2022-08-30 16:46:24,383:INFO: lightgbm: 3.3.2
2022-08-30 16:46:24,383:INFO: numba: 0.55.2
2022-08-30 16:46:24,383:INFO: requests: 2.28.1
2022-08-30 16:46:24,383:INFO: matplotlib: 3.5.3
2022-08-30 16:46:24,383:INFO: scikitplot: 0.3.7
2022-08-30 16:46:24,383:INFO: yellowbrick: 1.4
2022-08-30 16:46:24,383:INFO: plotly: 5.10.0
2022-08-30 16:46:24,383:INFO: kaleido: 0.2.1
2022-08-30 16:46:24,383:INFO: statsmodels: 0.13.2
2022-08-30 16:46:24,383:INFO: sktime: 0.13.1
2022-08-30 16:46:24,383:INFO: tbats: Installed but version unavailable
2022-08-30 16:46:24,383:INFO: pmdarima: 1.8.5
2022-08-30 16:46:24,383:INFO: psutil: 5.9.1
2022-08-30 16:46:24,383:INFO:None
2022-08-30 16:46:24,383:INFO:Set up data.
2022-08-30 16:46:24,394:INFO:Set up train/test split.
2022-08-30 16:46:24,397:INFO:Set up folding strategy.
2022-08-30 16:46:24,397:INFO:Assigning column types.
2022-08-30 16:46:24,399:INFO:Preparing preprocessing pipeline...
2022-08-30 16:46:24,399:INFO:Set up simple imputation.
2022-08-30 16:46:24,433:INFO:Finished creating preprocessing pipeline.
2022-08-30 16:46:24,435:INFO:Pipeline: Pipeline(memory=Memory(location=/tmp/joblib),
steps=[('numerical_imputer',
TransformerWrapper(include=['20aa07010', '87ffda550', '63c094ba4', '0572565c2', '6619d81fc', 'aca228668', '6eef030c1', 'bc70cbc26', 'e222309b0', 'bb1113dbb', 'df838756c', '024c577b9', 'ba5bbaffc', '134ac90df', '2155f5e16', '41bc25fef', '58e056e12', 'f8b733d3f', '241f0f867', '1931ccfdd', 'c07f4dab...', '122c135ed', 'aeff360c7', 'eeb9cd3aa', '58232a6fb', 'd6bb78916', 'adb64ff71', '15ace8c9f', 'f190486d6', 'f74e8f13d', '5d3b81ef8', 'f514fdb2e', 'cb7ecfc41', '9d5c7cb94', 'c5a231d81', 'e176a204a', '1702b5bf0'], transformer=SimpleImputer())),
('categorical_imputer',
TransformerWrapper(include=[], transformer=SimpleImputer(fill_value='constant', strategy='constant')))])
2022-08-30 16:46:24,435:INFO:Creating final display dataframe.
2022-08-30 16:46:24,465:INFO:Setup display_container: Description Value
0 Session id 3983
1 Target target
2 Target type Regression
3 Data shape (1784, 50)
4 Train data shape (1248, 50)
5 Test data shape (536, 50)
6 Numeric features 49
7 Preprocess True
8 Imputation type simple
9 Numeric imputation mean
10 Categorical imputation constant
11 Fold Generator KFold
12 Fold Number 10
13 CPU Jobs -1
14 Use GPU False
15 Log Experiment False
16 Experiment Name reg-default-name
17 USI 061e
2022-08-30 16:46:24,569:WARNING:No module named 'xgboost'.
'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]`
2022-08-30 16:46:24,569:WARNING:No module named 'catboost'.
'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]`
2022-08-30 16:46:24,654:WARNING:No module named 'xgboost'.
'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]`
2022-08-30 16:46:24,654:WARNING:No module named 'catboost'.
'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]`
2022-08-30 16:46:24,659:INFO:setup() successfully completed in 0.49s...............
