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logs.log
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2023-11-19 19:56:35,548:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2023-11-19 19:56:35,548:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2023-11-19 19:56:35,548:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2023-11-19 19:56:35,548:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2023-11-19 19:57:25,187:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2023-11-19 19:57:25,187:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2023-11-19 19:57:25,187:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2023-11-19 19:57:25,187:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2023-11-19 20:26:13,648:INFO:PyCaret ClassificationExperiment
2023-11-19 20:26:13,649:INFO:Logging name: clf-default-name
2023-11-19 20:26:13,649:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2023-11-19 20:26:13,649:INFO:version 3.2.0
2023-11-19 20:26:13,649:INFO:Initializing setup()
2023-11-19 20:26:13,649:INFO:self.USI: 3599
2023-11-19 20:26:13,649:INFO:self._variable_keys: {'idx', 'X', 'html_param', 'log_plots_param', 'target_param', 'fold_shuffle_param', 'exp_name_log', 'data', 'is_multiclass', '_ml_usecase', 'USI', 'fold_groups_param', 'fold_generator', 'y_test', 'fix_imbalance', 'memory', '_available_plots', 'pipeline', 'y_train', 'X_test', 'gpu_param', 'seed', 'exp_id', 'gpu_n_jobs_param', 'X_train', 'logging_param', 'n_jobs_param', 'y'}
2023-11-19 20:26:13,649:INFO:Checking environment
2023-11-19 20:26:13,649:INFO:python_version: 3.10.8
2023-11-19 20:26:13,649:INFO:python_build: ('main', 'Nov 2 2023 18:07:42')
2023-11-19 20:26:13,650:INFO:machine: x86_64
2023-11-19 20:26:13,650:INFO:platform: Linux-6.2.0-1016-azure-x86_64-with-glibc2.31
2023-11-19 20:26:13,650:INFO:Memory: svmem(total=8315170816, available=5249802240, percent=36.9, used=2733936640, free=161234944, active=1407561728, inactive=5963649024, buffers=259330048, cached=5160669184, shared=1261568, slab=624091136)
2023-11-19 20:26:13,651:INFO:Physical Core: 1
2023-11-19 20:26:13,651:INFO:Logical Core: 2
2023-11-19 20:26:13,651:INFO:Checking libraries
2023-11-19 20:26:13,651:INFO:System:
2023-11-19 20:26:13,651:INFO: python: 3.10.8 (main, Nov 2 2023, 18:07:42) [GCC 9.4.0]
2023-11-19 20:26:13,651:INFO:executable: /home/codespace/.python/current/bin/python3
2023-11-19 20:26:13,651:INFO: machine: Linux-6.2.0-1016-azure-x86_64-with-glibc2.31
2023-11-19 20:26:13,651:INFO:PyCaret required dependencies:
2023-11-19 20:26:13,711:INFO: pip: 23.3.1
2023-11-19 20:26:13,711:INFO: setuptools: 68.0.0
2023-11-19 20:26:13,711:INFO: pycaret: 3.2.0
2023-11-19 20:26:13,711:INFO: IPython: 8.17.2
2023-11-19 20:26:13,711:INFO: ipywidgets: 8.1.1
2023-11-19 20:26:13,711:INFO: tqdm: 4.66.1
2023-11-19 20:26:13,711:INFO: numpy: 1.25.2
2023-11-19 20:26:13,711:INFO: pandas: 1.5.3
2023-11-19 20:26:13,711:INFO: jinja2: 3.1.2
2023-11-19 20:26:13,711:INFO: scipy: 1.10.1
2023-11-19 20:26:13,712:INFO: joblib: 1.3.2
2023-11-19 20:26:13,712:INFO: sklearn: 1.2.2
2023-11-19 20:26:13,712:INFO: pyod: 1.1.2
2023-11-19 20:26:13,712:INFO: imblearn: 0.11.0
2023-11-19 20:26:13,712:INFO: category_encoders: 2.6.3
2023-11-19 20:26:13,712:INFO: lightgbm: 4.1.0
2023-11-19 20:26:13,712:INFO: numba: 0.58.1
2023-11-19 20:26:13,712:INFO: requests: 2.31.0
2023-11-19 20:26:13,712:INFO: matplotlib: 3.6.0
2023-11-19 20:26:13,712:INFO: scikitplot: 0.3.7
2023-11-19 20:26:13,712:INFO: yellowbrick: 1.5
2023-11-19 20:26:13,713:INFO: plotly: 5.18.0
2023-11-19 20:26:13,713:INFO: plotly-resampler: Not installed
