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logs.log
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2024-05-28 16:51:45,048:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-05-28 16:51:45,048:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-05-28 16:51:45,048:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-05-28 16:51:45,048:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-05-28 16:51:54,182:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-05-28 16:51:54,183:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-05-28 16:51:54,183:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-05-28 16:51:54,183:WARNING:
'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
2024-05-28 16:51:54,739:INFO:PyCaret ClassificationExperiment
2024-05-28 16:51:54,739:INFO:Logging name: clf-default-name
2024-05-28 16:51:54,739:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2024-05-28 16:51:54,739:INFO:version 3.3.2
2024-05-28 16:51:54,739:INFO:Initializing setup()
2024-05-28 16:51:54,739:INFO:self.USI: f8a0
2024-05-28 16:51:54,739:INFO:self._variable_keys: {'n_jobs_param', 'X_test', 'gpu_param', 'exp_name_log', 'seed', 'y_train', 'is_multiclass', 'fold_shuffle_param', 'idx', 'html_param', 'logging_param', 'y_test', 'y', 'target_param', 'fold_groups_param', 'data', 'fold_generator', 'memory', 'X', 'X_train', 'pipeline', '_available_plots', 'fix_imbalance', 'USI', 'log_plots_param', 'exp_id', '_ml_usecase', 'gpu_n_jobs_param'}
2024-05-28 16:51:54,739:INFO:Checking environment
2024-05-28 16:51:54,739:INFO:python_version: 3.11.6
2024-05-28 16:51:54,739:INFO:python_build: ('main', 'Oct 3 2023 10:29:11')
2024-05-28 16:51:54,739:INFO:machine: AMD64
2024-05-28 16:51:54,739:INFO:platform: Windows-10-10.0.22631-SP0
2024-05-28 16:51:54,746:INFO:Memory: svmem(total=34156871680, available=20935389184, percent=38.7, used=13221482496, free=20935389184)
2024-05-28 16:51:54,746:INFO:Physical Core: 6
2024-05-28 16:51:54,746:INFO:Logical Core: 12
2024-05-28 16:51:54,747:INFO:Checking libraries
2024-05-28 16:51:54,747:INFO:System:
2024-05-28 16:51:54,747:INFO: python: 3.11.6 | packaged by conda-forge | (main, Oct 3 2023, 10:29:11) [MSC v.1935 64 bit (AMD64)]
2024-05-28 16:51:54,747:INFO:executable: c:\ProgramData\miniconda3\envs\kaggle_ML_env\python.exe
2024-05-28 16:51:54,747:INFO: machine: Windows-10-10.0.22631-SP0
2024-05-28 16:51:54,747:INFO:PyCaret required dependencies:
2024-05-28 16:51:54,810:INFO: pip: 24.0
2024-05-28 16:51:54,810:INFO: setuptools: 70.0.0
2024-05-28 16:51:54,810:INFO: pycaret: 3.3.2
2024-05-28 16:51:54,810:INFO: IPython: 8.24.0
2024-05-28 16:51:54,810:INFO: ipywidgets: 8.1.2
2024-05-28 16:51:54,810:INFO: tqdm: 4.66.4
2024-05-28 16:51:54,810:INFO: numpy: 1.26.4
2024-05-28 16:51:54,810:INFO: pandas: 2.1.4
2024-05-28 16:51:54,810:INFO: jinja2: 3.1.4
2024-05-28 16:51:54,810:INFO: scipy: 1.11.4
2024-05-28 16:51:54,810:INFO: joblib: 1.3.2
2024-05-28 16:51:54,810:INFO: sklearn: 1.4.2
2024-05-28 16:51:54,810:INFO: pyod: 1.1.3
2024-05-28 16:51:54,810:INFO: imblearn: 0.12.2
2024-05-28 16:51:54,810:INFO: category_encoders: 2.6.3
2024-05-28 16:51:54,810:INFO: lightgbm: 4.3.0
2024-05-28 16:51:54,810:INFO: numba: 0.59.1
2024-05-28 16:51:54,810:INFO: requests: 2.32.2
2024-05-28 16:51:54,810:INFO: matplotlib: 3.7.5
2024-05-28 16:51:54,810:INFO: scikitplot: 0.3.7
2024-05-28 16:51:54,810:INFO: yellowbrick: 1.5
2024-05-28 16:51:54,810:INFO: plotly: 5.22.0
2024-05-28 16:51:54,810:INFO: plotly-resampler: Not installed
2024-05-28 16:51:54,810:INFO: kaleido: 0.2.1
2024-05-28 16:51:54,810:INFO: schemdraw: 0.15
2024-05-28 16:51:54,810:INFO: statsmodels: 0.14.2
2024-05-28 16:51:54,810:INFO: sktime: 0.26.0
2024-05-28 16:51:54,810:INFO: tbats: 1.1.3
2024-05-28 16:51:54,810:INFO: pmdarima: 2.0.4
2024-05-28 16:51:54,810:INFO: psutil: 5.9.8
2024-05-28 16:51:54,810:INFO: markupsafe: 2.1.5
2024-05-28 16:51:54,810:INFO: pickle5: Not installed
2024-05-28 16:51:54,810:INFO: cloudpickle: 3.0.0
2024-05-28 16:51:54,810:INFO: deprecation: 2.1.0