2022-08-30 16:46:24,659:INFO:Initializing compare_models()
2022-08-30 16:46:24,659:INFO:compare_models(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, 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 0x7f2b3a50d9c0>, 'include': None, 'exclude': ['lr', 'svm', 'rbfsvm', 'dt', 'rf', 'lightgbm', 'lasso'], '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, 'verbose': True, 'parallel': None, '__class__': <class 'pycaret.regression.oop.RegressionExperiment'>}, exclude=['lr', 'svm', 'rbfsvm', 'dt', 'rf', 'lightgbm', 'lasso'])
2022-08-30 16:46:24,659:INFO:Checking exceptions
2022-08-30 16:46:24,660:INFO:Preparing display monitor
2022-08-30 16:46:24,663:INFO:Initializing Ridge Regression
2022-08-30 16:46:24,663:INFO:Total runtime is 1.4146169026692709e-06 minutes
2022-08-30 16:46:24,663:INFO:SubProcess create_model() called ==================================
2022-08-30 16:46:24,664:INFO:Initializing create_model()
2022-08-30 16:46:24,664:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, 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 0x7f2b3a50e110>, return_train_score=False, kwargs={})
2022-08-30 16:46:24,664:INFO:Checking exceptions
2022-08-30 16:46:24,665:INFO:Importing libraries
2022-08-30 16:46:24,665:INFO:Copying training dataset
2022-08-30 16:46:24,667:INFO:Defining folds
2022-08-30 16:46:24,667:INFO:Declaring metric variables
2022-08-30 16:46:24,667:INFO:Importing untrained model
2022-08-30 16:46:24,668:INFO:Ridge Regression Imported successfully
2022-08-30 16:46:24,668:INFO:Starting cross validation
2022-08-30 16:46:24,668:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-08-30 16:46:26,505:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:26,506:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,057:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,068:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,154:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,229:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,239:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,320:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,321:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,341:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,349:INFO:Calculating mean and std
2022-08-30 16:46:27,350:INFO:Creating metrics dataframe
2022-08-30 16:46:27,357:INFO:Uploading results into container
2022-08-30 16:46:27,359:INFO:Uploading model into container now
2022-08-30 16:46:27,359:INFO:master_model_container: 1
2022-08-30 16:46:27,359:INFO:display_container: 2
2022-08-30 16:46:27,360:INFO:Ridge(random_state=3983)
2022-08-30 16:46:27,360:INFO:create_model() successfully completed......................................
2022-08-30 16:46:27,464:INFO:SubProcess create_model() end ==================================
2022-08-30 16:46:27,464:INFO:Creating metrics dataframe
2022-08-30 16:46:27,468:INFO:Initializing Elastic Net
2022-08-30 16:46:27,468:INFO:Total runtime is 0.04675155083338419 minutes
2022-08-30 16:46:27,468:INFO:SubProcess create_model() called ==================================
2022-08-30 16:46:27,469:INFO:Initializing create_model()
2022-08-30 16:46:27,469:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, 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 0x7f2b3a50e110>, return_train_score=False, kwargs={})
2022-08-30 16:46:27,469:INFO:Checking exceptions
2022-08-30 16:46:27,470:INFO:Importing libraries
2022-08-30 16:46:27,470:INFO:Copying training dataset
2022-08-30 16:46:27,473:INFO:Defining folds
2022-08-30 16:46:27,473:INFO:Declaring metric variables
2022-08-30 16:46:27,473:INFO:Importing untrained model
2022-08-30 16:46:27,474:INFO:Elastic Net Imported successfully
2022-08-30 16:46:27,474:INFO:Starting cross validation
2022-08-30 16:46:27,475:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-08-30 16:46:27,609:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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: 2.807e+16, tolerance: 7.358e+12
model = cd_fast.enet_coordinate_descent(
2022-08-30 16:46:27,610:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,617:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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: 2.794e+16, tolerance: 7.135e+12
model = cd_fast.enet_coordinate_descent(
2022-08-30 16:46:27,618:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,620:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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: 2.736e+16, tolerance: 7.091e+12
model = cd_fast.enet_coordinate_descent(
2022-08-30 16:46:27,620:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,622:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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: 2.739e+16, tolerance: 7.041e+12
model = cd_fast.enet_coordinate_descent(
2022-08-30 16:46:27,622:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,623:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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: 2.808e+16, tolerance: 7.245e+12
model = cd_fast.enet_coordinate_descent(
2022-08-30 16:46:27,624:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,638:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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: 2.702e+16, tolerance: 7.095e+12
model = cd_fast.enet_coordinate_descent(
2022-08-30 16:46:27,638:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,639:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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: 2.846e+16, tolerance: 7.289e+12
model = cd_fast.enet_coordinate_descent(
2022-08-30 16:46:27,639:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:27,639:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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: 2.606e+16, tolerance: 7.030e+12
model = cd_fast.enet_coordinate_descent(
2022-08-30 16:46:27,640:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,588:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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: 2.748e+16, tolerance: 7.265e+12
model = cd_fast.enet_coordinate_descent(
2022-08-30 16:46:28,588:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,589:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:648: 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: 2.724e+16, tolerance: 6.791e+12
model = cd_fast.enet_coordinate_descent(
2022-08-30 16:46:28,589:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,596:INFO:Calculating mean and std
2022-08-30 16:46:28,597:INFO:Creating metrics dataframe
2022-08-30 16:46:28,600:INFO:Uploading results into container
2022-08-30 16:46:28,601:INFO:Uploading model into container now
2022-08-30 16:46:28,601:INFO:master_model_container: 2
2022-08-30 16:46:28,601:INFO:display_container: 2
2022-08-30 16:46:28,602:INFO:ElasticNet(random_state=3983)
2022-08-30 16:46:28,602:INFO:create_model() successfully completed......................................