2023-11-19 20:26:13,713:INFO: kaleido: 0.2.1
2023-11-19 20:26:13,713:INFO: schemdraw: 0.15
2023-11-19 20:26:13,713:INFO: statsmodels: 0.14.0
2023-11-19 20:26:13,713:INFO: sktime: 0.21.1
2023-11-19 20:26:13,713:INFO: tbats: 1.1.3
2023-11-19 20:26:13,713:INFO: pmdarima: 2.0.4
2023-11-19 20:26:13,713:INFO: psutil: 5.9.6
2023-11-19 20:26:13,713:INFO: markupsafe: 2.1.3
2023-11-19 20:26:13,713:INFO: pickle5: Not installed
2023-11-19 20:26:13,713:INFO: cloudpickle: 3.0.0
2023-11-19 20:26:13,714:INFO: deprecation: 2.1.0
2023-11-19 20:26:13,714:INFO: xxhash: 3.4.1
2023-11-19 20:26:13,714:INFO: wurlitzer: 3.0.3
2023-11-19 20:26:13,714:INFO:PyCaret optional dependencies:
2023-11-19 20:26:13,732:INFO: shap: Not installed
2023-11-19 20:26:13,732:INFO: interpret: Not installed
2023-11-19 20:26:13,732:INFO: umap: Not installed
2023-11-19 20:26:13,732:INFO: ydata_profiling: Not installed
2023-11-19 20:26:13,732:INFO: explainerdashboard: Not installed
2023-11-19 20:26:13,732:INFO: autoviz: Not installed
2023-11-19 20:26:13,733:INFO: fairlearn: Not installed
2023-11-19 20:26:13,733:INFO: deepchecks: Not installed
2023-11-19 20:26:13,733:INFO: xgboost: Not installed
2023-11-19 20:26:13,733:INFO: catboost: Not installed
2023-11-19 20:26:13,733:INFO: kmodes: Not installed
2023-11-19 20:26:13,733:INFO: mlxtend: Not installed
2023-11-19 20:26:13,733:INFO: statsforecast: Not installed
2023-11-19 20:26:13,733:INFO: tune_sklearn: Not installed
2023-11-19 20:26:13,734:INFO: ray: Not installed
2023-11-19 20:26:13,734:INFO: hyperopt: Not installed
2023-11-19 20:26:13,734:INFO: optuna: Not installed
2023-11-19 20:26:13,734:INFO: skopt: Not installed
2023-11-19 20:26:13,734:INFO: mlflow: Not installed
2023-11-19 20:26:13,734:INFO: gradio: Not installed
2023-11-19 20:26:13,734:INFO: fastapi: Not installed
2023-11-19 20:26:13,734:INFO: uvicorn: Not installed
2023-11-19 20:26:13,734:INFO: m2cgen: Not installed
2023-11-19 20:26:13,735:INFO: evidently: Not installed
2023-11-19 20:26:13,735:INFO: fugue: Not installed
2023-11-19 20:26:13,735:INFO: streamlit: Not installed
2023-11-19 20:26:13,735:INFO: prophet: 1.1.5
2023-11-19 20:26:13,735:INFO:None
2023-11-19 20:26:13,735:INFO:Set up data.
2023-11-19 20:26:36,478:INFO:PyCaret ClassificationExperiment
2023-11-19 20:26:36,480:INFO:Logging name: clf-default-name
2023-11-19 20:26:36,480:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2023-11-19 20:26:36,480:INFO:version 3.2.0
2023-11-19 20:26:36,480:INFO:Initializing setup()
2023-11-19 20:26:36,480:INFO:self.USI: 987c
2023-11-19 20:26:36,481:INFO:self._variable_keys: {'idx', 'X', 'html_param', 'log_plots_param', 'target_param', 'fold_shuffle_param', 'exp_name_log', 'data', 'is_multiclass', '_ml_usecase', 'USI', 'fold_groups_param', 'fold_generator', 'y_test', 'fix_imbalance', 'memory', '_available_plots', 'pipeline', 'y_train', 'X_test', 'gpu_param', 'seed', 'exp_id', 'gpu_n_jobs_param', 'X_train', 'logging_param', 'n_jobs_param', 'y'}
2023-11-19 20:26:36,481:INFO:Checking environment
2023-11-19 20:26:36,483:INFO:python_version: 3.10.8
2023-11-19 20:26:36,483:INFO:python_build: ('main', 'Nov 2 2023 18:07:42')
2023-11-19 20:26:36,483:INFO:machine: x86_64
2023-11-19 20:26:36,483:INFO:platform: Linux-6.2.0-1016-azure-x86_64-with-glibc2.31
2023-11-19 20:26:36,484:INFO:Memory: svmem(total=8315170816, available=5220683776, percent=37.2, used=2763067392, free=147939328, active=1408405504, inactive=5939138560, buffers=259563520, cached=5144600576, shared=1261568, slab=623796224)
2023-11-19 20:26:36,484:INFO:Physical Core: 1
2023-11-19 20:26:36,484:INFO:Logical Core: 2
2023-11-19 20:26:36,484:INFO:Checking libraries