2024-05-28 16:51:54,810:INFO: xxhash: 3.4.1
2024-05-28 16:51:54,810:INFO: wurlitzer: Not installed
2024-05-28 16:51:54,810:INFO:PyCaret optional dependencies:
2024-05-28 16:51:54,867:INFO: shap: Not installed
2024-05-28 16:51:54,867:INFO: interpret: Not installed
2024-05-28 16:51:54,874:INFO: umap: Not installed
2024-05-28 16:51:54,874:INFO: ydata_profiling: Not installed
2024-05-28 16:51:54,874:INFO: explainerdashboard: Not installed
2024-05-28 16:51:54,874:INFO: autoviz: Not installed
2024-05-28 16:51:54,875:INFO: fairlearn: Not installed
2024-05-28 16:51:54,875:INFO: deepchecks: Not installed
2024-05-28 16:51:54,875:INFO: xgboost: 2.0.3
2024-05-28 16:51:54,875:INFO: catboost: Not installed
2024-05-28 16:51:54,875:INFO: kmodes: Not installed
2024-05-28 16:51:54,875:INFO: mlxtend: Not installed
2024-05-28 16:51:54,875:INFO: statsforecast: Not installed
2024-05-28 16:51:54,875:INFO: tune_sklearn: Not installed
2024-05-28 16:51:54,875:INFO: ray: Not installed
2024-05-28 16:51:54,875:INFO: hyperopt: Not installed
2024-05-28 16:51:54,875:INFO: optuna: Not installed
2024-05-28 16:51:54,875:INFO: skopt: Not installed
2024-05-28 16:51:54,875:INFO: mlflow: Not installed
2024-05-28 16:51:54,875:INFO: gradio: Not installed
2024-05-28 16:51:54,875:INFO: fastapi: Not installed
2024-05-28 16:51:54,875:INFO: uvicorn: Not installed
2024-05-28 16:51:54,875:INFO: m2cgen: Not installed
2024-05-28 16:51:54,875:INFO: evidently: Not installed
2024-05-28 16:51:54,875:INFO: fugue: Not installed
2024-05-28 16:51:54,875:INFO: streamlit: Not installed
2024-05-28 16:51:54,876:INFO: prophet: Not installed
2024-05-28 16:51:54,876:INFO:None
2024-05-28 16:51:54,876:INFO:Set up data.
2024-05-28 16:51:54,886:INFO:Set up folding strategy.
2024-05-28 16:51:54,887:INFO:Set up train/test split.
2024-05-28 16:51:54,899:INFO:Set up index.
2024-05-28 16:51:54,899:INFO:Assigning column types.
2024-05-28 16:51:54,907:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2024-05-28 16:51:54,968:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-05-28 16:51:54,977:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-05-28 16:51:55,031:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:51:55,034:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-05-28 16:51:55,089:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-05-28 16:51:55,089:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-05-28 16:51:55,121:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:51:55,127:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-05-28 16:51:55,127:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2024-05-28 16:51:55,177:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-05-28 16:51:55,209:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:51:55,211:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-05-28 16:51:55,259:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-05-28 16:51:55,308:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:51:55,309:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-05-28 16:51:55,309:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2024-05-28 16:51:55,426:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:51:55,427:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-05-28 16:51:55,517:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:51:55,522:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-05-28 16:51:55,528:INFO:Preparing preprocessing pipeline...
2024-05-28 16:51:55,530:INFO:Set up simple imputation.
2024-05-28 16:51:55,567:INFO:Finished creating preprocessing pipeline.
2024-05-28 16:51:55,571:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\torha\AppData\Local\Temp\joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None, include=['PassengerId'],
transformer=SimpleImputer(add_indicator=False,
copy=True,
fill_value=None,
keep_empty_features=False,
missing_values=nan,
strategy='mean'))),
('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=False)
2024-05-28 16:51:55,571:INFO:Creating final display dataframe.
