +2024-05-01 19:00:45,173:INFO:Set up index. +2024-05-01 19:00:45,569:INFO:Initializing load_model() +2024-05-01 19:00:45,570:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:45,687:INFO:Initializing predict_model() +2024-05-01 19:00:45,687:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD7EC5C860>) +2024-05-01 19:00:45,687:INFO:Checking exceptions +2024-05-01 19:00:45,688:INFO:Preloading libraries +2024-05-01 19:00:45,688:INFO:Set up data. +2024-05-01 19:00:45,720:INFO:Set up index. +2024-05-01 19:00:46,124:INFO:Initializing load_model() +2024-05-01 19:00:46,124:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:46,235:INFO:Initializing predict_model() +2024-05-01 19:00:46,235:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D577E20>) +2024-05-01 19:00:46,236:INFO:Checking exceptions +2024-05-01 19:00:46,237:INFO:Preloading libraries +2024-05-01 19:00:46,238:INFO:Set up data. +2024-05-01 19:00:46,250:INFO:Set up index. +2024-05-01 19:00:46,663:INFO:Initializing load_model() +2024-05-01 19:00:46,664:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:00:46,772:INFO:Initializing predict_model() +2024-05-01 19:00:46,772:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6CFA60>) +2024-05-01 19:00:46,772:INFO:Checking exceptions +2024-05-01 19:00:46,772:INFO:Preloading libraries +2024-05-01 19:00:46,773:INFO:Set up data. +2024-05-01 19:00:46,784:INFO:Set up index. +2024-05-01 19:01:00,794:INFO:Initializing load_model() +2024-05-01 19:01:00,794:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:01:00,886:INFO:Initializing predict_model() +2024-05-01 19:01:00,887:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD7EC5C860>) +2024-05-01 19:01:00,887:INFO:Checking exceptions +2024-05-01 19:01:00,889:INFO:Preloading libraries +2024-05-01 19:01:00,900:INFO:Set up data. +2024-05-01 19:01:00,911:INFO:Set up index. +2024-05-01 19:01:01,400:INFO:Initializing load_model() +2024-05-01 19:01:01,400:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:01:01,508:INFO:Initializing predict_model() +2024-05-01 19:01:01,508:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD14EFCA40>) +2024-05-01 19:01:01,509:INFO:Checking exceptions +2024-05-01 19:01:01,509:INFO:Preloading libraries +2024-05-01 19:01:01,509:INFO:Set up data. +2024-05-01 19:01:01,520:INFO:Set up index. +2024-05-01 19:01:03,025:INFO:Initializing load_model() +2024-05-01 19:01:03,026:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:01:03,123:INFO:Initializing predict_model() +2024-05-01 19:01:03,123:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D5767A0>) +2024-05-01 19:01:03,123:INFO:Checking exceptions +2024-05-01 19:01:03,123:INFO:Preloading libraries +2024-05-01 19:01:03,126:INFO:Set up data. +2024-05-01 19:01:03,141:INFO:Set up index. +2024-05-01 19:01:07,810:INFO:Initializing load_model() +2024-05-01 19:01:07,810:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:01:07,877:INFO:Initializing predict_model() +2024-05-01 19:01:07,878:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD18E16200>) +2024-05-01 19:01:07,878:INFO:Checking exceptions +2024-05-01 19:01:07,878:INFO:Preloading libraries +2024-05-01 19:01:07,878:INFO:Set up data. +2024-05-01 19:01:07,917:INFO:Set up index. +2024-05-01 19:01:08,777:INFO:Initializing load_model() +2024-05-01 19:01:08,777:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:01:08,883:INFO:Initializing predict_model() +2024-05-01 19:01:08,883:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D575DA0>) +2024-05-01 19:01:08,883:INFO:Checking exceptions +2024-05-01 19:01:08,883:INFO:Preloading libraries +2024-05-01 19:01:08,884:INFO:Set up data. +2024-05-01 19:01:08,916:INFO:Set up index. +2024-05-01 19:01:09,295:INFO:Initializing load_model() +2024-05-01 19:01:09,295:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:01:09,444:INFO:Initializing predict_model() +2024-05-01 19:01:09,444:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D576F20>) +2024-05-01 19:01:09,444:INFO:Checking exceptions +2024-05-01 19:01:09,444:INFO:Preloading libraries +2024-05-01 19:01:09,444:INFO:Set up data. +2024-05-01 19:01:09,450:INFO:Set up index. +2024-05-01 19:11:20,585:INFO:Initializing load_model() +2024-05-01 19:11:20,586:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:11:20,647:INFO:Initializing predict_model() +2024-05-01 19:11:20,647:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B6B60>) +2024-05-01 19:11:20,647:INFO:Checking exceptions +2024-05-01 19:11:20,647:INFO:Preloading libraries +2024-05-01 19:11:20,648:INFO:Set up data. +2024-05-01 