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logs.log
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2024-07-05 19:08:59,772:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-07-05 19:08:59,785:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-07-05 19:08:59,785:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-07-05 19:08:59,785:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-07-05 19:09:01,363:WARNING:C:\Users\riddh\Desktop\DeepLearning\AutoML\env\lib\site-packages\numba\core\decorators.py:262: NumbaDeprecationWarning: [1mnumba.generated_jit is deprecated. Please see the documentation at: https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-generated-jit for more information and advice on a suitable replacement.[0m
warnings.warn(msg, NumbaDeprecationWarning)
2024-07-05 19:09:01,420:WARNING:C:\Users\riddh\Desktop\DeepLearning\AutoML\env\lib\site-packages\visions\backends\shared\nan_handling.py:51: NumbaDeprecationWarning: [1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.[0m
def hasna(x: np.ndarray) -> bool:
2024-07-05 19:09:01,746:WARNING:C:\Users\riddh\Desktop\DeepLearning\AutoML\autoML.py:4: DeprecationWarning: `import pandas_profiling` is going to be deprecated by April 1st. Please use `import ydata_profiling` instead.
from pandas_profiling import ProfileReport
2024-07-05 19:10:03,710:INFO:PyCaret RegressionExperiment
2024-07-05 19:10:03,710:INFO:Logging name: reg-default-name
2024-07-05 19:10:03,710:INFO:ML Usecase: MLUsecase.REGRESSION
2024-07-05 19:10:03,710:INFO:version 3.3.2
2024-07-05 19:10:03,710:INFO:Initializing setup()
2024-07-05 19:10:03,710:INFO:self.USI: 8a62
2024-07-05 19:10:03,710:INFO:self._variable_keys: {'exp_name_log', 'USI', 'exp_id', 'fold_generator', 'pipeline', 'X_train', 'X_test', 'data', 'idx', 'gpu_param', 'y', '_available_plots', '_ml_usecase', 'html_param', 'fold_shuffle_param', 'n_jobs_param', 'X', 'y_train', 'fold_groups_param', 'transform_target_param', 'memory', 'seed', 'logging_param', 'y_test', 'target_param', 'gpu_n_jobs_param', 'log_plots_param'}
2024-07-05 19:10:03,710:INFO:Checking environment
2024-07-05 19:10:03,710:INFO:python_version: 3.9.19
2024-07-05 19:10:03,710:INFO:python_build: ('main', 'May 6 2024 20:12:36')
2024-07-05 19:10:03,715:INFO:machine: AMD64
2024-07-05 19:10:03,737:INFO:platform: Windows-10-10.0.22631-SP0
2024-07-05 19:10:03,749:INFO:Memory: svmem(total=7887364096, available=610856960, percent=92.3, used=7276507136, free=610856960)
2024-07-05 19:10:03,749:INFO:Physical Core: 6
2024-07-05 19:10:03,749:INFO:Logical Core: 12
2024-07-05 19:10:03,749:INFO:Checking libraries
2024-07-05 19:10:03,749:INFO:System:
2024-07-05 19:10:03,749:INFO: python: 3.9.19 (main, May 6 2024, 20:12:36) [MSC v.1916 64 bit (AMD64)]
2024-07-05 19:10:03,749:INFO:executable: C:\Users\riddh\Desktop\DeepLearning\AutoML\env\python.exe
2024-07-05 19:10:03,749:INFO: machine: Windows-10-10.0.22631-SP0
2024-07-05 19:10:03,749:INFO:PyCaret required dependencies:
2024-07-05 19:10:03,871:INFO: pip: 24.1.1
2024-07-05 19:10:03,871:INFO: setuptools: 70.2.0
2024-07-05 19:10:03,871:INFO: pycaret: 3.3.2
2024-07-05 19:10:03,871:INFO: IPython: 8.18.1
2024-07-05 19:10:03,871:INFO: ipywidgets: 8.1.3
2024-07-05 19:10:03,871:INFO: tqdm: 4.64.1
2024-07-05 19:10:03,871:INFO: numpy: 1.23.5
2024-07-05 19:10:03,871:INFO: pandas: 1.5.3
2024-07-05 19:10:03,871:INFO: jinja2: 3.1.4
2024-07-05 19:10:03,871:INFO: scipy: 1.9.3
2024-07-05 19:10:03,871:INFO: joblib: 1.3.2
2024-07-05 19:10:03,871:INFO: sklearn: 1.4.2
2024-07-05 19:10:03,871:INFO: pyod: 2.0.1
2024-07-05 19:10:03,871:INFO: imblearn: 0.12.3
2024-07-05 19:10:03,871:INFO: category_encoders: 2.6.3
2024-07-05 19:10:03,871:INFO: lightgbm: 4.4.0
2024-07-05 19:10:03,871:INFO: numba: 0.58.1
2024-07-05 19:10:03,871:INFO: requests: 2.28.2
2024-07-05 19:10:03,871:INFO: matplotlib: 3.6.3
2024-07-05 19:10:03,871:INFO: scikitplot: 0.3.7
2024-07-05 19:10:03,871:INFO: yellowbrick: 1.5
2024-07-05 19:10:03,871:INFO: plotly: 5.22.0
2024-07-05 19:10:03,871:INFO: plotly-resampler: Not installed
2024-07-05 19:10:03,871:INFO: kaleido: 0.2.1
2024-07-05 19:10:03,871:INFO: schemdraw: 0.15
2024-07-05 19:10:03,871:INFO: statsmodels: 0.13.5
2024-07-05 19:10:03,871:INFO: sktime: 0.26.0
2024-07-05 19:10:03,871:INFO: tbats: 1.1.3
2024-07-05 19:10:03,871:INFO: pmdarima: 2.0.4
2024-07-05 19:10:03,871:INFO: psutil: 6.0.0
2024-07-05 19:10:03,871:INFO: markupsafe: 2.1.5
2024-07-05 19:10:03,871:INFO: pickle5: Not installed
2024-07-05 19:10:03,871:INFO: cloudpickle: 3.0.0
2024-07-05 19:10:03,871:INFO: deprecation: 2.1.0
2024-07-05 19:10:03,871:INFO: xxhash: 3.4.1
2024-07-05 19:10:03,871:INFO: wurlitzer: Not installed
2024-07-05 19:10:03,871:INFO:PyCaret optional dependencies:
2024-07-05 19:10:03,898:INFO: shap: Not installed
2024-07-05 19:10:03,898:INFO: interpret: Not installed
2024-07-05 19:10:03,898:INFO: umap: Not installed
2024-07-05 19:10:03,898:INFO: ydata_profiling: 4.0.0
2024-07-05 19:10:03,898:INFO: explainerdashboard: Not installed
2024-07-05 19:10:03,898:INFO: autoviz: Not installed
2024-07-05 19:10:03,898:INFO: fairlearn: Not installed
2024-07-05 19:10:03,898:INFO: deepchecks: Not installed
2024-07-05 19:10:03,898:INFO: xgboost: Not installed
2024-07-05 19:10:03,898:INFO: catboost: Not installed
2024-07-05 19:10:03,898:INFO: kmodes: Not installed
2024-07-05 19:10:03,898:INFO: mlxtend: Not installed
2024-07-05 19:10:03,898:INFO: statsforecast: Not installed
2024-07-05 19:10:03,898:INFO: tune_sklearn: Not installed
2024-07-05 19:10:03,898:INFO: ray: Not installed
2024-07-05 19:10:03,898:INFO: hyperopt: Not installed
2024-07-05 19:10:03,898:INFO: optuna: Not installed
2024-07-05 19:10:03,898:INFO: skopt: Not installed
2024-07-05 19:10:03,898:INFO: mlflow: Not installed
2024-07-05 19:10:03,898:INFO: gradio: Not installed
2024-07-05 19:10:03,898:INFO: fastapi: Not installed
2024-07-05 19:10:03,898:INFO: uvicorn: Not installed
2024-07-05 19:10:03,898:INFO: m2cgen: Not installed
2024-07-05 19:10:03,898:INFO: evidently: Not installed
2024-07-05 19:10:03,898:INFO: fugue: Not installed
2024-07-05 19:10:03,898:INFO: streamlit: 1.36.0
2024-07-05 19:10:03,898:INFO: prophet: Not installed
2024-07-05 19:10:03,898:INFO:None
2024-07-05 19:10:03,900:INFO:Set up data.