2023-11-19 20:26:36,485:INFO:System:
2023-11-19 20:26:36,485:INFO: python: 3.10.8 (main, Nov 2 2023, 18:07:42) [GCC 9.4.0]
2023-11-19 20:26:36,485:INFO:executable: /home/codespace/.python/current/bin/python3
2023-11-19 20:26:36,485:INFO: machine: Linux-6.2.0-1016-azure-x86_64-with-glibc2.31
2023-11-19 20:26:36,485:INFO:PyCaret required dependencies:
2023-11-19 20:26:36,486:INFO: pip: 23.3.1
2023-11-19 20:26:36,486:INFO: setuptools: 68.0.0
2023-11-19 20:26:36,486:INFO: pycaret: 3.2.0
2023-11-19 20:26:36,486:INFO: IPython: 8.17.2
2023-11-19 20:26:36,486:INFO: ipywidgets: 8.1.1
2023-11-19 20:26:36,487:INFO: tqdm: 4.66.1
2023-11-19 20:26:36,487:INFO: numpy: 1.25.2
2023-11-19 20:26:36,487:INFO: pandas: 1.5.3
2023-11-19 20:26:36,487:INFO: jinja2: 3.1.2
2023-11-19 20:26:36,487:INFO: scipy: 1.10.1
2023-11-19 20:26:36,488:INFO: joblib: 1.3.2
2023-11-19 20:26:36,488:INFO: sklearn: 1.2.2
2023-11-19 20:26:36,488:INFO: pyod: 1.1.2
2023-11-19 20:26:36,488:INFO: imblearn: 0.11.0
2023-11-19 20:26:36,488:INFO: category_encoders: 2.6.3
2023-11-19 20:26:36,489:INFO: lightgbm: 4.1.0
2023-11-19 20:26:36,489:INFO: numba: 0.58.1
2023-11-19 20:26:36,489:INFO: requests: 2.31.0
2023-11-19 20:26:36,489:INFO: matplotlib: 3.6.0
2023-11-19 20:26:36,489:INFO: scikitplot: 0.3.7
2023-11-19 20:26:36,490:INFO: yellowbrick: 1.5
2023-11-19 20:26:36,490:INFO: plotly: 5.18.0
2023-11-19 20:26:36,490:INFO: plotly-resampler: Not installed
2023-11-19 20:26:36,490:INFO: kaleido: 0.2.1
2023-11-19 20:26:36,490:INFO: schemdraw: 0.15
2023-11-19 20:26:36,490:INFO: statsmodels: 0.14.0
2023-11-19 20:26:36,490:INFO: sktime: 0.21.1
2023-11-19 20:26:36,491:INFO: tbats: 1.1.3
2023-11-19 20:26:36,491:INFO: pmdarima: 2.0.4
2023-11-19 20:26:36,491:INFO: psutil: 5.9.6
2023-11-19 20:26:36,491:INFO: markupsafe: 2.1.3
2023-11-19 20:26:36,491:INFO: pickle5: Not installed
2023-11-19 20:26:36,491:INFO: cloudpickle: 3.0.0
2023-11-19 20:26:36,491:INFO: deprecation: 2.1.0
2023-11-19 20:26:36,491:INFO: xxhash: 3.4.1
2023-11-19 20:26:36,492:INFO: wurlitzer: 3.0.3
2023-11-19 20:26:36,492:INFO:PyCaret optional dependencies:
2023-11-19 20:26:36,492:INFO: shap: Not installed
2023-11-19 20:26:36,492:INFO: interpret: Not installed
2023-11-19 20:26:36,492:INFO: umap: Not installed
2023-11-19 20:26:36,492:INFO: ydata_profiling: Not installed
2023-11-19 20:26:36,492:INFO: explainerdashboard: Not installed
2023-11-19 20:26:36,492:INFO: autoviz: Not installed
2023-11-19 20:26:36,492:INFO: fairlearn: Not installed
2023-11-19 20:26:36,493:INFO: deepchecks: Not installed
2023-11-19 20:26:36,493:INFO: xgboost: Not installed
2023-11-19 20:26:36,493:INFO: catboost: Not installed
2023-11-19 20:26:36,493:INFO: kmodes: Not installed
2023-11-19 20:26:36,493:INFO: mlxtend: Not installed
2023-11-19 20:26:36,493:INFO: statsforecast: Not installed
2023-11-19 20:26:36,493:INFO: tune_sklearn: Not installed
2023-11-19 20:26:36,493:INFO: ray: Not installed
2023-11-19 20:26:36,493:INFO: hyperopt: Not installed
2023-11-19 20:26:36,493:INFO: optuna: Not installed
2023-11-19 20:26:36,494:INFO: skopt: Not installed
2023-11-19 20:26:36,494:INFO: mlflow: Not installed
2023-11-19 20:26:36,494:INFO: gradio: Not installed
2023-11-19 20:26:36,494:INFO: fastapi: Not installed
2023-11-19 20:26:36,494:INFO: uvicorn: Not installed
2023-11-19 20:26:36,494:INFO: m2cgen: Not installed
2023-11-19 20:26:36,494:INFO: evidently: Not installed
2023-11-19 20:26:36,494:INFO: fugue: Not installed
2023-11-19 20:26:36,494:INFO: streamlit: Not installed
2023-11-19 20:26:36,494:INFO: prophet: 1.1.5
2023-11-19 20:26:36,494:INFO:None
2023-11-19 20:26:36,495:INFO:Set up data.