2024-05-28 16:51:55,677:INFO:Setup _display_container: Description Value
0 Session id 123
1 Target Survived
2 Target type Binary
3 Original data shape (891, 18)
4 Transformed data shape (891, 18)
5 Transformed train set shape (623, 18)
6 Transformed test set shape (268, 18)
7 Numeric features 1
8 Preprocess True
9 Imputation type simple
10 Numeric imputation mean
11 Categorical imputation mode
12 Fold Generator StratifiedKFold
13 Fold Number 10
14 CPU Jobs -1
15 Use GPU False
16 Log Experiment False
17 Experiment Name clf-default-name
18 USI f8a0
2024-05-28 16:51:55,818:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:51:55,823:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-05-28 16:51:55,917:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:51:55,920: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]`
2024-05-28 16:51:55,920:INFO:setup() successfully completed in 1.18s...............
2024-05-28 16:53:35,239:INFO:PyCaret ClassificationExperiment
2024-05-28 16:53:35,239:INFO:Logging name: clf-default-name
2024-05-28 16:53:35,239:INFO:ML Usecase: MLUsecase.CLASSIFICATION
2024-05-28 16:53:35,239:INFO:version 3.3.2
2024-05-28 16:53:35,239:INFO:Initializing setup()
2024-05-28 16:53:35,239:INFO:self.USI: a21c
2024-05-28 16:53:35,239:INFO:self._variable_keys: {'n_jobs_param', 'X_test', 'gpu_param', 'exp_name_log', 'seed', 'y_train', 'is_multiclass', 'fold_shuffle_param', 'idx', 'html_param', 'logging_param', 'y_test', 'y', 'target_param', 'fold_groups_param', 'data', 'fold_generator', 'memory', 'X', 'X_train', 'pipeline', '_available_plots', 'fix_imbalance', 'USI', 'log_plots_param', 'exp_id', '_ml_usecase', 'gpu_n_jobs_param'}
2024-05-28 16:53:35,239:INFO:Checking environment
2024-05-28 16:53:35,239:INFO:python_version: 3.11.6
2024-05-28 16:53:35,239:INFO:python_build: ('main', 'Oct 3 2023 10:29:11')
2024-05-28 16:53:35,242:INFO:machine: AMD64
2024-05-28 16:53:35,242:INFO:platform: Windows-10-10.0.22631-SP0
2024-05-28 16:53:35,247:INFO:Memory: svmem(total=34156871680, available=20949577728, percent=38.7, used=13207293952, free=20949577728)
2024-05-28 16:53:35,247:INFO:Physical Core: 6
2024-05-28 16:53:35,247:INFO:Logical Core: 12
2024-05-28 16:53:35,247:INFO:Checking libraries
2024-05-28 16:53:35,247:INFO:System:
2024-05-28 16:53:35,247:INFO: python: 3.11.6 | packaged by conda-forge | (main, Oct 3 2023, 10:29:11) [MSC v.1935 64 bit (AMD64)]
2024-05-28 16:53:35,247:INFO:executable: c:\ProgramData\miniconda3\envs\kaggle_ML_env\python.exe
2024-05-28 16:53:35,247:INFO: machine: Windows-10-10.0.22631-SP0
2024-05-28 16:53:35,247:INFO:PyCaret required dependencies:
2024-05-28 16:53:35,247:INFO: pip: 24.0
2024-05-28 16:53:35,247:INFO: setuptools: 70.0.0
2024-05-28 16:53:35,247:INFO: pycaret: 3.3.2
2024-05-28 16:53:35,247:INFO: IPython: 8.24.0
2024-05-28 16:53:35,247:INFO: ipywidgets: 8.1.2
2024-05-28 16:53:35,247:INFO: tqdm: 4.66.4
2024-05-28 16:53:35,247:INFO: numpy: 1.26.4
2024-05-28 16:53:35,250:INFO: pandas: 2.1.4
2024-05-28 16:53:35,250:INFO: jinja2: 3.1.4
2024-05-28 16:53:35,250:INFO: scipy: 1.11.4
2024-05-28 16:53:35,250:INFO: joblib: 1.3.2
2024-05-28 16:53:35,250:INFO: sklearn: 1.4.2
2024-05-28 16:53:35,250:INFO: pyod: 1.1.3
2024-05-28 16:53:35,250:INFO: imblearn: 0.12.2
2024-05-28 16:53:35,250:INFO: category_encoders: 2.6.3
2024-05-28 16:53:35,250:INFO: lightgbm: 4.3.0
2024-05-28 16:53:35,250:INFO: numba: 0.59.1
2024-05-28 16:53:35,250:INFO: requests: 2.32.2