2024-07-05 19:10:03,911:INFO:Set up folding strategy.
2024-07-05 19:10:03,911:INFO:Set up train/test split.
2024-07-05 19:10:03,919:INFO:Set up index.
2024-07-05 19:10:03,919:INFO:Assigning column types.
2024-07-05 19:10:03,927:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2024-07-05 19:10:03,927:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None.
2024-07-05 19:10:03,936:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
2024-07-05 19:10:03,942:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,027:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,092:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,092:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:04,092: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]`
2024-07-05 19:10:04,092:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,092:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,105:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,187:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,247:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,249:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:04,250: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]`
2024-07-05 19:10:04,250:INFO:Engine successfully changes for model 'lasso' to 'sklearn'.
2024-07-05 19:10:04,250:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,263:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,328:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,398:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,398:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:04,398: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]`
2024-07-05 19:10:04,414:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,415:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,517:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,583:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,583:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:04,583: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]`
2024-07-05 19:10:04,583:INFO:Engine successfully changes for model 'ridge' to 'sklearn'.
2024-07-05 19:10:04,600:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,675:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,757:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,758:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:04,758: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]`
2024-07-05 19:10:04,766:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,840:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,911:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:04,911:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:04,911: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]`
2024-07-05 19:10:04,911:INFO:Engine successfully changes for model 'en' to 'sklearn'.
2024-07-05 19:10:05,003:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:05,067:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:05,067:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:05,067: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]`
2024-07-05 19:10:05,166:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:05,236:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:05,236:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:05,236: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]`
2024-07-05 19:10:05,236:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2024-07-05 19:10:05,323:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:05,388:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:05,388: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]`
2024-07-05 19:10:05,484:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:05,549:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:05,549: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]`
2024-07-05 19:10:05,553:INFO:Engine successfully changes for model 'svm' to 'sklearn'.
2024-07-05 19:10:05,709:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:05,709: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]`
2024-07-05 19:10:05,856:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:05,856: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]`
2024-07-05 19:10:05,894:INFO:Preparing preprocessing pipeline...
2024-07-05 19:10:05,894:INFO:Set up simple imputation.
2024-07-05 19:10:05,897:INFO:Set up encoding of ordinal features.
2024-07-05 19:10:05,897:INFO:Set up encoding of categorical features.
2024-07-05 19:10:06,033:INFO:Finished creating preprocessing pipeline.
2024-07-05 19:10:06,087:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\riddh\AppData\Local\Temp\joblib),
steps=[('numerical_imputer',
TransformerWrapper(include=['CustomerID', 'Age', 'Income',
'AverageSpend', 'GroupSize',
'OnlineReservation',
'DeliveryOrder',
'LoyaltyProgramMember', 'WaitTime',
'ServiceRating', 'FoodRating',
'AmbianceRating'],
transformer=SimpleImputer())),
('categorical_...
{'col': 'MealType',
'data_type': dtype('O'),
'mapping': Dine-in 0
Takeaway 1
NaN -1
dtype: int64}]))),
('onehot_encoding',
TransformerWrapper(include=['VisitFrequency',
'PreferredCuisine', 'TimeOfVisit',
'DiningOccasion'],
transformer=OneHotEncoder(cols=['VisitFrequency',
'PreferredCuisine',
'TimeOfVisit',
'DiningOccasion'],
handle_missing='return_nan',
use_cat_names=True)))])
2024-07-05 19:10:06,087:INFO:Creating final display dataframe.
2024-07-05 19:10:06,443:INFO:Setup _display_container: Description Value
0 Session id 6424
1 Target HighSatisfaction
2 Target type Regression
3 Original data shape (1500, 19)
4 Transformed data shape (1500, 30)
5 Transformed train set shape (1050, 30)
6 Transformed test set shape (450, 30)
7 Numeric features 12
8 Categorical features 6
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 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 8a62
2024-07-05 19:10:06,596:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:06,596: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]`
2024-07-05 19:10:06,768:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:06,769: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]`
2024-07-05 19:10:06,770:INFO:setup() successfully completed in 3.08s...............
2024-07-05 19:10:06,771:INFO:Initializing compare_models()
2024-07-05 19:10:06,771:INFO:compare_models(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, include=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': <pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, '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__': <class 'pycaret.regression.oop.RegressionExperiment'>}, exclude=None)
2024-07-05 19:10:06,771:INFO:Checking exceptions
2024-07-05 19:10:06,775:INFO:Preparing display monitor
2024-07-05 19:10:06,785:INFO:Initializing Linear Regression
2024-07-05 19:10:06,785:INFO:Total runtime is 0.0 minutes
2024-07-05 19:10:06,786:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:06,786:INFO:Initializing create_model()
2024-07-05 19:10:06,786:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:06,786:INFO:Checking exceptions
2024-07-05 19:10:06,786:INFO:Importing libraries
2024-07-05 19:10:06,786:INFO:Copying training dataset
2024-07-05 19:10:06,788:INFO:Defining folds
2024-07-05 19:10:06,788:INFO:Declaring metric variables
2024-07-05 19:10:06,788:INFO:Importing untrained model
2024-07-05 19:10:06,788:INFO:Linear Regression Imported successfully
2024-07-05 19:10:06,788:INFO:Starting cross validation
2024-07-05 19:10:06,803:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:13,404:INFO:Calculating mean and std
2024-07-05 19:10:13,404:INFO:Creating metrics dataframe
2024-07-05 19:10:13,404:INFO:Uploading results into container
2024-07-05 19:10:13,404:INFO:Uploading model into container now
2024-07-05 19:10:13,404:INFO:_master_model_container: 1
2024-07-05 19:10:13,404:INFO:_display_container: 2
2024-07-05 19:10:13,404:INFO:LinearRegression(n_jobs=-1)
2024-07-05 19:10:13,404:INFO:create_model() successfully completed......................................