2023-11-19 20:26:36,510:INFO:Set up folding strategy.
2023-11-19 20:26:36,510:INFO:Set up train/test split.
2023-11-19 20:26:36,517:INFO:Set up index.
2023-11-19 20:26:36,517:INFO:Assigning column types.
2023-11-19 20:26:36,521:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2023-11-19 20:26:36,565:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2023-11-19 20:26:36,566:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2023-11-19 20:26:36,595:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:36,595:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:36,639:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2023-11-19 20:26:36,640:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2023-11-19 20:26:36,667:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:36,668:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:36,668:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2023-11-19 20:26:36,711:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2023-11-19 20:26:36,738:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:36,738:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:36,807:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2023-11-19 20:26:36,850:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:36,851:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:36,851:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2023-11-19 20:26:36,920:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:36,920:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:36,989:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:36,990:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:36,992:INFO:Preparing preprocessing pipeline...
2023-11-19 20:26:36,993:INFO:Set up label encoding.
2023-11-19 20:26:36,993:INFO:Set up date feature engineering.
2023-11-19 20:26:36,993:INFO:Set up simple imputation.
2023-11-19 20:26:37,032:INFO:Finished creating preprocessing pipeline.
2023-11-19 20:26:37,038:INFO:Pipeline: Pipeline(memory=FastMemory(location=/tmp/joblib),
steps=[('label_encoding',
TransformerWrapperWithInverse(exclude=None, include=None,
transformer=LabelEncoder())),
('date_feature_extractor',
TransformerWrapper(exclude=None, include=['start_time'],
transformer=ExtractDateTimeFeatures(features=['day',
'month',
'year']))),
('numerical_imputer',
TransformerWrapper...
copy=True,
fill_value=None,
keep_empty_features=False,
missing_values=nan,
strategy='mean',
verbose='deprecated'))),
('categorical_imputer',
TransformerWrapper(exclude=None, include=[],
transformer=SimpleImputer(add_indicator=False,
copy=True,
fill_value=None,
keep_empty_features=False,
missing_values=nan,
strategy='most_frequent',
verbose='deprecated')))],
verbose=False)
2023-11-19 20:26:37,038:INFO:Creating final display dataframe.
2023-11-19 20:26:37,155:INFO:Setup _display_container: Description Value
0 Session id 123
1 Target highest_surplus_country
2 Target type Multiclass
3 Target mapping 0: 0, 4: 1, 5: 2, 6: 3, 7: 4
4 Original data shape (8760, 29)
5 Transformed data shape (8760, 31)
6 Transformed train set shape (7008, 31)
7 Transformed test set shape (1752, 31)
8 Numeric features 27
9 Date features 1
10 Preprocess True
11 Imputation type simple
12 Numeric imputation mean
13 Categorical imputation mode
14 Fold Generator StratifiedKFold
15 Fold Number 10
16 CPU Jobs -1
17 Use GPU False
18 Log Experiment False
19 Experiment Name clf-default-name
20 USI 987c
2023-11-19 20:26:37,285:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:37,285:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:37,423:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:37,424:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:26:37,424:INFO:setup() successfully completed in 0.95s...............