2024-05-28 16:53:35,250:INFO: matplotlib: 3.7.5
2024-05-28 16:53:35,250:INFO: scikitplot: 0.3.7
2024-05-28 16:53:35,250:INFO: yellowbrick: 1.5
2024-05-28 16:53:35,250:INFO: plotly: 5.22.0
2024-05-28 16:53:35,250:INFO: plotly-resampler: Not installed
2024-05-28 16:53:35,250:INFO: kaleido: 0.2.1
2024-05-28 16:53:35,250:INFO: schemdraw: 0.15
2024-05-28 16:53:35,250:INFO: statsmodels: 0.14.2
2024-05-28 16:53:35,250:INFO: sktime: 0.26.0
2024-05-28 16:53:35,250:INFO: tbats: 1.1.3
2024-05-28 16:53:35,250:INFO: pmdarima: 2.0.4
2024-05-28 16:53:35,250:INFO: psutil: 5.9.8
2024-05-28 16:53:35,250:INFO: markupsafe: 2.1.5
2024-05-28 16:53:35,250:INFO: pickle5: Not installed
2024-05-28 16:53:35,250:INFO: cloudpickle: 3.0.0
2024-05-28 16:53:35,250:INFO: deprecation: 2.1.0
2024-05-28 16:53:35,250:INFO: xxhash: 3.4.1
2024-05-28 16:53:35,250:INFO: wurlitzer: Not installed
2024-05-28 16:53:35,250:INFO:PyCaret optional dependencies:
2024-05-28 16:53:35,250:INFO: shap: Not installed
2024-05-28 16:53:35,250:INFO: interpret: Not installed
2024-05-28 16:53:35,250:INFO: umap: Not installed
2024-05-28 16:53:35,250:INFO: ydata_profiling: Not installed
2024-05-28 16:53:35,250:INFO: explainerdashboard: Not installed
2024-05-28 16:53:35,250:INFO: autoviz: Not installed
2024-05-28 16:53:35,250:INFO: fairlearn: Not installed
2024-05-28 16:53:35,250:INFO: deepchecks: Not installed
2024-05-28 16:53:35,250:INFO: xgboost: 2.0.3
2024-05-28 16:53:35,250:INFO: catboost: Not installed
2024-05-28 16:53:35,250:INFO: kmodes: Not installed
2024-05-28 16:53:35,250:INFO: mlxtend: Not installed
2024-05-28 16:53:35,250:INFO: statsforecast: Not installed
2024-05-28 16:53:35,250:INFO: tune_sklearn: Not installed
2024-05-28 16:53:35,250:INFO: ray: Not installed
2024-05-28 16:53:35,250:INFO: hyperopt: Not installed
2024-05-28 16:53:35,250:INFO: optuna: Not installed
2024-05-28 16:53:35,250:INFO: skopt: Not installed
2024-05-28 16:53:35,250:INFO: mlflow: Not installed
2024-05-28 16:53:35,250:INFO: gradio: Not installed
2024-05-28 16:53:35,250:INFO: fastapi: Not installed
2024-05-28 16:53:35,256:INFO: uvicorn: Not installed
2024-05-28 16:53:35,256:INFO: m2cgen: Not installed
2024-05-28 16:53:35,256:INFO: evidently: Not installed
2024-05-28 16:53:35,257:INFO: fugue: Not installed
2024-05-28 16:53:35,257:INFO: streamlit: Not installed
2024-05-28 16:53:35,257:INFO: prophet: Not installed
2024-05-28 16:53:35,257:INFO:None
2024-05-28 16:53:35,257:INFO:Set up data.
2024-05-28 16:53:35,260:INFO:Set up folding strategy.
2024-05-28 16:53:35,260:INFO:Set up train/test split.
2024-05-28 16:53:35,267:INFO:Set up index.
2024-05-28 16:53:35,267:INFO:Assigning column types.
2024-05-28 16:53:35,283:INFO:Engine successfully changes for model 'lr' to 'sklearn'.
2024-05-28 16:53:35,369:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-05-28 16:53:35,369:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-05-28 16:53:35,401:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:53:35,401:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-05-28 16:53:35,457:INFO:Engine for model 'knn' has not been set explicitly, hence returning None.
2024-05-28 16:53:35,460:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-05-28 16:53:35,517:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:53:35,520:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-05-28 16:53:35,526:INFO:Engine successfully changes for model 'knn' to 'sklearn'.