2024-07-05 19:10:13,594:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:13,594:INFO:Creating metrics dataframe
2024-07-05 19:10:13,603:INFO:Initializing Lasso Regression
2024-07-05 19:10:13,603:INFO:Total runtime is 0.1136337955792745 minutes
2024-07-05 19:10:13,603:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:13,603:INFO:Initializing create_model()
2024-07-05 19:10:13,603:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:13,603:INFO:Checking exceptions
2024-07-05 19:10:13,603:INFO:Importing libraries
2024-07-05 19:10:13,603:INFO:Copying training dataset
2024-07-05 19:10:13,610:INFO:Defining folds
2024-07-05 19:10:13,610:INFO:Declaring metric variables
2024-07-05 19:10:13,610:INFO:Importing untrained model
2024-07-05 19:10:13,610:INFO:Lasso Regression Imported successfully
2024-07-05 19:10:13,610:INFO:Starting cross validation
2024-07-05 19:10:13,610:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:16,665:INFO:Calculating mean and std
2024-07-05 19:10:16,665:INFO:Creating metrics dataframe
2024-07-05 19:10:16,673:INFO:Uploading results into container
2024-07-05 19:10:16,673:INFO:Uploading model into container now
2024-07-05 19:10:16,673:INFO:_master_model_container: 2
2024-07-05 19:10:16,673:INFO:_display_container: 2
2024-07-05 19:10:16,673:INFO:Lasso(random_state=6424)
2024-07-05 19:10:16,673:INFO:create_model() successfully completed......................................
2024-07-05 19:10:16,855:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:16,856:INFO:Creating metrics dataframe
2024-07-05 19:10:16,861:INFO:Initializing Ridge Regression
2024-07-05 19:10:16,862:INFO:Total runtime is 0.1679274876912435 minutes
2024-07-05 19:10:16,862:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:16,862:INFO:Initializing create_model()
2024-07-05 19:10:16,862:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:16,862:INFO:Checking exceptions
2024-07-05 19:10:16,862:INFO:Importing libraries
2024-07-05 19:10:16,862:INFO:Copying training dataset
2024-07-05 19:10:16,868:INFO:Defining folds
2024-07-05 19:10:16,868:INFO:Declaring metric variables
2024-07-05 19:10:16,868:INFO:Importing untrained model
2024-07-05 19:10:16,868:INFO:Ridge Regression Imported successfully
2024-07-05 19:10:16,868:INFO:Starting cross validation
2024-07-05 19:10:16,870:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:17,115:INFO:Calculating mean and std
2024-07-05 19:10:17,115:INFO:Creating metrics dataframe
2024-07-05 19:10:17,117:INFO:Uploading results into container
2024-07-05 19:10:17,117:INFO:Uploading model into container now
2024-07-05 19:10:17,117:INFO:_master_model_container: 3
2024-07-05 19:10:17,117:INFO:_display_container: 2
2024-07-05 19:10:17,117:INFO:Ridge(random_state=6424)
2024-07-05 19:10:17,117:INFO:create_model() successfully completed......................................
2024-07-05 19:10:17,264:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:17,264:INFO:Creating metrics dataframe
2024-07-05 19:10:17,272:INFO:Initializing Elastic Net
2024-07-05 19:10:17,272:INFO:Total runtime is 0.17477264404296874 minutes
2024-07-05 19:10:17,272:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:17,272:INFO:Initializing create_model()
2024-07-05 19:10:17,272:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:17,272:INFO:Checking exceptions
2024-07-05 19:10:17,272:INFO:Importing libraries
2024-07-05 19:10:17,272:INFO:Copying training dataset
2024-07-05 19:10:17,272:INFO:Defining folds
2024-07-05 19:10:17,272:INFO:Declaring metric variables
2024-07-05 19:10:17,272:INFO:Importing untrained model
2024-07-05 19:10:17,272:INFO:Elastic Net Imported successfully
2024-07-05 19:10:17,272:INFO:Starting cross validation
2024-07-05 19:10:17,272:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:17,526:INFO:Calculating mean and std
2024-07-05 19:10:17,527:INFO:Creating metrics dataframe
2024-07-05 19:10:17,531:INFO:Uploading results into container
2024-07-05 19:10:17,531:INFO:Uploading model into container now
2024-07-05 19:10:17,534:INFO:_master_model_container: 4
2024-07-05 19:10:17,534:INFO:_display_container: 2
2024-07-05 19:10:17,534:INFO:ElasticNet(random_state=6424)
2024-07-05 19:10:17,534:INFO:create_model() successfully completed......................................
2024-07-05 19:10:17,683:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:17,683:INFO:Creating metrics dataframe
2024-07-05 19:10:17,698:INFO:Initializing Least Angle Regression
2024-07-05 19:10:17,698:INFO:Total runtime is 0.18188565174738566 minutes
2024-07-05 19:10:17,698:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:17,698:INFO:Initializing create_model()
2024-07-05 19:10:17,698:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:17,698:INFO:Checking exceptions
2024-07-05 19:10:17,698:INFO:Importing libraries
2024-07-05 19:10:17,698:INFO:Copying training dataset
2024-07-05 19:10:17,704:INFO:Defining folds
2024-07-05 19:10:17,704:INFO:Declaring metric variables
2024-07-05 19:10:17,704:INFO:Importing untrained model
2024-07-05 19:10:17,704:INFO:Least Angle Regression Imported successfully
2024-07-05 19:10:17,704:INFO:Starting cross validation
2024-07-05 19:10:17,706:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:17,996:INFO:Calculating mean and std
2024-07-05 19:10:17,999:INFO:Creating metrics dataframe
2024-07-05 19:10:18,000:INFO:Uploading results into container
2024-07-05 19:10:18,000:INFO:Uploading model into container now
2024-07-05 19:10:18,000:INFO:_master_model_container: 5
2024-07-05 19:10:18,000:INFO:_display_container: 2
2024-07-05 19:10:18,000:INFO:Lars(random_state=6424)
2024-07-05 19:10:18,000:INFO:create_model() successfully completed......................................