2023-11-19 20:27:33,466:INFO:PyCaret ClassificationExperiment
2023-11-19 20:27:33,466:INFO:Logging name: clf-default-name
2023-11-19 20:27:33,466:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2023-11-19 20:27:33,466:INFO:version 3.2.0
2023-11-19 20:27:33,466:INFO:Initializing setup()
2023-11-19 20:27:33,466:INFO:self.USI: f273
2023-11-19 20:27:33,467:INFO:self._variable_keys: {'idx', 'X', 'html_param', 'log_plots_param', 'target_param', 'fold_shuffle_param', 'exp_name_log', 'data', 'is_multiclass', '_ml_usecase', 'USI', 'fold_groups_param', 'fold_generator', 'y_test', 'fix_imbalance', 'memory', '_available_plots', 'pipeline', 'y_train', 'X_test', 'gpu_param', 'seed', 'exp_id', 'gpu_n_jobs_param', 'X_train', 'logging_param', 'n_jobs_param', 'y'}
2023-11-19 20:27:33,467:INFO:Checking environment
2023-11-19 20:27:33,467:INFO:python_version: 3.10.8
2023-11-19 20:27:33,467:INFO:python_build: ('main', 'Nov 2 2023 18:07:42')
2023-11-19 20:27:33,467:INFO:machine: x86_64
2023-11-19 20:27:33,467:INFO:platform: Linux-6.2.0-1016-azure-x86_64-with-glibc2.31
2023-11-19 20:27:33,468:INFO:Memory: svmem(total=8315170816, available=5316448256, percent=36.1, used=2667302912, free=243728384, active=1408823296, inactive=5888524288, buffers=259952640, cached=5144186880, shared=1261568, slab=623767552)
2023-11-19 20:27:33,468:INFO:Physical Core: 1
2023-11-19 20:27:33,469:INFO:Logical Core: 2
2023-11-19 20:27:33,469:INFO:Checking libraries
2023-11-19 20:27:33,469:INFO:System:
2023-11-19 20:27:33,469:INFO: python: 3.10.8 (main, Nov 2 2023, 18:07:42) [GCC 9.4.0]
2023-11-19 20:27:33,469:INFO:executable: /home/codespace/.python/current/bin/python3
2023-11-19 20:27:33,470:INFO: machine: Linux-6.2.0-1016-azure-x86_64-with-glibc2.31
2023-11-19 20:27:33,470:INFO:PyCaret required dependencies:
2023-11-19 20:27:33,470:INFO: pip: 23.3.1
2023-11-19 20:27:33,470:INFO: setuptools: 68.0.0
2023-11-19 20:27:33,470:INFO: pycaret: 3.2.0
2023-11-19 20:27:33,470:INFO: IPython: 8.17.2
2023-11-19 20:27:33,470:INFO: ipywidgets: 8.1.1
2023-11-19 20:27:33,471:INFO: tqdm: 4.66.1
2023-11-19 20:27:33,471:INFO: numpy: 1.25.2
2023-11-19 20:27:33,471:INFO: pandas: 1.5.3
2023-11-19 20:27:33,471:INFO: jinja2: 3.1.2
2023-11-19 20:27:33,471:INFO: scipy: 1.10.1
2023-11-19 20:27:33,471:INFO: joblib: 1.3.2
2023-11-19 20:27:33,471:INFO: sklearn: 1.2.2
2023-11-19 20:27:33,471:INFO: pyod: 1.1.2
2023-11-19 20:27:33,472:INFO: imblearn: 0.11.0
2023-11-19 20:27:33,472:INFO: category_encoders: 2.6.3
2023-11-19 20:27:33,472:INFO: lightgbm: 4.1.0
2023-11-19 20:27:33,472:INFO: numba: 0.58.1
2023-11-19 20:27:33,472:INFO: requests: 2.31.0
2023-11-19 20:27:33,472:INFO: matplotlib: 3.6.0
2023-11-19 20:27:33,472:INFO: scikitplot: 0.3.7
2023-11-19 20:27:33,472:INFO: yellowbrick: 1.5
2023-11-19 20:27:33,473:INFO: plotly: 5.18.0
2023-11-19 20:27:33,473:INFO: plotly-resampler: Not installed
2023-11-19 20:27:33,473:INFO: kaleido: 0.2.1
2023-11-19 20:27:33,473:INFO: schemdraw: 0.15
2023-11-19 20:27:33,473:INFO: statsmodels: 0.14.0
2023-11-19 20:27:33,473:INFO: sktime: 0.21.1
2023-11-19 20:27:33,473:INFO: tbats: 1.1.3
2023-11-19 20:27:33,473:INFO: pmdarima: 2.0.4
2023-11-19 20:27:33,473:INFO: psutil: 5.9.6
2023-11-19 20:27:33,473:INFO: markupsafe: 2.1.3
2023-11-19 20:27:33,474:INFO: pickle5: Not installed
2023-11-19 20:27:33,474:INFO: cloudpickle: 3.0.0
2023-11-19 20:27:33,474:INFO: deprecation: 2.1.0
2023-11-19 20:27:33,474:INFO: xxhash: 3.4.1
2023-11-19 20:27:33,474:INFO: wurlitzer: 3.0.3
2023-11-19 20:27:33,474:INFO:PyCaret optional dependencies:
2023-11-19 20:27:33,474:INFO: shap: Not installed
2023-11-19 20:27:33,474:INFO: interpret: Not installed
2023-11-19 20:27:33,474:INFO: umap: Not installed
2023-11-19 20:27:33,474:INFO: ydata_profiling: Not installed
2023-11-19 20:27:33,475:INFO: explainerdashboard: Not installed
2023-11-19 20:27:33,475:INFO: autoviz: Not installed
2023-11-19 20:27:33,475:INFO: fairlearn: Not installed
2023-11-19 20:27:33,475:INFO: deepchecks: Not installed
2023-11-19 20:27:33,475:INFO: xgboost: Not installed
2023-11-19 20:27:33,475:INFO: catboost: Not installed
2023-11-19 20:27:33,475:INFO: kmodes: Not installed
2023-11-19 20:27:33,475:INFO: mlxtend: Not installed