2022-08-30 16:46:28,710:INFO:SubProcess create_model() end ==================================
2022-08-30 16:46:28,710:INFO:Creating metrics dataframe
2022-08-30 16:46:28,716:INFO:Initializing Least Angle Regression
2022-08-30 16:46:28,716:INFO:Total runtime is 0.06755028963088988 minutes
2022-08-30 16:46:28,716:INFO:SubProcess create_model() called ==================================
2022-08-30 16:46:28,717:INFO:Initializing create_model()
2022-08-30 16:46:28,717:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, 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 0x7f2b3a50e110>, return_train_score=False, kwargs={})
2022-08-30 16:46:28,717:INFO:Checking exceptions
2022-08-30 16:46:28,719:INFO:Importing libraries
2022-08-30 16:46:28,719:INFO:Copying training dataset
2022-08-30 16:46:28,722:INFO:Defining folds
2022-08-30 16:46:28,722:INFO:Declaring metric variables
2022-08-30 16:46:28,722:INFO:Importing untrained model
2022-08-30 16:46:28,722:INFO:Least Angle Regression Imported successfully
2022-08-30 16:46:28,722:INFO:Starting cross validation
2022-08-30 16:46:28,723:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-08-30 16:46:28,750:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), Lars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:28,754:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), Lars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:28,758:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), Lars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:28,763:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,763:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), Lars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:28,768:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), Lars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:28,768:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,774:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,779:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,783:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,792:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), Lars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:28,795:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), Lars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:28,795:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), Lars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:28,799:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), Lars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:28,804:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), Lars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:28,805:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,810:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,811:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,812:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,813:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,822:INFO:Calculating mean and std
2022-08-30 16:46:28,823:INFO:Creating metrics dataframe
2022-08-30 16:46:28,826:INFO:Uploading results into container
2022-08-30 16:46:28,826:INFO:Uploading model into container now
2022-08-30 16:46:28,827:INFO:master_model_container: 3
2022-08-30 16:46:28,827:INFO:display_container: 2
2022-08-30 16:46:28,827:INFO:Lars(random_state=3983)
2022-08-30 16:46:28,827:INFO:create_model() successfully completed......................................
2022-08-30 16:46:28,915:INFO:SubProcess create_model() end ==================================
2022-08-30 16:46:28,915:INFO:Creating metrics dataframe
2022-08-30 16:46:28,920:INFO:Initializing Lasso Least Angle Regression
2022-08-30 16:46:28,920:INFO:Total runtime is 0.07094737688700357 minutes
2022-08-30 16:46:28,920:INFO:SubProcess create_model() called ==================================
2022-08-30 16:46:28,920:INFO:Initializing create_model()
2022-08-30 16:46:28,920:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, 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 0x7f2b3a50e110>, return_train_score=False, kwargs={})
2022-08-30 16:46:28,920:INFO:Checking exceptions
2022-08-30 16:46:28,922:INFO:Importing libraries
2022-08-30 16:46:28,922:INFO:Copying training dataset
2022-08-30 16:46:28,923:INFO:Defining folds
2022-08-30 16:46:28,924:INFO:Declaring metric variables
2022-08-30 16:46:28,924:INFO:Importing untrained model
2022-08-30 16:46:28,924:INFO:Lasso Least Angle Regression Imported successfully
2022-08-30 16:46:28,924:INFO:Starting cross validation
2022-08-30 16:46:28,925:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-08-30 16:46:28,950:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LassoLars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
Set parameter alpha to: original_alpha * np.sqrt(n_samples).
warnings.warn(
2022-08-30 16:46:28,958:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LassoLars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
Set parameter alpha to: original_alpha * np.sqrt(n_samples).
warnings.warn(
2022-08-30 16:46:28,962:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LassoLars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
Set parameter alpha to: original_alpha * np.sqrt(n_samples).
warnings.warn(
2022-08-30 16:46:28,964:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,970:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LassoLars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
Set parameter alpha to: original_alpha * np.sqrt(n_samples).