2024-07-05 19:10:18,132:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:18,132:INFO:Creating metrics dataframe
2024-07-05 19:10:18,133:INFO:Initializing Lasso Least Angle Regression
2024-07-05 19:10:18,133:INFO:Total runtime is 0.18913275003433228 minutes
2024-07-05 19:10:18,133:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:18,133:INFO:Initializing create_model()
2024-07-05 19:10:18,133:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:18,133:INFO:Checking exceptions
2024-07-05 19:10:18,133:INFO:Importing libraries
2024-07-05 19:10:18,133:INFO:Copying training dataset
2024-07-05 19:10:18,140:INFO:Defining folds
2024-07-05 19:10:18,140:INFO:Declaring metric variables
2024-07-05 19:10:18,140:INFO:Importing untrained model
2024-07-05 19:10:18,140:INFO:Lasso Least Angle Regression Imported successfully
2024-07-05 19:10:18,140:INFO:Starting cross validation
2024-07-05 19:10:18,142:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:18,438:INFO:Calculating mean and std
2024-07-05 19:10:18,438:INFO:Creating metrics dataframe
2024-07-05 19:10:18,442:INFO:Uploading results into container
2024-07-05 19:10:18,442:INFO:Uploading model into container now
2024-07-05 19:10:18,442:INFO:_master_model_container: 6
2024-07-05 19:10:18,442:INFO:_display_container: 2
2024-07-05 19:10:18,442:INFO:LassoLars(random_state=6424)
2024-07-05 19:10:18,442:INFO:create_model() successfully completed......................................
2024-07-05 19:10:18,582:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:18,582:INFO:Creating metrics dataframe
2024-07-05 19:10:18,587:INFO:Initializing Orthogonal Matching Pursuit
2024-07-05 19:10:18,587:INFO:Total runtime is 0.19669640461603802 minutes
2024-07-05 19:10:18,588:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:18,588:INFO:Initializing create_model()
2024-07-05 19:10:18,588:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:18,588:INFO:Checking exceptions
2024-07-05 19:10:18,588:INFO:Importing libraries
2024-07-05 19:10:18,588:INFO:Copying training dataset
2024-07-05 19:10:18,592:INFO:Defining folds
2024-07-05 19:10:18,592:INFO:Declaring metric variables
2024-07-05 19:10:18,592:INFO:Importing untrained model
2024-07-05 19:10:18,592:INFO:Orthogonal Matching Pursuit Imported successfully
2024-07-05 19:10:18,593:INFO:Starting cross validation
2024-07-05 19:10:18,594:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:18,830:INFO:Calculating mean and std
2024-07-05 19:10:18,831:INFO:Creating metrics dataframe
2024-07-05 19:10:18,831:INFO:Uploading results into container
2024-07-05 19:10:18,831:INFO:Uploading model into container now
2024-07-05 19:10:18,831:INFO:_master_model_container: 7
2024-07-05 19:10:18,831:INFO:_display_container: 2
2024-07-05 19:10:18,831:INFO:OrthogonalMatchingPursuit()
2024-07-05 19:10:18,831:INFO:create_model() successfully completed......................................
2024-07-05 19:10:18,952:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:18,952:INFO:Creating metrics dataframe
2024-07-05 19:10:18,952:INFO:Initializing Bayesian Ridge
2024-07-05 19:10:18,952:INFO:Total runtime is 0.20277980168660484 minutes
2024-07-05 19:10:18,952:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:18,952:INFO:Initializing create_model()
2024-07-05 19:10:18,952:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:18,952:INFO:Checking exceptions
2024-07-05 19:10:18,952:INFO:Importing libraries
2024-07-05 19:10:18,952:INFO:Copying training dataset
2024-07-05 19:10:18,966:INFO:Defining folds
2024-07-05 19:10:18,966:INFO:Declaring metric variables
2024-07-05 19:10:18,966:INFO:Importing untrained model
2024-07-05 19:10:18,966:INFO:Bayesian Ridge Imported successfully
2024-07-05 19:10:18,966:INFO:Starting cross validation
2024-07-05 19:10:18,966:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:19,197:INFO:Calculating mean and std
2024-07-05 19:10:19,197:INFO:Creating metrics dataframe
2024-07-05 19:10:19,200:INFO:Uploading results into container
2024-07-05 19:10:19,200:INFO:Uploading model into container now
2024-07-05 19:10:19,202:INFO:_master_model_container: 8
2024-07-05 19:10:19,202:INFO:_display_container: 2
2024-07-05 19:10:19,203:INFO:BayesianRidge()
2024-07-05 19:10:19,203:INFO:create_model() successfully completed......................................
2024-07-05 19:10:19,352:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:19,352:INFO:Creating metrics dataframe
2024-07-05 19:10:19,356:INFO:Initializing Passive Aggressive Regressor
2024-07-05 19:10:19,356:INFO:Total runtime is 0.20950984557469687 minutes
2024-07-05 19:10:19,356:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:19,356:INFO:Initializing create_model()
2024-07-05 19:10:19,356:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:19,356:INFO:Checking exceptions
2024-07-05 19:10:19,356:INFO:Importing libraries
2024-07-05 19:10:19,356:INFO:Copying training dataset
2024-07-05 19:10:19,356:INFO:Defining folds
2024-07-05 19:10:19,356:INFO:Declaring metric variables
2024-07-05 19:10:19,362:INFO:Importing untrained model
2024-07-05 19:10:19,362:INFO:Passive Aggressive Regressor Imported successfully
2024-07-05 19:10:19,363:INFO:Starting cross validation
2024-07-05 19:10:19,364:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:19,630:INFO:Calculating mean and std
2024-07-05 19:10:19,631:INFO:Creating metrics dataframe
2024-07-05 19:10:19,632:INFO:Uploading results into container
2024-07-05 19:10:19,632:INFO:Uploading model into container now
2024-07-05 19:10:19,632:INFO:_master_model_container: 9
2024-07-05 19:10:19,632:INFO:_display_container: 2
2024-07-05 19:10:19,635:INFO:PassiveAggressiveRegressor(random_state=6424)
2024-07-05 19:10:19,635:INFO:create_model() successfully completed......................................
2024-07-05 19:10:19,769:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:19,769:INFO:Creating metrics dataframe
2024-07-05 19:10:19,769:INFO:Initializing Huber Regressor
2024-07-05 19:10:19,769:INFO:Total runtime is 0.21640213727951052 minutes
2024-07-05 19:10:19,769:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:19,774:INFO:Initializing create_model()
2024-07-05 19:10:19,774:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:19,774:INFO:Checking exceptions
2024-07-05 19:10:19,774:INFO:Importing libraries
2024-07-05 19:10:19,774:INFO:Copying training dataset
2024-07-05 19:10:19,774:INFO:Defining folds
2024-07-05 19:10:19,774:INFO:Declaring metric variables
2024-07-05 19:10:19,774:INFO:Importing untrained model
2024-07-05 19:10:19,774:INFO:Huber Regressor Imported successfully
2024-07-05 19:10:19,774:INFO:Starting cross validation
2024-07-05 19:10:19,774:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:19,993:WARNING:C:\Users\riddh\Desktop\DeepLearning\AutoML\env\lib\site-packages\sklearn\linear_model\_huber.py:342: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
2024-07-05 19:10:19,993:WARNING:C:\Users\riddh\Desktop\DeepLearning\AutoML\env\lib\site-packages\sklearn\linear_model\_huber.py:342: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
2024-07-05 19:10:19,993:WARNING:C:\Users\riddh\Desktop\DeepLearning\AutoML\env\lib\site-packages\sklearn\linear_model\_huber.py:342: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
2024-07-05 19:10:20,007:WARNING:C:\Users\riddh\Desktop\DeepLearning\AutoML\env\lib\site-packages\sklearn\linear_model\_huber.py:342: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
2024-07-05 19:10:20,054:INFO:Calculating mean and std
2024-07-05 19:10:20,055:INFO:Creating metrics dataframe
2024-07-05 19:10:20,057:INFO:Uploading results into container
2024-07-05 19:10:20,057:INFO:Uploading model into container now
2024-07-05 19:10:20,057:INFO:_master_model_container: 10
2024-07-05 19:10:20,057:INFO:_display_container: 2
2024-07-05 19:10:20,057:INFO:HuberRegressor()
2024-07-05 19:10:20,057:INFO:create_model() successfully completed......................................