19:11:20,659:INFO:Set up index. +2024-05-01 19:12:06,697:INFO:Initializing load_model() +2024-05-01 19:12:06,698:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:12:06,762:INFO:Initializing predict_model() +2024-05-01 19:12:06,762:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D576B60>) +2024-05-01 19:12:06,762:INFO:Checking exceptions +2024-05-01 19:12:06,762:INFO:Preloading libraries +2024-05-01 19:12:06,763:INFO:Set up data. +2024-05-01 19:12:06,772:INFO:Set up index. +2024-05-01 19:12:22,459:INFO:Initializing load_model() +2024-05-01 19:12:22,459:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:12:22,561:INFO:Initializing predict_model() +2024-05-01 19:12:22,562:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD18EACFE0>) +2024-05-01 19:12:22,562:INFO:Checking exceptions +2024-05-01 19:12:22,562:INFO:Preloading libraries +2024-05-01 19:12:22,569:INFO:Set up data. +2024-05-01 19:12:22,614:INFO:Set up index. +2024-05-01 19:12:55,135:INFO:Initializing load_model() +2024-05-01 19:12:55,135:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:12:55,215:INFO:Initializing predict_model() +2024-05-01 19:12:55,215:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D56F420>) +2024-05-01 19:12:55,216:INFO:Checking exceptions +2024-05-01 19:12:55,216:INFO:Preloading libraries +2024-05-01 19:12:55,216:INFO:Set up data. +2024-05-01 19:12:55,228:INFO:Set up index. +2024-05-01 19:13:04,405:INFO:Initializing load_model() +2024-05-01 19:13:04,406:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:13:04,489:INFO:Initializing predict_model() +2024-05-01 19:13:04,489:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD7EC5C860>) +2024-05-01 19:13:04,489:INFO:Checking exceptions +2024-05-01 19:13:04,489:INFO:Preloading libraries +2024-05-01 19:13:04,490:INFO:Set up data. +2024-05-01 19:13:04,502:INFO:Set up index. +2024-05-01 19:13:11,661:INFO:Initializing load_model() +2024-05-01 19:13:11,662:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:13:11,733:INFO:Initializing predict_model() +2024-05-01 19:13:11,734:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B5B20>) +2024-05-01 19:13:11,734:INFO:Checking exceptions +2024-05-01 19:13:11,735:INFO:Preloading libraries +2024-05-01 19:13:11,735:INFO:Set up data. +2024-05-01 19:13:11,750:INFO:Set up index. +2024-05-01 19:13:20,707:INFO:Initializing load_model() +2024-05-01 19:13:20,708:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:13:20,776:INFO:Initializing predict_model() +2024-05-01 19:13:20,777:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD7EC5C860>) +2024-05-01 19:13:20,777:INFO:Checking exceptions +2024-05-01 19:13:20,777:INFO:Preloading libraries +2024-05-01 19:13:20,778:INFO:Set up data. +2024-05-01 19:13:20,787:INFO:Set up index. +2024-05-01 19:13:55,970:INFO:Initializing load_model() +2024-05-01 19:13:55,970:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:13:56,066:INFO:Initializing predict_model() +2024-05-01 19:13:56,066:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1CE3BF60>) +2024-05-01 19:13:56,067:INFO:Checking exceptions +2024-05-01 19:13:56,067:INFO:Preloading libraries +2024-05-01 19:13:56,068:INFO:Set up data. +2024-05-01 19:13:56,089:INFO:Set up index. +2024-05-01 19:14:19,690:INFO:Initializing load_model() +2024-05-01 19:14:19,690:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:14:19,756:INFO:Initializing predict_model() +2024-05-01 19:14:19,756:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D577240>) +2024-05-01 19:14:19,756:INFO:Checking exceptions +2024-05-01 19:14:19,757:INFO:Preloading libraries +2024-05-01 19:14:19,757:INFO:Set up data. +2024-05-01 19:14:19,773:INFO:Set up index. +2024-05-01 19:14:48,747:INFO:Initializing load_model() +2024-05-01 19:14:48,747:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:14:48,846:INFO:Initializing predict_model() +2024-05-01 19:14:48,846:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD14EFEDE0>) +2024-05-01 19:14:48,846:INFO:Checking exceptions +2024-05-01 19:14:48,846:INFO:Preloading libraries +2024-05-01 19:14:48,847:INFO:Set up data. +2024-05-01 19:14:48,920:INFO:Set up index. +2024-05-01 19:15:04,097:INFO:Initializing load_model() +2024-05-01 19:15:04,098:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:15:04,196:INFO:Initializing predict_model() +2024-05-01 19:15:04,196:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B72E0>) +2024-05-01 19:15:04,197:INFO:Checking exceptions +2024-05-01 19:15:04,197:INFO:Preloading libraries +2024-05-01 19:15:04,198:INFO:Set up data. +2024-05-01 