2024-05-28 16:53:35,618:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-05-28 16:53:35,651:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:53:35,651: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]`
2024-05-28 16:53:35,697:INFO:Engine for model 'rbfsvm' has not been set explicitly, hence returning None.
2024-05-28 16:53:35,728:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:53:35,728:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-05-28 16:53:35,728:INFO:Engine successfully changes for model 'rbfsvm' to 'sklearn'.
2024-05-28 16:53:35,817:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:53:35,817:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-05-28 16:53:35,907:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:53:35,907:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-05-28 16:53:35,912:INFO:Preparing preprocessing pipeline...
2024-05-28 16:53:35,912:INFO:Set up simple imputation.
2024-05-28 16:53:35,938:INFO:Finished creating preprocessing pipeline.
2024-05-28 16:53:35,938:INFO:Pipeline: Pipeline(memory=FastMemory(location=C:\Users\torha\AppData\Local\Temp\joblib),
steps=[('numerical_imputer',
TransformerWrapper(exclude=None, include=['PassengerId'],
transformer=SimpleImputer(add_indicator=False,
copy=True,
fill_value=None,
keep_empty_features=False,
missing_values=nan,
strategy='mean'))),
('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=False)
2024-05-28 16:53:35,938:INFO:Creating final display dataframe.
2024-05-28 16:53:36,037:INFO:Setup _display_container: Description Value
0 Session id 123
1 Target Survived
2 Target type Binary
3 Original data shape (891, 18)
4 Transformed data shape (891, 18)
5 Transformed train set shape (623, 18)
6 Transformed test set shape (268, 18)
7 Numeric features 1
8 Preprocess True
9 Imputation type simple
10 Numeric imputation mean
11 Categorical imputation mode
12 Fold Generator StratifiedKFold
13 Fold Number 10
14 CPU Jobs -1
15 Use GPU False
16 Log Experiment False
17 Experiment Name clf-default-name
18 USI a21c
2024-05-28 16:53:36,163:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:53:36,167:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-05-28 16:53:36,319:INFO:Soft dependency imported: xgboost: 2.0.3
2024-05-28 16:53:36,327:WARNING:
'catboost' is a soft dependency and not included in the pycaret installation. Please run: `pip install catboost` to install.
Alternately, you can install this by running `pip install pycaret[models]`
2024-05-28 16:53:36,328:INFO:setup() successfully completed in 1.09s...............
2024-05-28 16:54:52,857:INFO:Initializing compare_models()
2024-05-28 16:54:52,857:INFO:compare_models(self=<pycaret.classification.oop.ClassificationExperiment object at 0x0000018BFA8FD010>, 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, verbose=True, parallel=None, caller_params={'self': <pycaret.classification.oop.ClassificationExperiment object at 0x0000018BFA8FD010>, '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'>})
2024-05-28 16:54:52,858:INFO:Checking exceptions
2024-05-28 16:54:52,870:INFO:Preparing display monitor
2024-05-28 16:54:52,915:INFO:Initializing Logistic Regression
2024-05-28 16:54:52,916:INFO:Total runtime is 1.713434855143229e-05 minutes
2024-05-28 16:54:52,924:INFO:SubProcess create_model() called ==================================
2024-05-28 16:54:52,925:INFO:Initializing create_model()
2024-05-28 16:54:52,925:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x0000018BFA8FD010>, 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 0x0000018BFD2A52D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-05-28 16:54:52,926:INFO:Checking exceptions
2024-05-28 16:54:52,926:INFO:Importing libraries
2024-05-28 16:54:52,927:INFO:Copying training dataset
2024-05-28 16:54:52,941:INFO:Defining folds
2024-05-28 16:54:52,941:INFO:Declaring metric variables
2024-05-28 16:54:52,948:INFO:Importing untrained model
2024-05-28 16:54:52,957:INFO:Logistic Regression Imported successfully
2024-05-28 16:54:52,972:INFO:Starting cross validation
2024-05-28 16:54:52,974:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-05-28 16:54:58,399:INFO:Calculating mean and std
2024-05-28 16:54:58,400:INFO:Creating metrics dataframe
2024-05-28 16:54:58,403:INFO:Uploading results into container
2024-05-28 16:54:58,404:INFO:Uploading model into container now
2024-05-28 16:54:58,404:INFO:_master_model_container: 1
2024-05-28 16:54:58,404:INFO:_display_container: 2
2024-05-28 16:54:58,405: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)
2024-05-28 16:54:58,405:INFO:create_model() successfully completed......................................