2023-11-19 20:27:33,475:INFO: statsforecast: Not installed
2023-11-19 20:27:33,475:INFO: tune_sklearn: Not installed
2023-11-19 20:27:33,476:INFO: ray: Not installed
2023-11-19 20:27:33,476:INFO: hyperopt: Not installed
2023-11-19 20:27:33,476:INFO: optuna: Not installed
2023-11-19 20:27:33,476:INFO: skopt: Not installed
2023-11-19 20:27:33,476:INFO: mlflow: Not installed
2023-11-19 20:27:33,476:INFO: gradio: Not installed
2023-11-19 20:27:33,476:INFO: fastapi: Not installed
2023-11-19 20:27:33,476:INFO: uvicorn: Not installed
2023-11-19 20:27:33,476:INFO: m2cgen: Not installed
2023-11-19 20:27:33,476:INFO: evidently: Not installed
2023-11-19 20:27:33,477:INFO: fugue: Not installed
2023-11-19 20:27:33,477:INFO: streamlit: Not installed
2023-11-19 20:27:33,477:INFO: prophet: 1.1.5
2023-11-19 20:27:33,477:INFO:None
2023-11-19 20:27:33,477:INFO:Set up data.
2023-11-19 20:27:33,491:INFO:Set up folding strategy.
2023-11-19 20:27:33,491:INFO:Set up train/test split.
2023-11-19 20:27:33,497:INFO:Set up index.
2023-11-19 20:27:33,498:INFO:Assigning column types.
2023-11-19 20:27:33,503:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2023-11-19 20:27:33,581:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:33,582:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:33,651:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:33,651:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:33,651:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2023-11-19 20:27:33,724:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:33,724:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:33,793:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:33,793:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:33,793:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2023-11-19 20:27:33,862:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:33,862:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:33,935:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:33,935:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:33,936:INFO:Preparing preprocessing pipeline...
2023-11-19 20:27:33,938:INFO:Set up label encoding.
2023-11-19 20:27:33,938:INFO:Set up date feature engineering.
2023-11-19 20:27:33,938:INFO:Set up simple imputation.
2023-11-19 20:27:33,977:INFO:Finished creating preprocessing pipeline.
2023-11-19 20:27:33,982:INFO:Pipeline: Pipeline(memory=FastMemory(location=/tmp/joblib),
steps=[('label_encoding',
TransformerWrapperWithInverse(exclude=None, include=None,
transformer=LabelEncoder())),
('date_feature_extractor',
TransformerWrapper(exclude=None, include=['start_time'],
transformer=ExtractDateTimeFeatures(features=['day',
'month',
'year']))),
('numerical_imputer',
TransformerWrapper...
copy=True,
fill_value=None,
keep_empty_features=False,
missing_values=nan,
strategy='mean',
verbose='deprecated'))),
('categorical_imputer',
TransformerWrapper(exclude=None, include=[],
transformer=SimpleImputer(add_indicator=False,
copy=True,
fill_value=None,
keep_empty_features=False,
missing_values=nan,
strategy='most_frequent',
verbose='deprecated')))],
verbose=False)
2023-11-19 20:27:33,982:INFO:Creating final display dataframe.
2023-11-19 20:27:34,143:INFO:Setup _display_container: Description Value
0 Session id 123
1 Target highest_surplus_country
2 Target type Multiclass
3 Target mapping 0: 0, 4: 1, 5: 2, 6: 3, 7: 4
4 Original data shape (8760, 29)
5 Transformed data shape (8760, 31)
6 Transformed train set shape (7008, 31)
7 Transformed test set shape (1752, 31)
8 Numeric features 27
9 Date features 1
10 Preprocess True
11 Imputation type simple
12 Numeric imputation mean
13 Categorical imputation mode
14 Fold Generator StratifiedKFold
15 Fold Number 10
16 CPU Jobs -1
17 Use GPU False
18 Log Experiment False
19 Experiment Name clf-default-name
20 USI f273
2023-11-19 20:27:34,246:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:34,247:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:34,315:WARNING:
'xgboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install xgboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:34,316:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2023-11-19 20:27:34,316:INFO:setup() successfully completed in 0.85s...............