2024-07-05 19:10:20,193:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:20,193:INFO:Creating metrics dataframe
2024-07-05 19:10:20,193:INFO:Initializing K Neighbors Regressor
2024-07-05 19:10:20,193:INFO:Total runtime is 0.2234671195348104 minutes
2024-07-05 19:10:20,193:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:20,193:INFO:Initializing create_model()
2024-07-05 19:10:20,193:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:20,193:INFO:Checking exceptions
2024-07-05 19:10:20,193:INFO:Importing libraries
2024-07-05 19:10:20,193:INFO:Copying training dataset
2024-07-05 19:10:20,202:INFO:Defining folds
2024-07-05 19:10:20,202:INFO:Declaring metric variables
2024-07-05 19:10:20,202:INFO:Importing untrained model
2024-07-05 19:10:20,202:INFO:K Neighbors Regressor Imported successfully
2024-07-05 19:10:20,202:INFO:Starting cross validation
2024-07-05 19:10:20,202:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:20,678:INFO:Calculating mean and std
2024-07-05 19:10:20,678:INFO:Creating metrics dataframe
2024-07-05 19:10:20,683:INFO:Uploading results into container
2024-07-05 19:10:20,684:INFO:Uploading model into container now
2024-07-05 19:10:20,684:INFO:_master_model_container: 11
2024-07-05 19:10:20,684:INFO:_display_container: 2
2024-07-05 19:10:20,684:INFO:KNeighborsRegressor(n_jobs=-1)
2024-07-05 19:10:20,684:INFO:create_model() successfully completed......................................
2024-07-05 19:10:20,818:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:20,818:INFO:Creating metrics dataframe
2024-07-05 19:10:20,823:INFO:Initializing Decision Tree Regressor
2024-07-05 19:10:20,823:INFO:Total runtime is 0.23396118481953943 minutes
2024-07-05 19:10:20,823:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:20,824:INFO:Initializing create_model()
2024-07-05 19:10:20,824:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:20,824:INFO:Checking exceptions
2024-07-05 19:10:20,824:INFO:Importing libraries
2024-07-05 19:10:20,824:INFO:Copying training dataset
2024-07-05 19:10:20,824:INFO:Defining folds
2024-07-05 19:10:20,824:INFO:Declaring metric variables
2024-07-05 19:10:20,824:INFO:Importing untrained model
2024-07-05 19:10:20,824:INFO:Decision Tree Regressor Imported successfully
2024-07-05 19:10:20,824:INFO:Starting cross validation
2024-07-05 19:10:20,824:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:21,062:INFO:Calculating mean and std
2024-07-05 19:10:21,064:INFO:Creating metrics dataframe
2024-07-05 19:10:21,066:INFO:Uploading results into container
2024-07-05 19:10:21,066:INFO:Uploading model into container now
2024-07-05 19:10:21,066:INFO:_master_model_container: 12
2024-07-05 19:10:21,066:INFO:_display_container: 2
2024-07-05 19:10:21,066:INFO:DecisionTreeRegressor(random_state=6424)
2024-07-05 19:10:21,066:INFO:create_model() successfully completed......................................
2024-07-05 19:10:21,200:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:21,200:INFO:Creating metrics dataframe
2024-07-05 19:10:21,200:INFO:Initializing Random Forest Regressor
2024-07-05 19:10:21,200:INFO:Total runtime is 0.2402449210484823 minutes
2024-07-05 19:10:21,200:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:21,200:INFO:Initializing create_model()
2024-07-05 19:10:21,200:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:21,200:INFO:Checking exceptions
2024-07-05 19:10:21,200:INFO:Importing libraries
2024-07-05 19:10:21,200:INFO:Copying training dataset
2024-07-05 19:10:21,208:INFO:Defining folds
2024-07-05 19:10:21,208:INFO:Declaring metric variables
2024-07-05 19:10:21,208:INFO:Importing untrained model
2024-07-05 19:10:21,208:INFO:Random Forest Regressor Imported successfully
2024-07-05 19:10:21,208:INFO:Starting cross validation
2024-07-05 19:10:21,213:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:22,021:INFO:Calculating mean and std
2024-07-05 19:10:22,021:INFO:Creating metrics dataframe
2024-07-05 19:10:22,021:INFO:Uploading results into container
2024-07-05 19:10:22,021:INFO:Uploading model into container now
2024-07-05 19:10:22,021:INFO:_master_model_container: 13
2024-07-05 19:10:22,021:INFO:_display_container: 2
2024-07-05 19:10:22,021:INFO:RandomForestRegressor(n_jobs=-1, random_state=6424)
2024-07-05 19:10:22,021:INFO:create_model() successfully completed......................................
2024-07-05 19:10:22,133:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:22,133:INFO:Creating metrics dataframe
2024-07-05 19:10:22,149:INFO:Initializing Extra Trees Regressor
2024-07-05 19:10:22,149:INFO:Total runtime is 0.2560630838076274 minutes
2024-07-05 19:10:22,149:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:22,149:INFO:Initializing create_model()
2024-07-05 19:10:22,149:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:22,149:INFO:Checking exceptions
2024-07-05 19:10:22,149:INFO:Importing libraries
2024-07-05 19:10:22,149:INFO:Copying training dataset
2024-07-05 19:10:22,149:INFO:Defining folds
2024-07-05 19:10:22,149:INFO:Declaring metric variables
2024-07-05 19:10:22,149:INFO:Importing untrained model
2024-07-05 19:10:22,149:INFO:Extra Trees Regressor Imported successfully
2024-07-05 19:10:22,149:INFO:Starting cross validation
2024-07-05 19:10:22,149:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:22,790:INFO:Calculating mean and std
2024-07-05 19:10:22,790:INFO:Creating metrics dataframe
2024-07-05 19:10:22,790:INFO:Uploading results into container
2024-07-05 19:10:22,790:INFO:Uploading model into container now
2024-07-05 19:10:22,790:INFO:_master_model_container: 14
2024-07-05 19:10:22,790:INFO:_display_container: 2
2024-07-05 19:10:22,790:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=6424)
2024-07-05 19:10:22,790:INFO:create_model() successfully completed......................................