warnings.warn(
2022-08-30 16:46:28,973:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LassoLars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
Set parameter alpha to: original_alpha * np.sqrt(n_samples).
warnings.warn(
2022-08-30 16:46:28,974:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LassoLars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
Set parameter alpha to: original_alpha * np.sqrt(n_samples).
warnings.warn(
2022-08-30 16:46:28,976:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,978:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LassoLars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
Set parameter alpha to: original_alpha * np.sqrt(n_samples).
warnings.warn(
2022-08-30 16:46:28,978:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,984:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,986:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,987:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,988:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LassoLars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
Set parameter alpha to: original_alpha * np.sqrt(n_samples).
warnings.warn(
2022-08-30 16:46:28,990:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:28,997:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LassoLars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
Set parameter alpha to: original_alpha * np.sqrt(n_samples).
warnings.warn(
2022-08-30 16:46:28,999:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), LassoLars())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
Set parameter alpha to: original_alpha * np.sqrt(n_samples).
warnings.warn(
2022-08-30 16:46:29,002:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,006:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,010:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,017:INFO:Calculating mean and std
2022-08-30 16:46:29,017:INFO:Creating metrics dataframe
2022-08-30 16:46:29,020:INFO:Uploading results into container
2022-08-30 16:46:29,021:INFO:Uploading model into container now
2022-08-30 16:46:29,021:INFO:master_model_container: 4
2022-08-30 16:46:29,021:INFO:display_container: 2
2022-08-30 16:46:29,021:INFO:LassoLars(random_state=3983)
2022-08-30 16:46:29,021:INFO:create_model() successfully completed......................................
2022-08-30 16:46:29,103:INFO:SubProcess create_model() end ==================================
2022-08-30 16:46:29,103:INFO:Creating metrics dataframe
2022-08-30 16:46:29,107:INFO:Initializing Orthogonal Matching Pursuit
2022-08-30 16:46:29,107:INFO:Total runtime is 0.0740662654240926 minutes
2022-08-30 16:46:29,107:INFO:SubProcess create_model() called ==================================
2022-08-30 16:46:29,107:INFO:Initializing create_model()
2022-08-30 16:46:29,107:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, 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 0x7f2b3a50e110>, return_train_score=False, kwargs={})
2022-08-30 16:46:29,107:INFO:Checking exceptions
2022-08-30 16:46:29,108:INFO:Importing libraries
2022-08-30 16:46:29,109:INFO:Copying training dataset
2022-08-30 16:46:29,110:INFO:Defining folds
2022-08-30 16:46:29,110:INFO:Declaring metric variables
2022-08-30 16:46:29,110:INFO:Importing untrained model
2022-08-30 16:46:29,111:INFO:Orthogonal Matching Pursuit Imported successfully
2022-08-30 16:46:29,111:INFO:Starting cross validation
2022-08-30 16:46:29,111:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-08-30 16:46:29,135:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), OrthogonalMatchingPursuit())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:29,138:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,145:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), OrthogonalMatchingPursuit())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:29,146:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), OrthogonalMatchingPursuit())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:29,151:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,151:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), OrthogonalMatchingPursuit())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:29,152:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,155:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), OrthogonalMatchingPursuit())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:29,155:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,158:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), OrthogonalMatchingPursuit())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:29,160:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,161:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), OrthogonalMatchingPursuit())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:29,162:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), OrthogonalMatchingPursuit())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:29,162:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,165:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,165:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,168:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), OrthogonalMatchingPursuit())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:29,172:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,172:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:
from sklearn.pipeline import make_pipeline
model = make_pipeline(StandardScaler(with_mean=False), OrthogonalMatchingPursuit())
If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:
kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)
warnings.warn(
2022-08-30 16:46:29,175:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,180:INFO:Calculating mean and std
2022-08-30 16:46:29,180:INFO:Creating metrics dataframe
2022-08-30 16:46:29,184:INFO:Uploading results into container
2022-08-30 16:46:29,184:INFO:Uploading model into container now
2022-08-30 16:46:29,185:INFO:master_model_container: 5
2022-08-30 16:46:29,185:INFO:display_container: 2
2022-08-30 16:46:29,185:INFO:OrthogonalMatchingPursuit()
2022-08-30 16:46:29,185:INFO:create_model() successfully completed......................................