19:15:04,212:INFO:Set up index. +2024-05-01 19:18:30,120:INFO:Initializing load_model() +2024-05-01 19:18:30,120:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:18:30,281:INFO:Initializing predict_model() +2024-05-01 19:18:30,285:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D576520>) +2024-05-01 19:18:30,286:INFO:Checking exceptions +2024-05-01 19:18:30,286:INFO:Preloading libraries +2024-05-01 19:18:30,287:INFO:Set up data. +2024-05-01 19:18:30,321:INFO:Set up index. +2024-05-01 19:18:49,547:INFO:Initializing load_model() +2024-05-01 19:18:49,547:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:18:49,745:INFO:Initializing predict_model() +2024-05-01 19:18:49,746:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD14EFCFE0>) +2024-05-01 19:18:49,747:INFO:Checking exceptions +2024-05-01 19:18:49,747:INFO:Preloading libraries +2024-05-01 19:18:49,748:INFO:Set up data. +2024-05-01 19:18:49,764:INFO:Set up index. +2024-05-01 19:22:42,808:INFO:Initializing load_model() +2024-05-01 19:22:42,809:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:22:42,916:INFO:Initializing predict_model() +2024-05-01 19:22:42,916:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD7EC5C860>) +2024-05-01 19:22:42,916:INFO:Checking exceptions +2024-05-01 19:22:42,916:INFO:Preloading libraries +2024-05-01 19:22:42,917:INFO:Set up data. +2024-05-01 19:22:42,948:INFO:Set up index. +2024-05-01 19:23:07,234:INFO:Initializing load_model() +2024-05-01 19:23:07,234:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:23:07,317:INFO:Initializing predict_model() +2024-05-01 19:23:07,318:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B7D80>) +2024-05-01 19:23:07,318:INFO:Checking exceptions +2024-05-01 19:23:07,318:INFO:Preloading libraries +2024-05-01 19:23:07,319:INFO:Set up data. +2024-05-01 19:23:07,328:INFO:Set up index. +2024-05-01 19:23:25,449:INFO:Initializing load_model() +2024-05-01 19:23:25,450:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:23:25,583:INFO:Initializing predict_model() +2024-05-01 19:23:25,583:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B62A0>) +2024-05-01 19:23:25,583:INFO:Checking exceptions +2024-05-01 19:23:25,584:INFO:Preloading libraries +2024-05-01 19:23:25,584:INFO:Set up data. +2024-05-01 19:23:25,650:INFO:Set up index. +2024-05-01 19:24:00,149:INFO:Initializing load_model() +2024-05-01 19:24:00,149:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:24:00,389:INFO:Initializing predict_model() +2024-05-01 19:24:00,389:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D6B7E20>) +2024-05-01 19:24:00,389:INFO:Checking exceptions +2024-05-01 19:24:00,390:INFO:Preloading libraries +2024-05-01 19:24:00,390:INFO:Set up data. +2024-05-01 19:24:00,402:INFO:Set up index. +2024-05-01 19:27:41,202:INFO:Initializing load_model() +2024-05-01 19:27:41,203:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:27:41,409:INFO:Initializing predict_model() +2024-05-01 19:27:41,409:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1D5779C0>) +2024-05-01 19:27:41,409:INFO:Checking exceptions +2024-05-01 19:27:41,410:INFO:Preloading libraries +2024-05-01 19:27:41,411:INFO:Set up data. +2024-05-01 19:27:41,427:INFO:Set up index. +2024-05-01 19:34:42,193:WARNING:C:\Users\Jason\Documents\DataB3\.venv\Streamlit\walpa\app.py:139: UserWarning: color argument of Icon should be one of: {'gray', 'lightgray', 'darkred', 'beige', 'lightred', 'blue', 'white', 'red', 'purple', 'green', 'cadetblue', 'darkgreen', 'lightblue', 'darkblue', 'black', 'pink', 'orange', 'darkpurple', 'lightgreen'}. + folium.Marker([lat, lon], popup=store,icon=folium.Icon(color='dodgerblue', icon='shopping-cart', prefix='fa')).add_to(wmap) + +2024-05-01 19:34:43,533:WARNING:C:\Users\Jason\Documents\DataB3\.venv\Streamlit\walpa\app.py:139: UserWarning: color argument of Icon should be one of: {'gray', 'lightgray', 'darkred', 'beige', 'lightred', 'blue', 'white', 'red', 'purple', 'green', 'cadetblue', 'darkgreen', 'lightblue', 'darkblue', 'black', 'pink', 'orange', 'darkpurple', 'lightgreen'}. + folium.Marker([lat, lon], popup=store,icon=folium.Icon(color='dodgerblue', icon='shopping-cart', prefix='fa')).add_to(wmap) + +2024-05-01 19:34:45,002:WARNING:C:\Users\Jason\Documents\DataB3\.venv\Streamlit\walpa\app.py:139: UserWarning: color argument of Icon should be one of: {'gray', 'lightgray', 'darkred', 'beige', 'lightred', 'blue', 'white', 'red', 'purple', 'green', 'cadetblue', 'darkgreen', 'lightblue', 'darkblue', 'black', 'pink', 'orange', 'darkpurple', 