2024-05-28 16:54:58,474:INFO:SubProcess create_model() end ==================================
2024-05-28 16:54:58,474:INFO:Creating metrics dataframe
2024-05-28 16:54:58,479:INFO:Initializing K Neighbors Classifier
2024-05-28 16:54:58,480:INFO:Total runtime is 0.09275459051132201 minutes
2024-05-28 16:54:58,484:INFO:SubProcess create_model() called ==================================
2024-05-28 16:54:58,484:INFO:Initializing create_model()
2024-05-28 16:54:58,484:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x0000018BFA8FD010>, 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 0x0000018BFD2A52D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-05-28 16:54:58,484:INFO:Checking exceptions
2024-05-28 16:54:58,484:INFO:Importing libraries
2024-05-28 16:54:58,484:INFO:Copying training dataset
2024-05-28 16:54:58,491:INFO:Defining folds
2024-05-28 16:54:58,491:INFO:Declaring metric variables
2024-05-28 16:54:58,494:INFO:Importing untrained model
2024-05-28 16:54:58,497:INFO:K Neighbors Classifier Imported successfully
2024-05-28 16:54:58,504:INFO:Starting cross validation
2024-05-28 16:54:58,505:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-05-28 16:55:00,847:INFO:Calculating mean and std
2024-05-28 16:55:00,848:INFO:Creating metrics dataframe
2024-05-28 16:55:00,850:INFO:Uploading results into container
2024-05-28 16:55:00,851:INFO:Uploading model into container now
2024-05-28 16:55:00,851:INFO:_master_model_container: 2
2024-05-28 16:55:00,851:INFO:_display_container: 2
2024-05-28 16:55:00,852:INFO:KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=5, p=2,
weights='uniform')
2024-05-28 16:55:00,852:INFO:create_model() successfully completed......................................
2024-05-28 16:55:00,925:INFO:SubProcess create_model() end ==================================
2024-05-28 16:55:00,926:INFO:Creating metrics dataframe
2024-05-28 16:55:00,933:INFO:Initializing Naive Bayes
2024-05-28 16:55:00,933:INFO:Total runtime is 0.13363826672236123 minutes
2024-05-28 16:55:00,937:INFO:SubProcess create_model() called ==================================
2024-05-28 16:55:00,937:INFO:Initializing create_model()
2024-05-28 16:55:00,938:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x0000018BFA8FD010>, 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 0x0000018BFD2A52D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-05-28 16:55:00,938:INFO:Checking exceptions
2024-05-28 16:55:00,938:INFO:Importing libraries
2024-05-28 16:55:00,938:INFO:Copying training dataset
2024-05-28 16:55:00,943:INFO:Defining folds
2024-05-28 16:55:00,943:INFO:Declaring metric variables
2024-05-28 16:55:00,946:INFO:Importing untrained model
2024-05-28 16:55:00,949:INFO:Naive Bayes Imported successfully
2024-05-28 16:55:00,959:INFO:Starting cross validation
2024-05-28 16:55:00,960:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-05-28 16:55:01,056:INFO:Calculating mean and std
2024-05-28 16:55:01,057:INFO:Creating metrics dataframe
2024-05-28 16:55:01,058:INFO:Uploading results into container
2024-05-28 16:55:01,059:INFO:Uploading model into container now
2024-05-28 16:55:01,059:INFO:_master_model_container: 3
2024-05-28 16:55:01,060:INFO:_display_container: 2
2024-05-28 16:55:01,060:INFO:GaussianNB(priors=None, var_smoothing=1e-09)
2024-05-28 16:55:01,060:INFO:create_model() successfully completed......................................
2024-05-28 16:55:01,123:INFO:SubProcess create_model() end ==================================
2024-05-28 16:55:01,123:INFO:Creating metrics dataframe
2024-05-28 16:55:01,129:INFO:Initializing Decision Tree Classifier
2024-05-28 16:55:01,130:INFO:Total runtime is 0.13692135016123452 minutes
2024-05-28 16:55:01,133:INFO:SubProcess create_model() called ==================================
2024-05-28 16:55:01,133:INFO:Initializing create_model()
2024-05-28 16:55:01,133:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x0000018BFA8FD010>, 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 0x0000018BFD2A52D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-05-28 16:55:01,133:INFO:Checking exceptions
2024-05-28 16:55:01,133:INFO:Importing libraries
2024-05-28 16:55:01,134:INFO:Copying training dataset
2024-05-28 16:55:01,141:INFO:Defining folds
2024-05-28 16:55:01,141:INFO:Declaring metric variables
2024-05-28 16:55:01,144:INFO:Importing untrained model
2024-05-28 16:55:01,146:INFO:Decision Tree Classifier Imported successfully
2024-05-28 16:55:01,153:INFO:Starting cross validation
2024-05-28 16:55:01,154:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-05-28 16:55:01,235:INFO:Calculating mean and std
2024-05-28 16:55:01,237:INFO:Creating metrics dataframe
2024-05-28 16:55:01,238:INFO:Uploading results into container
2024-05-28 16:55:01,239:INFO:Uploading model into container now
2024-05-28 16:55:01,239:INFO:_master_model_container: 4
2024-05-28 16:55:01,239:INFO:_display_container: 2
2024-05-28 16:55:01,240:INFO:DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
max_depth=None, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
monotonic_cst=None, random_state=123, splitter='best')
2024-05-28 16:55:01,240:INFO:create_model() successfully completed......................................