2022-08-30 16:46:29,265:INFO:SubProcess create_model() end ==================================
2022-08-30 16:46:29,266:INFO:Creating metrics dataframe
2022-08-30 16:46:29,269:INFO:Initializing Bayesian Ridge
2022-08-30 16:46:29,269:INFO:Total runtime is 0.07677255471547444 minutes
2022-08-30 16:46:29,269:INFO:SubProcess create_model() called ==================================
2022-08-30 16:46:29,270:INFO:Initializing create_model()
2022-08-30 16:46:29,270:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, 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 0x7f2b3a50e110>, return_train_score=False, kwargs={})
2022-08-30 16:46:29,270:INFO:Checking exceptions
2022-08-30 16:46:29,271:INFO:Importing libraries
2022-08-30 16:46:29,271:INFO:Copying training dataset
2022-08-30 16:46:29,273:INFO:Defining folds
2022-08-30 16:46:29,273:INFO:Declaring metric variables
2022-08-30 16:46:29,274:INFO:Importing untrained model
2022-08-30 16:46:29,274:INFO:Bayesian Ridge Imported successfully
2022-08-30 16:46:29,274:INFO:Starting cross validation
2022-08-30 16:46:29,275:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-08-30 16:46:29,307:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,318:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,324:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,324:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,329:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,331:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,334:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,334:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,338:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,343:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,352:INFO:Calculating mean and std
2022-08-30 16:46:29,353:INFO:Creating metrics dataframe
2022-08-30 16:46:29,357:INFO:Uploading results into container
2022-08-30 16:46:29,358:INFO:Uploading model into container now
2022-08-30 16:46:29,358:INFO:master_model_container: 6
2022-08-30 16:46:29,358:INFO:display_container: 2
2022-08-30 16:46:29,359:INFO:BayesianRidge()
2022-08-30 16:46:29,359:INFO:create_model() successfully completed......................................
2022-08-30 16:46:29,466:INFO:SubProcess create_model() end ==================================
2022-08-30 16:46:29,466:INFO:Creating metrics dataframe
2022-08-30 16:46:29,471:INFO:Initializing Passive Aggressive Regressor
2022-08-30 16:46:29,471:INFO:Total runtime is 0.08013484477996825 minutes
2022-08-30 16:46:29,472:INFO:SubProcess create_model() called ==================================
2022-08-30 16:46:29,472:INFO:Initializing create_model()
2022-08-30 16:46:29,472:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, 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 0x7f2b3a50e110>, return_train_score=False, kwargs={})
2022-08-30 16:46:29,472:INFO:Checking exceptions
2022-08-30 16:46:29,474:INFO:Importing libraries
2022-08-30 16:46:29,474:INFO:Copying training dataset
2022-08-30 16:46:29,476:INFO:Defining folds
2022-08-30 16:46:29,477:INFO:Declaring metric variables
2022-08-30 16:46:29,477:INFO:Importing untrained model
2022-08-30 16:46:29,477:INFO:Passive Aggressive Regressor Imported successfully
2022-08-30 16:46:29,478:INFO:Starting cross validation
2022-08-30 16:46:29,478:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-08-30 16:46:29,509:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,514:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,519:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,522:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,531:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,532:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,541:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,543:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,550:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,554:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,561:INFO:Calculating mean and std
2022-08-30 16:46:29,562:INFO:Creating metrics dataframe
2022-08-30 16:46:29,565:INFO:Uploading results into container
2022-08-30 16:46:29,566:INFO:Uploading model into container now
2022-08-30 16:46:29,566:INFO:master_model_container: 7
2022-08-30 16:46:29,566:INFO:display_container: 2
2022-08-30 16:46:29,566:INFO:PassiveAggressiveRegressor(random_state=3983)
2022-08-30 16:46:29,566:INFO:create_model() successfully completed......................................