2024-07-05 19:10:22,910:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:22,910:INFO:Creating metrics dataframe
2024-07-05 19:10:22,910:INFO:Initializing AdaBoost Regressor
2024-07-05 19:10:22,910:INFO:Total runtime is 0.26874147256215414 minutes
2024-07-05 19:10:22,910:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:22,910:INFO:Initializing create_model()
2024-07-05 19:10:22,910:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:22,910:INFO:Checking exceptions
2024-07-05 19:10:22,910:INFO:Importing libraries
2024-07-05 19:10:22,910:INFO:Copying training dataset
2024-07-05 19:10:22,910:INFO:Defining folds
2024-07-05 19:10:22,910:INFO:Declaring metric variables
2024-07-05 19:10:22,910:INFO:Importing untrained model
2024-07-05 19:10:22,910:INFO:AdaBoost Regressor Imported successfully
2024-07-05 19:10:22,925:INFO:Starting cross validation
2024-07-05 19:10:22,927:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:23,303:INFO:Calculating mean and std
2024-07-05 19:10:23,303:INFO:Creating metrics dataframe
2024-07-05 19:10:23,303:INFO:Uploading results into container
2024-07-05 19:10:23,303:INFO:Uploading model into container now
2024-07-05 19:10:23,303:INFO:_master_model_container: 15
2024-07-05 19:10:23,303:INFO:_display_container: 2
2024-07-05 19:10:23,303:INFO:AdaBoostRegressor(random_state=6424)
2024-07-05 19:10:23,303:INFO:create_model() successfully completed......................................
2024-07-05 19:10:23,435:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:23,435:INFO:Creating metrics dataframe
2024-07-05 19:10:23,440:INFO:Initializing Gradient Boosting Regressor
2024-07-05 19:10:23,440:INFO:Total runtime is 0.2775774081548055 minutes
2024-07-05 19:10:23,440:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:23,440:INFO:Initializing create_model()
2024-07-05 19:10:23,440:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:23,440:INFO:Checking exceptions
2024-07-05 19:10:23,440:INFO:Importing libraries
2024-07-05 19:10:23,440:INFO:Copying training dataset
2024-07-05 19:10:23,448:INFO:Defining folds
2024-07-05 19:10:23,448:INFO:Declaring metric variables
2024-07-05 19:10:23,448:INFO:Importing untrained model
2024-07-05 19:10:23,448:INFO:Gradient Boosting Regressor Imported successfully
2024-07-05 19:10:23,448:INFO:Starting cross validation
2024-07-05 19:10:23,451:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:24,102:INFO:Calculating mean and std
2024-07-05 19:10:24,102:INFO:Creating metrics dataframe
2024-07-05 19:10:24,102:INFO:Uploading results into container
2024-07-05 19:10:24,102:INFO:Uploading model into container now
2024-07-05 19:10:24,102:INFO:_master_model_container: 16
2024-07-05 19:10:24,102:INFO:_display_container: 2
2024-07-05 19:10:24,102:INFO:GradientBoostingRegressor(random_state=6424)
2024-07-05 19:10:24,102:INFO:create_model() successfully completed......................................
2024-07-05 19:10:24,214:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:24,214:INFO:Creating metrics dataframe
2024-07-05 19:10:24,230:INFO:Initializing Light Gradient Boosting Machine
2024-07-05 19:10:24,230:INFO:Total runtime is 0.29073939323425296 minutes
2024-07-05 19:10:24,230:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:24,230:INFO:Initializing create_model()
2024-07-05 19:10:24,230:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:24,230:INFO:Checking exceptions
2024-07-05 19:10:24,230:INFO:Importing libraries
2024-07-05 19:10:24,230:INFO:Copying training dataset
2024-07-05 19:10:24,230:INFO:Defining folds
2024-07-05 19:10:24,230:INFO:Declaring metric variables
2024-07-05 19:10:24,230:INFO:Importing untrained model
2024-07-05 19:10:24,230:INFO:Light Gradient Boosting Machine Imported successfully
2024-07-05 19:10:24,230:INFO:Starting cross validation
2024-07-05 19:10:24,230:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:25,605:INFO:Calculating mean and std
2024-07-05 19:10:25,605:INFO:Creating metrics dataframe
2024-07-05 19:10:25,610:INFO:Uploading results into container
2024-07-05 19:10:25,610:INFO:Uploading model into container now
2024-07-05 19:10:25,610:INFO:_master_model_container: 17
2024-07-05 19:10:25,610:INFO:_display_container: 2
2024-07-05 19:10:25,610:INFO:LGBMRegressor(n_jobs=-1, random_state=6424)
2024-07-05 19:10:25,610:INFO:create_model() successfully completed......................................
2024-07-05 19:10:25,748:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:25,748:INFO:Creating metrics dataframe
2024-07-05 19:10:25,748:INFO:Initializing Dummy Regressor
2024-07-05 19:10:25,748:INFO:Total runtime is 0.3160529096921285 minutes
2024-07-05 19:10:25,748:INFO:SubProcess create_model() called ==================================
2024-07-05 19:10:25,748:INFO:Initializing create_model()
2024-07-05 19:10:25,748:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, 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=<pycaret.internal.display.display.CommonDisplay object at 0x0000021ED2ABA220>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-07-05 19:10:25,748:INFO:Checking exceptions
2024-07-05 19:10:25,748:INFO:Importing libraries
2024-07-05 19:10:25,748:INFO:Copying training dataset
2024-07-05 19:10:25,748:INFO:Defining folds
2024-07-05 19:10:25,748:INFO:Declaring metric variables
2024-07-05 19:10:25,748:INFO:Importing untrained model
2024-07-05 19:10:25,748:INFO:Dummy Regressor Imported successfully
2024-07-05 19:10:25,748:INFO:Starting cross validation
2024-07-05 19:10:25,764:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-07-05 19:10:26,063:INFO:Calculating mean and std
2024-07-05 19:10:26,063:INFO:Creating metrics dataframe
2024-07-05 19:10:26,068:INFO:Uploading results into container
2024-07-05 19:10:26,068:INFO:Uploading model into container now
2024-07-05 19:10:26,068:INFO:_master_model_container: 18
2024-07-05 19:10:26,068:INFO:_display_container: 2
2024-07-05 19:10:26,068:INFO:DummyRegressor()
2024-07-05 19:10:26,068:INFO:create_model() successfully completed......................................