2023-11-19 20:28:11,319:INFO:Initializing compare_models()
2023-11-19 20:28:11,320:INFO:compare_models(self=<pycaret.classification.oop.ClassificationExperiment object at 0x7fa554eff2e0>, include=None, fold=None, round=4, cross_validation=True, sort=Accuracy, n_select=1, budget_time=None, turbo=True, errors=ignore, fit_kwargs=None, groups=None, experiment_custom_tags=None, probability_threshold=None, verbose=True, parallel=None, caller_params={'self': <pycaret.classification.oop.ClassificationExperiment object at 0x7fa554eff2e0>, 'include': None, 'exclude': None, 'fold': None, 'round': 4, 'cross_validation': True, 'sort': 'Accuracy', 'n_select': 1, 'budget_time': None, 'turbo': True, 'errors': 'ignore', 'fit_kwargs': None, 'groups': None, 'experiment_custom_tags': None, 'probability_threshold': None, 'engine': None, 'verbose': True, 'parallel': None, '__class__': <class 'pycaret.classification.oop.ClassificationExperiment'>}, exclude=None)
2023-11-19 20:28:11,320:INFO:Checking exceptions
2023-11-19 20:28:11,325:INFO:Preparing display monitor
2023-11-19 20:28:11,352:INFO:Initializing Logistic Regression
2023-11-19 20:28:11,352:INFO:Total runtime is 5.614757537841797e-06 minutes
2023-11-19 20:28:11,356:INFO:SubProcess create_model() called ==================================
2023-11-19 20:28:11,357:INFO:Initializing create_model()
2023-11-19 20:28:11,357:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x7fa554eff2e0>, estimator=lr, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, 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 0x7fa554313250>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2023-11-19 20:28:11,357:INFO:Checking exceptions
2023-11-19 20:28:11,357:INFO:Importing libraries
2023-11-19 20:28:11,357:INFO:Copying training dataset
2023-11-19 20:28:11,363:INFO:Defining folds
2023-11-19 20:28:11,363:INFO:Declaring metric variables
2023-11-19 20:28:11,366:INFO:Importing untrained model
2023-11-19 20:28:11,370:INFO:Logistic Regression Imported successfully
2023-11-19 20:28:11,377:INFO:Starting cross validation
2023-11-19 20:28:11,378:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2023-11-19 20:28:16,418:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: 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
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2023-11-19 20:28:16,448:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:16,451:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:16,454:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:16,503:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: 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
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2023-11-19 20:28:16,538:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:16,540:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:16,543:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:18,940:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: 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
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2023-11-19 20:28:19,003:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:19,007:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:19,010:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: 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
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2023-11-19 20:28:19,011:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:19,043:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:19,048:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:19,051:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:21,170:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: 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
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2023-11-19 20:28:21,199:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:21,202:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:21,205:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:21,305:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: 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
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2023-11-19 20:28:21,336:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:21,339:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:21,342:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:23,569:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: 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
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2023-11-19 20:28:23,612:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:23,615:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:23,618:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:23,766:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: 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
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2023-11-19 20:28:23,800:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:23,803:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:23,806:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:25,816:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: 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
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2023-11-19 20:28:25,849:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:25,852:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:25,855:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:25,973:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: 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
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
2023-11-19 20:28:25,993:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:25,995:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:25,997:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,010:INFO:Calculating mean and std
2023-11-19 20:28:26,012:INFO:Creating metrics dataframe
2023-11-19 20:28:26,018:INFO:Uploading results into container
2023-11-19 20:28:26,019:INFO:Uploading model into container now
2023-11-19 20:28:26,019:INFO:_master_model_container: 1
2023-11-19 20:28:26,020:INFO:_display_container: 2
2023-11-19 20:28:26,020:INFO:LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=1000,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=123, solver='lbfgs', tol=0.0001, verbose=0,
warm_start=False)
2023-11-19 20:28:26,020:INFO:create_model() successfully completed......................................
2023-11-19 20:28:26,141:INFO:SubProcess create_model() end ==================================
2023-11-19 20:28:26,141:INFO:Creating metrics dataframe
2023-11-19 20:28:26,151:INFO:Initializing K Neighbors Classifier
2023-11-19 20:28:26,151:INFO:Total runtime is 0.24665225346883138 minutes
2023-11-19 20:28:26,156:INFO:SubProcess create_model() called ==================================
2023-11-19 20:28:26,156:INFO:Initializing create_model()
2023-11-19 20:28:26,156:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x7fa554eff2e0>, estimator=knn, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, 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 0x7fa554313250>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2023-11-19 20:28:26,156:INFO:Checking exceptions
2023-11-19 20:28:26,157:INFO:Importing libraries
2023-11-19 20:28:26,157:INFO:Copying training dataset
2023-11-19 20:28:26,163:INFO:Defining folds
2023-11-19 20:28:26,164:INFO:Declaring metric variables
2023-11-19 20:28:26,168:INFO:Importing untrained model
2023-11-19 20:28:26,171:INFO:K Neighbors Classifier Imported successfully
2023-11-19 20:28:26,178:INFO:Starting cross validation
2023-11-19 20:28:26,179:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2023-11-19 20:28:26,467:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,470:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,471:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,474:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,475:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,477:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,664:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,667:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,671:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,705:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,709:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,720:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,885:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,889:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,891:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,910:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,915:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:26,919:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,114:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,120:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,130:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,142:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,146:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,149:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,290:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,293:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,296:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,314:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,316:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,318:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,324:INFO:Calculating mean and std
2023-11-19 20:28:27,325:INFO:Creating metrics dataframe
2023-11-19 20:28:27,329:INFO:Uploading results into container
2023-11-19 20:28:27,330:INFO:Uploading model into container now
2023-11-19 20:28:27,331:INFO:_master_model_container: 2
2023-11-19 20:28:27,331:INFO:_display_container: 2
2023-11-19 20:28:27,332:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform')
2023-11-19 20:28:27,332:INFO:create_model() successfully completed......................................