2022-08-30 16:46:29,674:INFO:SubProcess create_model() end ==================================
2022-08-30 16:46:29,675:INFO:Creating metrics dataframe
2022-08-30 16:46:29,678:INFO:Initializing Huber Regressor
2022-08-30 16:46:29,679:INFO:Total runtime is 0.08359276056289672 minutes
2022-08-30 16:46:29,679:INFO:SubProcess create_model() called ==================================
2022-08-30 16:46:29,679:INFO:Initializing create_model()
2022-08-30 16:46:29,679:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, 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 0x7f2b3a50e110>, return_train_score=False, kwargs={})
2022-08-30 16:46:29,679:INFO:Checking exceptions
2022-08-30 16:46:29,680:INFO:Importing libraries
2022-08-30 16:46:29,680:INFO:Copying training dataset
2022-08-30 16:46:29,683:INFO:Defining folds
2022-08-30 16:46:29,683:INFO:Declaring metric variables
2022-08-30 16:46:29,683:INFO:Importing untrained model
2022-08-30 16:46:29,683:INFO:Huber Regressor Imported successfully
2022-08-30 16:46:29,683:INFO:Starting cross validation
2022-08-30 16:46:29,684:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-08-30 16:46:29,817:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_huber.py:335: 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)
2022-08-30 16:46:29,818:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,825:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_huber.py:335: 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)
2022-08-30 16:46:29,827:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,844:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_huber.py:335: 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)
2022-08-30 16:46:29,845:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,848:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_huber.py:335: 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)
2022-08-30 16:46:29,849:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,851:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_huber.py:335: 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)
2022-08-30 16:46:29,852:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,853:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_huber.py:335: 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)
2022-08-30 16:46:29,853:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_huber.py:335: 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)
2022-08-30 16:46:29,853:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,854:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,861:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_huber.py:335: 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)
2022-08-30 16:46:29,862:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,886:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_huber.py:335: 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)
2022-08-30 16:46:29,887:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,900:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/linear_model/_huber.py:335: 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)
2022-08-30 16:46:29,901:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:29,908:INFO:Calculating mean and std
2022-08-30 16:46:29,909:INFO:Creating metrics dataframe
2022-08-30 16:46:29,913:INFO:Uploading results into container
2022-08-30 16:46:29,914:INFO:Uploading model into container now
2022-08-30 16:46:29,914:INFO:master_model_container: 8
2022-08-30 16:46:29,914:INFO:display_container: 2
2022-08-30 16:46:29,915:INFO:HuberRegressor()
2022-08-30 16:46:29,915:INFO:create_model() successfully completed......................................
2022-08-30 16:46:30,013:INFO:SubProcess create_model() end ==================================
2022-08-30 16:46:30,014:INFO:Creating metrics dataframe
2022-08-30 16:46:30,019:INFO:Initializing K Neighbors Regressor
2022-08-30 16:46:30,019:INFO:Total runtime is 0.08926135698954263 minutes
2022-08-30 16:46:30,019:INFO:SubProcess create_model() called ==================================
2022-08-30 16:46:30,019:INFO:Initializing create_model()
2022-08-30 16:46:30,019:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, 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 0x7f2b3a50e110>, return_train_score=False, kwargs={})
2022-08-30 16:46:30,019:INFO:Checking exceptions
2022-08-30 16:46:30,021:INFO:Importing libraries
2022-08-30 16:46:30,021:INFO:Copying training dataset
2022-08-30 16:46:30,025:INFO:Defining folds
2022-08-30 16:46:30,025:INFO:Declaring metric variables
2022-08-30 16:46:30,026:INFO:Importing untrained model
2022-08-30 16:46:30,026:INFO:K Neighbors Regressor Imported successfully
2022-08-30 16:46:30,026:INFO:Starting cross validation
2022-08-30 16:46:30,027:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-08-30 16:46:30,053:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:30,058:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:30,061:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:30,067:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:30,074:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:30,083:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:30,091:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:30,098:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:30,100:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:30,110:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:30,135:INFO:Calculating mean and std
2022-08-30 16:46:30,137:INFO:Creating metrics dataframe
2022-08-30 16:46:30,140:INFO:Uploading results into container
2022-08-30 16:46:30,141:INFO:Uploading model into container now
2022-08-30 16:46:30,141:INFO:master_model_container: 9
2022-08-30 16:46:30,141:INFO:display_container: 2
2022-08-30 16:46:30,141:INFO:KNeighborsRegressor(n_jobs=-1)
2022-08-30 16:46:30,141:INFO:create_model() successfully completed......................................
2022-08-30 16:46:30,229:INFO:SubProcess create_model() end ==================================
2022-08-30 16:46:30,229:INFO:Creating metrics dataframe
2022-08-30 16:46:30,233:INFO:Initializing Extra Trees Regressor
2022-08-30 16:46:30,233:INFO:Total runtime is 0.09283436536788939 minutes
2022-08-30 16:46:30,233:INFO:SubProcess create_model() called ==================================
2022-08-30 16:46:30,233:INFO:Initializing create_model()
2022-08-30 16:46:30,233:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, 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 0x7f2b3a50e110>, return_train_score=False, kwargs={})
2022-08-30 16:46:30,233:INFO:Checking exceptions
2022-08-30 16:46:30,235:INFO:Importing libraries
2022-08-30 16:46:30,235:INFO:Copying training dataset
2022-08-30 16:46:30,236:INFO:Defining folds
2022-08-30 16:46:30,237:INFO:Declaring metric variables
2022-08-30 16:46:30,237:INFO:Importing untrained model
2022-08-30 16:46:30,237:INFO:Extra Trees Regressor Imported successfully
2022-08-30 16:46:30,237:INFO:Starting cross validation
2022-08-30 16:46:30,238:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-08-30 16:46:31,561:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:31,568:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:31,602:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:31,635:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:31,642:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:31,650:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:31,653:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:31,655:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:31,660:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:31,664:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:31,704:INFO:Calculating mean and std
2022-08-30 16:46:31,704:INFO:Creating metrics dataframe
2022-08-30 16:46:31,707:INFO:Uploading results into container
2022-08-30 16:46:31,708:INFO:Uploading model into container now
2022-08-30 16:46:31,708:INFO:master_model_container: 10
2022-08-30 16:46:31,708:INFO:display_container: 2
2022-08-30 16:46:31,708:INFO:ExtraTreesRegressor(n_jobs=-1, random_state=3983)
2022-08-30 16:46:31,708:INFO:create_model() successfully completed......................................