2024-05-28 16:55:01,297:INFO:SubProcess create_model() end ==================================
2024-05-28 16:55:01,297:INFO:Creating metrics dataframe
2024-05-28 16:55:01,304:INFO:Initializing SVM - Linear Kernel
2024-05-28 16:55:01,304:INFO:Total runtime is 0.1398220062255859 minutes
2024-05-28 16:55:01,307:INFO:SubProcess create_model() called ==================================
2024-05-28 16:55:01,307:INFO:Initializing create_model()
2024-05-28 16:55:01,307:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x0000018BFA8FD010>, estimator=svm, 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 0x0000018BFD2A52D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-05-28 16:55:01,307:INFO:Checking exceptions
2024-05-28 16:55:01,307:INFO:Importing libraries
2024-05-28 16:55:01,307:INFO:Copying training dataset
2024-05-28 16:55:01,313:INFO:Defining folds
2024-05-28 16:55:01,314:INFO:Declaring metric variables
2024-05-28 16:55:01,316:INFO:Importing untrained model
2024-05-28 16:55:01,320:INFO:SVM - Linear Kernel Imported successfully
2024-05-28 16:55:01,326:INFO:Starting cross validation
2024-05-28 16:55:01,327:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-05-28 16:55:01,392:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-05-28 16:55:01,393:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-05-28 16:55:01,393:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-05-28 16:55:01,399:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-05-28 16:55:01,399:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-05-28 16:55:01,412:INFO:Calculating mean and std
2024-05-28 16:55:01,413:INFO:Creating metrics dataframe
2024-05-28 16:55:01,414:INFO:Uploading results into container
2024-05-28 16:55:01,415:INFO:Uploading model into container now
2024-05-28 16:55:01,415:INFO:_master_model_container: 5
2024-05-28 16:55:01,415:INFO:_display_container: 2
2024-05-28 16:55:01,415:INFO:SGDClassifier(alpha=0.0001, average=False, class_weight=None,
early_stopping=False, epsilon=0.1, eta0=0.001, fit_intercept=True,
l1_ratio=0.15, learning_rate='optimal', loss='hinge',
max_iter=1000, n_iter_no_change=5, n_jobs=-1, penalty='l2',
power_t=0.5, random_state=123, shuffle=True, tol=0.001,
validation_fraction=0.1, verbose=0, warm_start=False)
2024-05-28 16:55:01,415:INFO:create_model() successfully completed......................................
2024-05-28 16:55:01,473:INFO:SubProcess create_model() end ==================================
2024-05-28 16:55:01,473:INFO:Creating metrics dataframe
2024-05-28 16:55:01,483:INFO:Initializing Ridge Classifier
2024-05-28 16:55:01,483:INFO:Total runtime is 0.14280013243357337 minutes
2024-05-28 16:55:01,487:INFO:SubProcess create_model() called ==================================
2024-05-28 16:55:01,488:INFO:Initializing create_model()
2024-05-28 16:55:01,488:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x0000018BFA8FD010>, estimator=ridge, 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 0x0000018BFD2A52D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-05-28 16:55:01,488:INFO:Checking exceptions
2024-05-28 16:55:01,488:INFO:Importing libraries
2024-05-28 16:55:01,488:INFO:Copying training dataset
2024-05-28 16:55:01,493:INFO:Defining folds
2024-05-28 16:55:01,493:INFO:Declaring metric variables
2024-05-28 16:55:01,496:INFO:Importing untrained model
2024-05-28 16:55:01,499:INFO:Ridge Classifier Imported successfully
2024-05-28 16:55:01,507:INFO:Starting cross validation
2024-05-28 16:55:01,508:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-05-28 16:55:01,590:INFO:Calculating mean and std
2024-05-28 16:55:01,591:INFO:Creating metrics dataframe
2024-05-28 16:55:01,593:INFO:Uploading results into container
2024-05-28 16:55:01,593:INFO:Uploading model into container now
2024-05-28 16:55:01,593:INFO:_master_model_container: 6
2024-05-28 16:55:01,593:INFO:_display_container: 2
2024-05-28 16:55:01,594:INFO:RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,
max_iter=None, positive=False, random_state=123, solver='auto',
tol=0.0001)
2024-05-28 16:55:01,594:INFO:create_model() successfully completed......................................