'lightgreen'}. + folium.Marker([lat, lon], popup=store,icon=folium.Icon(color='dodgerblue', icon='shopping-cart', prefix='fa')).add_to(wmap) + +2024-05-01 19:35:57,676:WARNING:C:\Users\Jason\Documents\DataB3\.venv\Streamlit\walpa\app.py:160: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning. + st.write(sns.heatmap(data.corr(),annot=True)) + +2024-05-01 19:47:41,734:INFO:Initializing load_model() +2024-05-01 19:47:41,735:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:47:41,840:INFO:Initializing predict_model() +2024-05-01 19:47:41,841:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1935D3A0>) +2024-05-01 19:47:41,841:INFO:Checking exceptions +2024-05-01 19:47:41,842:INFO:Preloading libraries +2024-05-01 19:47:41,843:INFO:Set up data. +2024-05-01 19:47:41,856:INFO:Set up index. +2024-05-01 19:47:55,786:INFO:Initializing load_model() +2024-05-01 19:47:55,787:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:47:55,869:INFO:Initializing predict_model() +2024-05-01 19:47:55,869:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD195DB920>) +2024-05-01 19:47:55,869:INFO:Checking exceptions +2024-05-01 19:47:55,870:INFO:Preloading libraries +2024-05-01 19:47:55,870:INFO:Set up data. +2024-05-01 19:47:55,883:INFO:Set up index. +2024-05-01 19:54:19,186:INFO:Initializing load_model() +2024-05-01 19:54:19,186:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:54:19,427:INFO:Initializing predict_model() +2024-05-01 19:54:19,427:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1DA8D3A0>) +2024-05-01 19:54:19,427:INFO:Checking exceptions +2024-05-01 19:54:19,427:INFO:Preloading libraries +2024-05-01 19:54:19,428:INFO:Set up data. +2024-05-01 19:54:19,436:INFO:Set up index. +2024-05-01 19:55:29,190:INFO:Initializing load_model() +2024-05-01 19:55:29,190:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 19:55:29,279:INFO:Initializing predict_model() +2024-05-01 19:55:29,279:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1DA8E7A0>) +2024-05-01 19:55:29,280:INFO:Checking exceptions +2024-05-01 19:55:29,280:INFO:Preloading libraries +2024-05-01 19:55:29,280:INFO:Set up data. +2024-05-01 19:55:29,308:INFO:Set up index. +2024-05-01 21:40:09,978:INFO:Initializing load_model() +2024-05-01 21:40:09,979:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-01 21:40:10,219:INFO:Initializing predict_model() +2024-05-01 21:40:10,219:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1E542160>) +2024-05-01 21:40:10,219:INFO:Checking exceptions +2024-05-01 21:40:10,219:INFO:Preloading libraries +2024-05-01 21:40:10,220:INFO:Set up data. +2024-05-01 21:40:10,232:INFO:Set up index. +2024-05-02 15:27:19,272:INFO:Initializing load_model() +2024-05-02 15:27:19,273:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-02 15:27:19,682:INFO:Initializing predict_model() +2024-05-02 15:27:19,682:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1DB5EC00>) +2024-05-02 15:27:19,682:INFO:Checking exceptions +2024-05-02 15:27:19,682:INFO:Preloading libraries +2024-05-02 15:27:19,683:INFO:Set up data. +2024-05-02 15:27:19,696:INFO:Set up index. +2024-05-02 15:30:02,799:INFO:Initializing load_model() +2024-05-02 15:30:02,800:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-02 15:30:03,051:INFO:Initializing predict_model() +2024-05-02 15:30:03,052:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD14EE76A0>) +2024-05-02 15:30:03,052:INFO:Checking exceptions +2024-05-02 15:30:03,053:INFO:Preloading libraries +2024-05-02 15:30:03,054:INFO:Set up data. +2024-05-02 15:30:03,068:INFO:Set up index. +2024-05-02 16:39:35,842:INFO:Initializing load_model() +2024-05-02 16:39:35,843:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-02 16:39:36,107:INFO:Initializing predict_model() +2024-05-02 16:39:36,107:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD1DA8C9A0>) +2024-05-02 16:39:36,138:INFO:Checking exceptions +2024-05-02 16:39:36,138:INFO:Preloading libraries +2024-05-02 16:39:36,140:INFO:Set up data. +2024-05-02 16:39:36,153:INFO:Set up index. +2024-05-02 16:39:49,008:INFO:Initializing load_model() +2024-05-02 16:39:49,008:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-02 16:39:49,156:INFO:Initializing predict_model() +2024-05-02 16:39:49,156:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD14EE4EA0>) +2024-05-02 16:39:49,157:INFO:Checking exceptions +2024-05-02 16:39:49,157:INFO:Preloading libraries +2024-05-02 16:39:49,158:INFO:Set up data. +2024-05-02 16:39:49,170:INFO:Set up index. +2024-05-02 16:40:07,479:INFO:Initializing load_model() +2024-05-02 