2022-08-30 16:46:31,790:INFO:SubProcess create_model() end ==================================
2022-08-30 16:46:31,790:INFO:Creating metrics dataframe
2022-08-30 16:46:31,795:INFO:Initializing AdaBoost Regressor
2022-08-30 16:46:31,795:INFO:Total runtime is 0.11886018117268879 minutes
2022-08-30 16:46:31,795:INFO:SubProcess create_model() called ==================================
2022-08-30 16:46:31,795:INFO:Initializing create_model()
2022-08-30 16:46:31,795:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, 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 0x7f2b3a50e110>, return_train_score=False, kwargs={})
2022-08-30 16:46:31,795:INFO:Checking exceptions
2022-08-30 16:46:31,797:INFO:Importing libraries
2022-08-30 16:46:31,797:INFO:Copying training dataset
2022-08-30 16:46:31,799:INFO:Defining folds
2022-08-30 16:46:31,799:INFO:Declaring metric variables
2022-08-30 16:46:31,799:INFO:Importing untrained model
2022-08-30 16:46:31,799:INFO:AdaBoost Regressor Imported successfully
2022-08-30 16:46:31,799:INFO:Starting cross validation
2022-08-30 16:46:31,800:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-08-30 16:46:31,926:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:31,943:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:31,943:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:31,988:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:31,992:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,002:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,007:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,083:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,085:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,103:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,111:INFO:Calculating mean and std
2022-08-30 16:46:32,111:INFO:Creating metrics dataframe
2022-08-30 16:46:32,114:INFO:Uploading results into container
2022-08-30 16:46:32,115:INFO:Uploading model into container now
2022-08-30 16:46:32,115:INFO:master_model_container: 11
2022-08-30 16:46:32,115:INFO:display_container: 2
2022-08-30 16:46:32,115:INFO:AdaBoostRegressor(random_state=3983)
2022-08-30 16:46:32,116:INFO:create_model() successfully completed......................................
2022-08-30 16:46:32,208:INFO:SubProcess create_model() end ==================================
2022-08-30 16:46:32,208:INFO:Creating metrics dataframe
2022-08-30 16:46:32,212:INFO:Initializing Gradient Boosting Regressor
2022-08-30 16:46:32,212:INFO:Total runtime is 0.125814155737559 minutes
2022-08-30 16:46:32,212:INFO:SubProcess create_model() called ==================================
2022-08-30 16:46:32,212:INFO:Initializing create_model()
2022-08-30 16:46:32,212:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, 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 0x7f2b3a50e110>, return_train_score=False, kwargs={})
2022-08-30 16:46:32,212:INFO:Checking exceptions
2022-08-30 16:46:32,213:INFO:Importing libraries
2022-08-30 16:46:32,214:INFO:Copying training dataset
2022-08-30 16:46:32,216:INFO:Defining folds
2022-08-30 16:46:32,216:INFO:Declaring metric variables
2022-08-30 16:46:32,216:INFO:Importing untrained model
2022-08-30 16:46:32,217:INFO:Gradient Boosting Regressor Imported successfully
2022-08-30 16:46:32,217:INFO:Starting cross validation
2022-08-30 16:46:32,217:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-08-30 16:46:32,701:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,714:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,825:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,830:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,842:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,848:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,857:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,862:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,864:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,864:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:32,876:INFO:Calculating mean and std
2022-08-30 16:46:32,878:INFO:Creating metrics dataframe
2022-08-30 16:46:32,882:INFO:Uploading results into container
2022-08-30 16:46:32,883:INFO:Uploading model into container now
2022-08-30 16:46:32,884:INFO:master_model_container: 12
2022-08-30 16:46:32,884:INFO:display_container: 2
2022-08-30 16:46:32,884:INFO:GradientBoostingRegressor(random_state=3983)
2022-08-30 16:46:32,884:INFO:create_model() successfully completed......................................