2023-11-19 20:28:27,441:INFO:SubProcess create_model() end ==================================
2023-11-19 20:28:27,441:INFO:Creating metrics dataframe
2023-11-19 20:28:27,451:INFO:Initializing Naive Bayes
2023-11-19 20:28:27,451:INFO:Total runtime is 0.26831803321838377 minutes
2023-11-19 20:28:27,455:INFO:SubProcess create_model() called ==================================
2023-11-19 20:28:27,456:INFO:Initializing create_model()
2023-11-19 20:28:27,456:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x7fa554eff2e0>, estimator=nb, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, 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 0x7fa554313250>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2023-11-19 20:28:27,456:INFO:Checking exceptions
2023-11-19 20:28:27,456:INFO:Importing libraries
2023-11-19 20:28:27,456:INFO:Copying training dataset
2023-11-19 20:28:27,464:INFO:Defining folds
2023-11-19 20:28:27,465:INFO:Declaring metric variables
2023-11-19 20:28:27,470:INFO:Importing untrained model
2023-11-19 20:28:27,473:INFO:Naive Bayes Imported successfully
2023-11-19 20:28:27,481:INFO:Starting cross validation
2023-11-19 20:28:27,482:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2023-11-19 20:28:27,565:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,574:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,576:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,578:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,580:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,584:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,661:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,665:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,669:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,674:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,677:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,686:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,808:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,809:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,811:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,813:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,815:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,817:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,897:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,901:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,904:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,906:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,910:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,914:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:27,996:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,000:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,003:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,013:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,017:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,020:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,027:INFO:Calculating mean and std
2023-11-19 20:28:28,028:INFO:Creating metrics dataframe
2023-11-19 20:28:28,032:INFO:Uploading results into container
2023-11-19 20:28:28,033:INFO:Uploading model into container now
2023-11-19 20:28:28,033:INFO:_master_model_container: 3
2023-11-19 20:28:28,033:INFO:_display_container: 2
2023-11-19 20:28:28,034:INFO:GaussianNB(priors=None, var_smoothing=1e-09)
2023-11-19 20:28:28,034:INFO:create_model() successfully completed......................................
2023-11-19 20:28:28,135:INFO:SubProcess create_model() end ==================================
2023-11-19 20:28:28,135:INFO:Creating metrics dataframe
2023-11-19 20:28:28,146:INFO:Initializing Decision Tree Classifier
2023-11-19 20:28:28,146:INFO:Total runtime is 0.27990086078643794 minutes
2023-11-19 20:28:28,150:INFO:SubProcess create_model() called ==================================
2023-11-19 20:28:28,150:INFO:Initializing create_model()
2023-11-19 20:28:28,150:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x7fa554eff2e0>, estimator=dt, fold=StratifiedKFold(n_splits=10, random_state=None, shuffle=False), round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=False, 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 0x7fa554313250>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2023-11-19 20:28:28,150:INFO:Checking exceptions
2023-11-19 20:28:28,151:INFO:Importing libraries
2023-11-19 20:28:28,151:INFO:Copying training dataset
2023-11-19 20:28:28,156:INFO:Defining folds
2023-11-19 20:28:28,157:INFO:Declaring metric variables
2023-11-19 20:28:28,162:INFO:Importing untrained model
2023-11-19 20:28:28,166:INFO:Decision Tree Classifier Imported successfully
2023-11-19 20:28:28,175:INFO:Starting cross validation
2023-11-19 20:28:28,177:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2023-11-19 20:28:28,422:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,425:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,428:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,468:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,473:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,476:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,660:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,662:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,665:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,716:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,719:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,722:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(
2023-11-19 20:28:28,914:WARNING:/home/codespace/.python/current/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1396: UserWarning: Note that pos_label (set to 7) is ignored when average != 'binary' (got 'weighted'). You may use labels=[pos_label] to specify a single positive class.
warnings.warn(