16:40:07,479:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-02 16:40:07,623:INFO:Initializing predict_model() +2024-05-02 16:40:07,623:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x000001BD14EE7CE0>) +2024-05-02 16:40:07,623:INFO:Checking exceptions +2024-05-02 16:40:07,623:INFO:Preloading libraries +2024-05-02 16:40:07,624:INFO:Set up data. +2024-05-02 16:40:07,633:INFO:Set up index. +2024-05-03 16:58:28,747:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-03 16:58:28,773:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-03 16:58:28,774:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-03 16:58:28,775:WARNING: +'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install. +2024-05-03 18:44:40,533:INFO:Initializing load_model() +2024-05-03 18:44:40,533:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:44:43,126:INFO:Initializing predict_model() +2024-05-03 18:44:43,127:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276AA0919E0>) +2024-05-03 18:44:43,127:INFO:Checking exceptions +2024-05-03 18:44:43,127:INFO:Preloading libraries +2024-05-03 18:44:43,128:INFO:Set up data. +2024-05-03 18:44:43,289:INFO:Set up index. +2024-05-03 18:45:45,075:INFO:Initializing load_model() +2024-05-03 18:45:45,075:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:45:45,143:INFO:Initializing predict_model() +2024-05-03 18:45:45,143:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B151F060>) +2024-05-03 18:45:45,143:INFO:Checking exceptions +2024-05-03 18:45:45,143:INFO:Preloading libraries +2024-05-03 18:45:45,144:INFO:Set up data. +2024-05-03 18:45:45,155:INFO:Set up index. +2024-05-03 18:45:54,054:INFO:Initializing load_model() +2024-05-03 18:45:54,055:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:45:54,136:INFO:Initializing predict_model() +2024-05-03 18:45:54,136:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B151F4C0>) +2024-05-03 18:45:54,137:INFO:Checking exceptions +2024-05-03 18:45:54,137:INFO:Preloading libraries +2024-05-03 18:45:54,138:INFO:Set up data. +2024-05-03 18:45:54,153:INFO:Set up index. +2024-05-03 18:46:01,259:INFO:Initializing load_model() +2024-05-03 18:46:01,259:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:46:01,326:INFO:Initializing predict_model() +2024-05-03 18:46:01,326:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B151E5C0>) +2024-05-03 18:46:01,327:INFO:Checking exceptions +2024-05-03 18:46:01,327:INFO:Preloading libraries +2024-05-03 18:46:01,328:INFO:Set up data. +2024-05-03 18:46:01,344:INFO:Set up index. +2024-05-03 18:46:43,901:INFO:Initializing load_model() +2024-05-03 18:46:43,902:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:46:43,955:INFO:Initializing predict_model() +2024-05-03 18:46:43,956:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B141ACA0>) +2024-05-03 18:46:43,956:INFO:Checking exceptions +2024-05-03 18:46:43,956:INFO:Preloading libraries +2024-05-03 18:46:43,956:INFO:Set up data. +2024-05-03 18:46:43,969:INFO:Set up index. +2024-05-03 18:46:49,303:INFO:Initializing load_model() +2024-05-03 18:46:49,303:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:46:49,386:INFO:Initializing predict_model() +2024-05-03 18:46:49,386:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B151FA60>) +2024-05-03 18:46:49,387:INFO:Checking exceptions +2024-05-03 18:46:49,388:INFO:Preloading libraries +2024-05-03 18:46:49,389:INFO:Set up data. +2024-05-03 18:46:49,404:INFO:Set up index. +2024-05-03 18:48:11,673:INFO:Initializing load_model() +2024-05-03 18:48:11,674:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:48:11,737:INFO:Initializing predict_model() +2024-05-03 18:48:11,737:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B151EB60>) +2024-05-03 18:48:11,738:INFO:Checking exceptions +2024-05-03 18:48:11,738:INFO:Preloading libraries +2024-05-03 18:48:11,738:INFO:Set up data. +2024-05-03 18:48:11,756:INFO:Set up index. +2024-05-03 18:48:42,479:INFO:Initializing load_model() +2024-05-03 18:48:42,479:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:48:42,582:INFO:Initializing predict_model() +2024-05-03 18:48:42,582:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B151CA40>) +2024-05-03 18:48:42,582:INFO:Checking exceptions +2024-05-03 18:48:42,583:INFO:Preloading libraries +2024-05-03 18:48:42,583:INFO:Set up data. +2024-05-03 18:48:42,599:INFO:Set up index. +2024-05-03 18:48:45,317:INFO:Initializing load_model() +2024-05-03 18:48:45,317:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:48:45,395:INFO:Initializing predict_model() +2024-05-03 