2024-07-05 19:10:26,210:INFO:SubProcess create_model() end ==================================
2024-07-05 19:10:26,210:INFO:Creating metrics dataframe
2024-07-05 19:10:26,218:INFO:Initializing create_model()
2024-07-05 19:10:26,218:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x0000021ED0199250>, estimator=LGBMRegressor(n_jobs=-1, random_state=6424), 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={})
2024-07-05 19:10:26,218:INFO:Checking exceptions
2024-07-05 19:10:26,218:INFO:Importing libraries
2024-07-05 19:10:26,218:INFO:Copying training dataset
2024-07-05 19:10:26,218:INFO:Defining folds
2024-07-05 19:10:26,218:INFO:Declaring metric variables
2024-07-05 19:10:26,218:INFO:Importing untrained model
2024-07-05 19:10:26,218:INFO:Declaring custom model
2024-07-05 19:10:26,218:INFO:Light Gradient Boosting Machine Imported successfully
2024-07-05 19:10:26,218:INFO:Cross validation set to False
2024-07-05 19:10:26,218:INFO:Fitting Model
2024-07-05 19:10:26,299:INFO:[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000259 seconds.
2024-07-05 19:10:26,299:INFO:You can set `force_col_wise=true` to remove the overhead.
2024-07-05 19:10:26,299:INFO:[LightGBM] [Info] Total Bins 1141
2024-07-05 19:10:26,299:INFO:[LightGBM] [Info] Number of data points in the train set: 1050, number of used features: 29
2024-07-05 19:10:26,299:INFO:[LightGBM] [Info] Start training from score 0.144762
2024-07-05 19:10:26,299:INFO:[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
2024-07-05 19:10:26,503:INFO:LGBMRegressor(n_jobs=-1, random_state=6424)
2024-07-05 19:10:26,503:INFO:create_model() successfully completed......................................
2024-07-05 19:10:26,672:INFO:_master_model_container: 18
2024-07-05 19:10:26,672:INFO:_display_container: 2
2024-07-05 19:10:26,672:INFO:LGBMRegressor(n_jobs=-1, random_state=6424)
2024-07-05 19:10:26,672:INFO:compare_models() successfully completed......................................
2024-07-05 19:10:26,720:INFO:Initializing save_model()
2024-07-05 19:10:26,720:INFO:save_model(model=LGBMRegressor(n_jobs=-1, random_state=6424), model_name=best_model.pkl, prep_pipe_=Pipeline(memory=FastMemory(location=C:\Users\riddh\AppData\Local\Temp\joblib),
steps=[('numerical_imputer',
TransformerWrapper(include=['CustomerID', 'Age', 'Income',
'AverageSpend', 'GroupSize',
'OnlineReservation',
'DeliveryOrder',
'LoyaltyProgramMember', 'WaitTime',
'ServiceRating', 'FoodRating',
'AmbianceRating'],
transformer=SimpleImputer())),
('categorical_...
{'col': 'MealType',
'data_type': dtype('O'),
'mapping': Dine-in 0
Takeaway 1
NaN -1
dtype: int64}]))),
('onehot_encoding',
TransformerWrapper(include=['VisitFrequency',
'PreferredCuisine', 'TimeOfVisit',
'DiningOccasion'],
transformer=OneHotEncoder(cols=['VisitFrequency',
'PreferredCuisine',
'TimeOfVisit',
'DiningOccasion'],
handle_missing='return_nan',
use_cat_names=True)))]), verbose=True, use_case=MLUsecase.REGRESSION, kwargs={})
2024-07-05 19:10:26,720:INFO:Adding model into prep_pipe
2024-07-05 19:10:26,733:INFO:best_model.pkl.pkl saved in current working directory
2024-07-05 19:10:26,781:INFO:Pipeline(memory=Memory(location=None),
steps=[('numerical_imputer',
TransformerWrapper(include=['CustomerID', 'Age', 'Income',
'AverageSpend', 'GroupSize',
'OnlineReservation',
'DeliveryOrder',
'LoyaltyProgramMember', 'WaitTime',
'ServiceRating', 'FoodRating',
'AmbianceRating'],
transformer=SimpleImputer())),
('categorical_imputer',
TransformerWrapper(include=['Ge...
Takeaway 1
NaN -1
dtype: int64}]))),
('onehot_encoding',
TransformerWrapper(include=['VisitFrequency',
'PreferredCuisine', 'TimeOfVisit',
'DiningOccasion'],
transformer=OneHotEncoder(cols=['VisitFrequency',
'PreferredCuisine',
'TimeOfVisit',
'DiningOccasion'],
handle_missing='return_nan',
use_cat_names=True))),
('trained_model', LGBMRegressor(n_jobs=-1, random_state=6424))])
2024-07-05 19:10:26,781:INFO:save_model() successfully completed......................................
2024-07-05 19:10:44,578:INFO:PyCaret RegressionExperiment
2024-07-05 19:10:44,578:INFO:Logging name: reg-default-name
2024-07-05 19:10:44,578:INFO:ML Usecase: MLUsecase.REGRESSION
2024-07-05 19:10:44,578:INFO:version 3.3.2
2024-07-05 19:10:44,578:INFO:Initializing setup()
2024-07-05 19:10:44,578:INFO:self.USI: 30ca
2024-07-05 19:10:44,578:INFO:self._variable_keys: {'exp_name_log', 'USI', 'exp_id', 'fold_generator', 'pipeline', 'X_train', 'X_test', 'data', 'idx', 'gpu_param', 'y', '_available_plots', '_ml_usecase', 'html_param', 'fold_shuffle_param', 'n_jobs_param', 'X', 'y_train', 'fold_groups_param', 'transform_target_param', 'memory', 'seed', 'logging_param', 'y_test', 'target_param', 'gpu_n_jobs_param', 'log_plots_param'}
2024-07-05 19:10:44,578:INFO:Checking environment
2024-07-05 19:10:44,578:INFO:python_version: 3.9.19
2024-07-05 19:10:44,578:INFO:python_build: ('main', 'May 6 2024 20:12:36')
2024-07-05 19:10:44,578:INFO:machine: AMD64
2024-07-05 19:10:44,578:INFO:platform: Windows-10-10.0.22631-SP0
2024-07-05 19:10:44,589:INFO:Memory: svmem(total=7887364096, available=415453184, percent=94.7, used=7471910912, free=415453184)
2024-07-05 19:10:44,589:INFO:Physical Core: 6
2024-07-05 19:10:44,589:INFO:Logical Core: 12
2024-07-05 19:10:44,589:INFO:Checking libraries
2024-07-05 19:10:44,589:INFO:System:
2024-07-05 19:10:44,589:INFO: python: 3.9.19 (main, May 6 2024, 20:12:36) [MSC v.1916 64 bit (AMD64)]
2024-07-05 19:10:44,589:INFO:executable: C:\Users\riddh\Desktop\DeepLearning\AutoML\env\python.exe
2024-07-05 19:10:44,589:INFO: machine: Windows-10-10.0.22631-SP0
2024-07-05 19:10:44,589:INFO:PyCaret required dependencies:
2024-07-05 19:10:44,589:INFO: pip: 24.1.1
2024-07-05 19:10:44,589:INFO: setuptools: 70.2.0
2024-07-05 19:10:44,589:INFO: pycaret: 3.3.2
2024-07-05 19:10:44,589:INFO: IPython: 8.18.1
2024-07-05 19:10:44,589:INFO: ipywidgets: 8.1.3
2024-07-05 19:10:44,589:INFO: tqdm: 4.64.1
2024-07-05 19:10:44,589:INFO: numpy: 1.23.5
2024-07-05 19:10:44,589:INFO: pandas: 1.5.3
2024-07-05 19:10:44,589:INFO: jinja2: 3.1.4
2024-07-05 19:10:44,593:INFO: scipy: 1.9.3
2024-07-05 19:10:44,593:INFO: joblib: 1.3.2
2024-07-05 19:10:44,593:INFO: sklearn: 1.4.2
2024-07-05 19:10:44,593:INFO: pyod: 2.0.1
2024-07-05 19:10:44,593:INFO: imblearn: 0.12.3