2022-08-30 16:46:32,990:INFO:SubProcess create_model() end ==================================
2022-08-30 16:46:32,990:INFO:Creating metrics dataframe
2022-08-30 16:46:32,996:INFO:Initializing Dummy Regressor
2022-08-30 16:46:32,996:INFO:Total runtime is 0.13887821435928344 minutes
2022-08-30 16:46:32,996:INFO:SubProcess create_model() called ==================================
2022-08-30 16:46:32,996:INFO:Initializing create_model()
2022-08-30 16:46:32,996:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, 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 0x7f2b3a50e110>, return_train_score=False, kwargs={})
2022-08-30 16:46:32,996:INFO:Checking exceptions
2022-08-30 16:46:32,998:INFO:Importing libraries
2022-08-30 16:46:32,998:INFO:Copying training dataset
2022-08-30 16:46:33,001:INFO:Defining folds
2022-08-30 16:46:33,001:INFO:Declaring metric variables
2022-08-30 16:46:33,001:INFO:Importing untrained model
2022-08-30 16:46:33,002:INFO:Dummy Regressor Imported successfully
2022-08-30 16:46:33,002:INFO:Starting cross validation
2022-08-30 16:46:33,002:INFO:Cross validating with KFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2022-08-30 16:46:33,027:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:33,029:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:33,036:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:33,041:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:33,044:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:33,045:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:33,050:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:33,052:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:33,059:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:33,065:WARNING:/home/dyms/.local/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:201: FutureWarning: if_delegate_has_method was deprecated in version 1.1 and will be removed in version 1.3. Use available_if instead.
warnings.warn(
2022-08-30 16:46:33,072:INFO:Calculating mean and std
2022-08-30 16:46:33,073:INFO:Creating metrics dataframe
2022-08-30 16:46:33,077:INFO:Uploading results into container
2022-08-30 16:46:33,077:INFO:Uploading model into container now
2022-08-30 16:46:33,078:INFO:master_model_container: 13
2022-08-30 16:46:33,078:INFO:display_container: 2
2022-08-30 16:46:33,078:INFO:DummyRegressor()
2022-08-30 16:46:33,078:INFO:create_model() successfully completed......................................
2022-08-30 16:46:33,183:INFO:SubProcess create_model() end ==================================
2022-08-30 16:46:33,183:INFO:Creating metrics dataframe
2022-08-30 16:46:33,190:INFO:Initializing create_model()
2022-08-30 16:46:33,190:INFO:create_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, estimator=BayesianRidge(), 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, return_train_score=False, kwargs={})
2022-08-30 16:46:33,190:INFO:Checking exceptions
2022-08-30 16:46:33,193:INFO:Importing libraries
2022-08-30 16:46:33,193:INFO:Copying training dataset
2022-08-30 16:46:33,196:INFO:Defining folds
2022-08-30 16:46:33,196:INFO:Declaring metric variables
2022-08-30 16:46:33,196:INFO:Importing untrained model
2022-08-30 16:46:33,196:INFO:Declaring custom model
2022-08-30 16:46:33,197:INFO:Bayesian Ridge Imported successfully
2022-08-30 16:46:33,197:INFO:Cross validation set to False
2022-08-30 16:46:33,197:INFO:Fitting Model
2022-08-30 16:46:33,321:INFO:BayesianRidge()
2022-08-30 16:46:33,321:INFO:create_model() successfully completed......................................
2022-08-30 16:46:33,479:INFO:master_model_container: 13
2022-08-30 16:46:33,479:INFO:display_container: 2
2022-08-30 16:46:33,479:INFO:BayesianRidge()
2022-08-30 16:46:33,479:INFO:compare_models() successfully completed......................................
2022-08-30 16:46:33,491:INFO:Initializing predict_model()
2022-08-30 16:46:33,492:INFO:predict_model(self=<pycaret.regression.oop.RegressionExperiment object at 0x7f2b3a50d9c0>, estimator=BayesianRidge(), probability_threshold=None, encoded_labels=False, raw_score=False, drift_report=False, round=4, verbose=True, ml_usecase=None, preprocess=True, replace_labels_in_column=<function _SupervisedExperiment.predict_model.<locals>.replace_labels_in_column at 0x7f2afc12aef0>)
2022-08-30 16:46:33,492:INFO:Checking exceptions
2022-08-30 16:46:33,492:INFO:Preloading libraries