18:48:45,396:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B151CC20>) +2024-05-03 18:48:45,396:INFO:Checking exceptions +2024-05-03 18:48:45,396:INFO:Preloading libraries +2024-05-03 18:48:45,397:INFO:Set up data. +2024-05-03 18:48:45,416:INFO:Set up index. +2024-05-03 18:49:52,556:INFO:Initializing load_model() +2024-05-03 18:49:52,557:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:49:52,629:INFO:Initializing predict_model() +2024-05-03 18:49:52,629:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B151C720>) +2024-05-03 18:49:52,629:INFO:Checking exceptions +2024-05-03 18:49:52,629:INFO:Preloading libraries +2024-05-03 18:49:52,630:INFO:Set up data. +2024-05-03 18:49:52,646:INFO:Set up index. +2024-05-03 18:49:55,110:INFO:Initializing load_model() +2024-05-03 18:49:55,147:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:49:55,270:INFO:Initializing predict_model() +2024-05-03 18:49:55,270:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B170CCC0>) +2024-05-03 18:49:55,271:INFO:Checking exceptions +2024-05-03 18:49:55,271:INFO:Preloading libraries +2024-05-03 18:49:55,271:INFO:Set up data. +2024-05-03 18:49:55,290:INFO:Set up index. +2024-05-03 18:50:01,936:INFO:Initializing load_model() +2024-05-03 18:50:01,936:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:50:02,007:INFO:Initializing predict_model() +2024-05-03 18:50:02,008:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B170F740>) +2024-05-03 18:50:02,008:INFO:Checking exceptions +2024-05-03 18:50:02,008:INFO:Preloading libraries +2024-05-03 18:50:02,009:INFO:Set up data. +2024-05-03 18:50:02,026:INFO:Set up index. +2024-05-03 18:50:12,003:INFO:Initializing load_model() +2024-05-03 18:50:12,003:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:50:12,110:INFO:Initializing predict_model() +2024-05-03 18:50:12,110:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B170E200>) +2024-05-03 18:50:12,110:INFO:Checking exceptions +2024-05-03 18:50:12,110:INFO:Preloading libraries +2024-05-03 18:50:12,111:INFO:Set up data. +2024-05-03 18:50:12,127:INFO:Set up index. +2024-05-03 18:50:15,242:INFO:Initializing load_model() +2024-05-03 18:50:15,242:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:50:15,300:INFO:Initializing predict_model() +2024-05-03 18:50:15,300:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B0203EC0>) +2024-05-03 18:50:15,301:INFO:Checking exceptions +2024-05-03 18:50:15,301:INFO:Preloading libraries +2024-05-03 18:50:15,302:INFO:Set up data. +2024-05-03 18:50:15,318:INFO:Set up index. +2024-05-03 18:50:17,030:INFO:Initializing load_model() +2024-05-03 18:50:17,031:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:50:17,093:INFO:Initializing predict_model() +2024-05-03 18:50:17,093:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B01DC040>) +2024-05-03 18:50:17,093:INFO:Checking exceptions +2024-05-03 18:50:17,093:INFO:Preloading libraries +2024-05-03 18:50:17,094:INFO:Set up data. +2024-05-03 18:50:17,114:INFO:Set up index. +2024-05-03 18:50:23,665:INFO:Initializing load_model() +2024-05-03 18:50:23,665:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:50:23,725:INFO:Initializing predict_model() +2024-05-03 18:50:23,725:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276AFE977E0>) +2024-05-03 18:50:23,725:INFO:Checking exceptions +2024-05-03 18:50:23,726:INFO:Preloading libraries +2024-05-03 18:50:23,726:INFO:Set up data. +2024-05-03 18:50:23,739:INFO:Set up index. +2024-05-03 18:50:28,939:INFO:Initializing load_model() +2024-05-03 18:50:28,946:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:50:29,005:INFO:Initializing predict_model() +2024-05-03 18:50:29,005:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B170CEA0>) +2024-05-03 18:50:29,005:INFO:Checking exceptions +2024-05-03 18:50:29,005:INFO:Preloading libraries +2024-05-03 18:50:29,006:INFO:Set up data. +2024-05-03 18:50:29,021:INFO:Set up index. +2024-05-03 18:50:34,014:INFO:Initializing load_model() +2024-05-03 18:50:34,015:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:50:34,197:INFO:Initializing predict_model() +2024-05-03 18:50:34,198:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B170EE80>) +2024-05-03 18:50:34,198:INFO:Checking exceptions +2024-05-03 18:50:34,198:INFO:Preloading libraries +2024-05-03 18:50:34,200:INFO:Set up data. +2024-05-03 18:50:34,248:INFO:Set up index. +2024-05-03 18:50:42,623:INFO:Initializing load_model() +2024-05-03 18:50:42,623:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:50:42,733:INFO:Initializing predict_model() +2024-05-03 