2024-07-05 19:10:44,593:INFO: category_encoders: 2.6.3
2024-07-05 19:10:44,593:INFO: lightgbm: 4.4.0
2024-07-05 19:10:44,593:INFO: numba: 0.58.1
2024-07-05 19:10:44,593:INFO: requests: 2.28.2
2024-07-05 19:10:44,593:INFO: matplotlib: 3.6.3
2024-07-05 19:10:44,593:INFO: scikitplot: 0.3.7
2024-07-05 19:10:44,593:INFO: yellowbrick: 1.5
2024-07-05 19:10:44,593:INFO: plotly: 5.22.0
2024-07-05 19:10:44,593:INFO: plotly-resampler: Not installed
2024-07-05 19:10:44,593:INFO: kaleido: 0.2.1
2024-07-05 19:10:44,593:INFO: schemdraw: 0.15
2024-07-05 19:10:44,593:INFO: statsmodels: 0.13.5
2024-07-05 19:10:44,593:INFO: sktime: 0.26.0
2024-07-05 19:10:44,593:INFO: tbats: 1.1.3
2024-07-05 19:10:44,593:INFO: pmdarima: 2.0.4
2024-07-05 19:10:44,593:INFO: psutil: 6.0.0
2024-07-05 19:10:44,593:INFO: markupsafe: 2.1.5
2024-07-05 19:10:44,593:INFO: pickle5: Not installed
2024-07-05 19:10:44,593:INFO: cloudpickle: 3.0.0
2024-07-05 19:10:44,593:INFO: deprecation: 2.1.0
2024-07-05 19:10:44,593:INFO: xxhash: 3.4.1
2024-07-05 19:10:44,593:INFO: wurlitzer: Not installed
2024-07-05 19:10:44,593:INFO:PyCaret optional dependencies:
2024-07-05 19:10:44,593:INFO: shap: Not installed
2024-07-05 19:10:44,593:INFO: interpret: Not installed
2024-07-05 19:10:44,593:INFO: umap: Not installed
2024-07-05 19:10:44,593:INFO: ydata_profiling: 4.0.0
2024-07-05 19:10:44,593:INFO: explainerdashboard: Not installed
2024-07-05 19:10:44,593:INFO: autoviz: Not installed
2024-07-05 19:10:44,593:INFO: fairlearn: Not installed
2024-07-05 19:10:44,593:INFO: deepchecks: Not installed
2024-07-05 19:10:44,593:INFO: xgboost: Not installed
2024-07-05 19:10:44,593:INFO: catboost: Not installed
2024-07-05 19:10:44,593:INFO: kmodes: Not installed
2024-07-05 19:10:44,593:INFO: mlxtend: Not installed
2024-07-05 19:10:44,593:INFO: statsforecast: Not installed
2024-07-05 19:10:44,593:INFO: tune_sklearn: Not installed
2024-07-05 19:10:44,593:INFO: ray: Not installed
2024-07-05 19:10:44,593:INFO: hyperopt: Not installed
2024-07-05 19:10:44,593:INFO: optuna: Not installed
2024-07-05 19:10:44,593:INFO: skopt: Not installed
2024-07-05 19:10:44,593:INFO: mlflow: Not installed
2024-07-05 19:10:44,593:INFO: gradio: Not installed
2024-07-05 19:10:44,593:INFO: fastapi: Not installed
2024-07-05 19:10:44,593:INFO: uvicorn: Not installed
2024-07-05 19:10:44,593:INFO: m2cgen: Not installed
2024-07-05 19:10:44,593:INFO: evidently: Not installed
2024-07-05 19:10:44,593:INFO: fugue: Not installed
2024-07-05 19:10:44,593:INFO: streamlit: 1.36.0
2024-07-05 19:10:44,593:INFO: prophet: Not installed
2024-07-05 19:10:44,593:INFO:None
2024-07-05 19:10:44,593:INFO:Set up data.
2024-07-05 19:10:44,607:INFO:Set up folding strategy.
2024-07-05 19:10:44,607:INFO:Set up train/test split.
2024-07-05 19:10:44,615:INFO:Set up index.
2024-07-05 19:10:44,615:INFO:Assigning column types.
2024-07-05 19:10:44,615:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2024-07-05 19:10:44,615:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None.
2024-07-05 19:10:44,631:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
2024-07-05 19:10:44,631:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2024-07-05 19:10:44,725:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:44,788:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:44,788:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:44,789: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]`
2024-07-05 19:10:44,790:INFO:Engine for model 'lasso' has not been set explicitly, hence returning None.
2024-07-05 19:10:44,796:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
2024-07-05 19:10:44,800:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2024-07-05 19:10:44,886:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:44,949:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:44,951:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:44,951: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]`
2024-07-05 19:10:44,951:INFO:Engine successfully changes for model 'lasso' to 'sklearn'.
2024-07-05 19:10:44,957:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
2024-07-05 19:10:44,965:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,029:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,101:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,101:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:45,101: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]`
2024-07-05 19:10:45,101:INFO:Engine for model 'ridge' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,115:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,195:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,259:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,259:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:45,259: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]`
2024-07-05 19:10:45,259:INFO:Engine successfully changes for model 'ridge' to 'sklearn'.
2024-07-05 19:10:45,259:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,345:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,402:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,402:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:45,402: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]`
2024-07-05 19:10:45,419:INFO:Engine for model 'en' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,505:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,577:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,577:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:45,577: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]`
2024-07-05 19:10:45,577:INFO:Engine successfully changes for model 'en' to 'sklearn'.
2024-07-05 19:10:45,693:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,764:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,764:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:45,764: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]`
2024-07-05 19:10:45,852:INFO:Engine for model 'svm' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,915:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-07-05 19:10:45,915:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-07-05 19:10:45,915:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.