18:50:42,735:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B170DA80>) +2024-05-03 18:50:42,737:INFO:Checking exceptions +2024-05-03 18:50:42,737:INFO:Preloading libraries +2024-05-03 18:50:42,738:INFO:Set up data. +2024-05-03 18:50:42,761:INFO:Set up index. +2024-05-03 18:51:06,590:INFO:Initializing load_model() +2024-05-03 18:51:06,591:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:51:06,659:INFO:Initializing predict_model() +2024-05-03 18:51:06,659:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B170F6A0>) +2024-05-03 18:51:06,659:INFO:Checking exceptions +2024-05-03 18:51:06,660:INFO:Preloading libraries +2024-05-03 18:51:06,660:INFO:Set up data. +2024-05-03 18:51:06,688:INFO:Set up index. +2024-05-03 18:51:08,316:INFO:Initializing load_model() +2024-05-03 18:51:08,317:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:51:08,391:INFO:Initializing predict_model() +2024-05-03 18:51:08,391:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B170E480>) +2024-05-03 18:51:08,391:INFO:Checking exceptions +2024-05-03 18:51:08,392:INFO:Preloading libraries +2024-05-03 18:51:08,393:INFO:Set up data. +2024-05-03 18:51:08,420:INFO:Set up index. +2024-05-03 18:51:21,161:INFO:Initializing load_model() +2024-05-03 18:51:21,161:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:51:21,238:INFO:Initializing predict_model() +2024-05-03 18:51:21,238:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B170FA60>) +2024-05-03 18:51:21,239:INFO:Checking exceptions +2024-05-03 18:51:21,239:INFO:Preloading libraries +2024-05-03 18:51:21,240:INFO:Set up data. +2024-05-03 18:51:21,256:INFO:Set up index. +2024-05-03 18:51:38,792:INFO:Initializing load_model() +2024-05-03 18:51:38,792:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:51:38,870:INFO:Initializing predict_model() +2024-05-03 18:51:38,871:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B170DDA0>) +2024-05-03 18:51:38,871:INFO:Checking exceptions +2024-05-03 18:51:38,871:INFO:Preloading libraries +2024-05-03 18:51:38,872:INFO:Set up data. +2024-05-03 18:51:38,889:INFO:Set up index. +2024-05-03 18:51:49,797:INFO:Initializing load_model() +2024-05-03 18:51:49,799:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:51:49,903:INFO:Initializing predict_model() +2024-05-03 18:51:49,903:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B170FC40>) +2024-05-03 18:51:49,903:INFO:Checking exceptions +2024-05-03 18:51:49,903:INFO:Preloading libraries +2024-05-03 18:51:49,904:INFO:Set up data. +2024-05-03 18:51:49,920:INFO:Set up index. +2024-05-03 18:51:55,052:INFO:Initializing load_model() +2024-05-03 18:51:55,053:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:51:55,170:INFO:Initializing predict_model() +2024-05-03 18:51:55,170:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B170CD60>) +2024-05-03 18:51:55,170:INFO:Checking exceptions +2024-05-03 18:51:55,170:INFO:Preloading libraries +2024-05-03 18:51:55,171:INFO:Set up data. +2024-05-03 18:51:55,185:INFO:Set up index. +2024-05-03 18:53:38,719:INFO:Initializing load_model() +2024-05-03 18:53:38,719:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:53:38,832:INFO:Initializing predict_model() +2024-05-03 18:53:38,832:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B0203060>) +2024-05-03 18:53:38,833:INFO:Checking exceptions +2024-05-03 18:53:38,833:INFO:Preloading libraries +2024-05-03 18:53:38,833:INFO:Set up data. +2024-05-03 18:53:38,848:INFO:Set up index. +2024-05-03 18:54:02,691:INFO:Initializing load_model() +2024-05-03 18:54:02,692:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:54:02,804:INFO:Initializing predict_model() +2024-05-03 18:54:02,805:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B151D3A0>) +2024-05-03 18:54:02,805:INFO:Checking exceptions +2024-05-03 18:54:02,805:INFO:Preloading libraries +2024-05-03 18:54:02,806:INFO:Set up data. +2024-05-03 18:54:02,821:INFO:Set up index. +2024-05-03 18:54:14,480:INFO:Initializing load_model() +2024-05-03 18:54:14,480:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:54:14,575:INFO:Initializing predict_model() +2024-05-03 18:54:14,575:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B170E980>) +2024-05-03 18:54:14,575:INFO:Checking exceptions +2024-05-03 18:54:14,575:INFO:Preloading libraries +2024-05-03 18:54:14,576:INFO:Set up data. +2024-05-03 18:54:14,590:INFO:Set up index. +2024-05-03 18:57:12,137:INFO:Initializing load_model() +2024-05-03 18:57:12,137:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:57:12,280:INFO:Initializing predict_model() +2024-05-03 18:57:12,280:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B170FA60>) +2024-05-03 18:57:12,281:INFO:Checking exceptions +2024-05-03 18:57:12,281:INFO:Preloading libraries +2024-05-03 18:57:12,282:INFO:Set up data. +2024-05-03 18:57:12,301:INFO:Set up index. +2024-05-03 18:57:17,609:INFO:Initializing load_model() +2024-05-03 18:57:17,609:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 18:57:17,735:INFO:Initializing predict_model() +2024-05-03 18:57:17,736:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B170E7A0>) +2024-05-03 18:57:17,736:INFO:Checking exceptions +2024-05-03 18:57:17,736:INFO:Preloading libraries +2024-05-03 18:57:17,736:INFO:Set up data. +2024-05-03 18:57:17,749:INFO:Set up index. +2024-05-03 19:14:57,894:INFO:Initializing load_model() +2024-05-03 19:14:57,895:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 19:14:58,100:INFO:Initializing predict_model() +2024-05-03 19:14:58,100:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B151C720>) +2024-05-03 19:14:58,100:INFO:Checking exceptions +2024-05-03 19:14:58,101:INFO:Preloading libraries +2024-05-03 19:14:58,101:INFO:Set up data. +2024-05-03 19:14:58,119:INFO:Set up index. +2024-05-03 19:15:32,431:INFO:Initializing load_model() +2024-05-03 19:15:32,432:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 19:15:32,642:INFO:Initializing predict_model() +2024-05-03 19:15:32,643:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B1BFA660>) +2024-05-03 19:15:32,643:INFO:Checking exceptions +2024-05-03 19:15:32,643:INFO:Preloading libraries +2024-05-03 19:15:32,644:INFO:Set up data. +2024-05-03 19:15:32,661:INFO:Set up index. +2024-05-03 19:18:54,873:INFO:Initializing load_model() +2024-05-03 19:18:54,873:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 19:18:55,044:INFO:Initializing predict_model() +2024-05-03 19:18:55,045:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B1BFBF60>) +2024-05-03 19:18:55,045:INFO:Checking exceptions +2024-05-03 19:18:55,045:INFO:Preloading libraries +2024-05-03 19:18:55,046:INFO:Set up data. +2024-05-03 19:18:55,063:INFO:Set up index. +2024-05-03 19:19:04,977:INFO:Initializing load_model() +2024-05-03 19:19:04,977:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 19:19:05,320:INFO:Initializing predict_model() +2024-05-03 19:19:05,320:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B0203240>) +2024-05-03 19:19:05,321:INFO:Checking exceptions +2024-05-03 19:19:05,321:INFO:Preloading libraries +2024-05-03 19:19:05,322:INFO:Set up data. +2024-05-03 19:19:05,339:INFO:Set up index. +2024-05-03 19:20:54,776:INFO:Initializing load_model() +2024-05-03 19:20:54,776:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 19:20:55,305:INFO:Initializing predict_model() +2024-05-03 19:20:55,306:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276970B7880>) +2024-05-03 19:20:55,307:INFO:Checking exceptions +2024-05-03 19:20:55,307:INFO:Preloading libraries +2024-05-03 19:20:55,311:INFO:Set up data. +2024-05-03 19:20:55,356:INFO:Set up index. +2024-05-03 19:22:40,247:INFO:Initializing load_model() +2024-05-03 19:22:40,248:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 19:22:40,461:INFO:Initializing predict_model() +2024-05-03 19:22:40,462:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B1BF82C0>) +2024-05-03 19:22:40,462:INFO:Checking exceptions +2024-05-03 19:22:40,462:INFO:Preloading libraries +2024-05-03 19:22:40,464:INFO:Set up data. +2024-05-03 19:22:40,500:INFO:Set up index. +2024-05-03 19:23:08,634:INFO:Initializing load_model() +2024-05-03 19:23:08,635:INFO:load_model(model_name=./models/Walmart, platform=None, authentication=None, verbose=True) +2024-05-03 19:23:08,986:INFO:Initializing predict_model() +2024-05-03 19:23:08,987:INFO:predict_model(self=, estimator=Pipeline(memory=Memory(location=None), + steps=[('numerical_imputer', + TransformerWrapper(include=['Store', 'Holiday_Flag', + 'Temperature', 'Fuel_Price', 'CPI', + 'Unemployment', 'Year', 'Month', + 'Day'], + transformer=SimpleImputer())), + ('categorical_imputer', + TransformerWrapper(include=[], + transformer=SimpleImputer(strategy='most_frequent'))), + ('normalize', TransformerWr... + 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, + predictor='auto', random_state=8701, reg_alpha=0, + reg_lambda=1, ...))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=.encode_labels at 0x00000276B1BFA340>) +2024-05-03 19:23:08,988:INFO:Checking exceptions +2024-05-03 19:23:08,989:INFO:Preloading libraries +2024-05-03 19:23:08,996:INFO:Set up data. +2024-05-03 19:23:09,059:INFO:Set up index.