2024-05-28 16:55:01,653:INFO:SubProcess create_model() end ==================================
2024-05-28 16:55:01,653:INFO:Creating metrics dataframe
2024-05-28 16:55:01,660:INFO:Initializing Random Forest Classifier
2024-05-28 16:55:01,660:INFO:Total runtime is 0.14575105508168534 minutes
2024-05-28 16:55:01,663:INFO:SubProcess create_model() called ==================================
2024-05-28 16:55:01,663:INFO:Initializing create_model()
2024-05-28 16:55:01,664:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x0000018BFA8FD010>, estimator=rf, 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 0x0000018BFD2A52D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-05-28 16:55:01,664:INFO:Checking exceptions
2024-05-28 16:55:01,664:INFO:Importing libraries
2024-05-28 16:55:01,664:INFO:Copying training dataset
2024-05-28 16:55:01,670:INFO:Defining folds
2024-05-28 16:55:01,670:INFO:Declaring metric variables
2024-05-28 16:55:01,673:INFO:Importing untrained model
2024-05-28 16:55:01,676:INFO:Random Forest Classifier Imported successfully
2024-05-28 16:55:01,682:INFO:Starting cross validation
2024-05-28 16:55:01,683:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-05-28 16:55:02,159:INFO:Calculating mean and std
2024-05-28 16:55:02,160:INFO:Creating metrics dataframe
2024-05-28 16:55:02,162:INFO:Uploading results into container
2024-05-28 16:55:02,162:INFO:Uploading model into container now
2024-05-28 16:55:02,163:INFO:_master_model_container: 7
2024-05-28 16:55:02,163:INFO:_display_container: 2
2024-05-28 16:55:02,163:INFO:RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
criterion='gini', max_depth=None, max_features='sqrt',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
monotonic_cst=None, n_estimators=100, n_jobs=-1,
oob_score=False, random_state=123, verbose=0,
warm_start=False)
2024-05-28 16:55:02,164:INFO:create_model() successfully completed......................................
2024-05-28 16:55:02,223:INFO:SubProcess create_model() end ==================================
2024-05-28 16:55:02,224:INFO:Creating metrics dataframe
2024-05-28 16:55:02,230:INFO:Initializing Quadratic Discriminant Analysis
2024-05-28 16:55:02,230:INFO:Total runtime is 0.15525171359380083 minutes
2024-05-28 16:55:02,233:INFO:SubProcess create_model() called ==================================
2024-05-28 16:55:02,234:INFO:Initializing create_model()
2024-05-28 16:55:02,234:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x0000018BFA8FD010>, estimator=qda, 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 0x0000018BFD2A52D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-05-28 16:55:02,234:INFO:Checking exceptions
2024-05-28 16:55:02,235:INFO:Importing libraries
2024-05-28 16:55:02,235:INFO:Copying training dataset
2024-05-28 16:55:02,242:INFO:Defining folds
2024-05-28 16:55:02,242:INFO:Declaring metric variables
2024-05-28 16:55:02,244:INFO:Importing untrained model
2024-05-28 16:55:02,248:INFO:Quadratic Discriminant Analysis Imported successfully
2024-05-28 16:55:02,255:INFO:Starting cross validation
2024-05-28 16:55:02,256:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-05-28 16:55:02,317:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-05-28 16:55:02,317:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-05-28 16:55:02,317:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-05-28 16:55:02,317:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-05-28 16:55:02,318:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-05-28 16:55:02,318:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-05-28 16:55:02,318:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:935: UserWarning: Variables are collinear
warnings.warn("Variables are collinear")
2024-05-28 16:55:02,327:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: divide by zero encountered in power
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,327:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: invalid value encountered in multiply
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,328:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:963: RuntimeWarning: divide by zero encountered in log
u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
2024-05-28 16:55:02,328:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: divide by zero encountered in power
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,328:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: divide by zero encountered in power
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,328:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: divide by zero encountered in power
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,328:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: invalid value encountered in multiply
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,328:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: invalid value encountered in multiply
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,328:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: divide by zero encountered in power
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,328:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:963: RuntimeWarning: divide by zero encountered in log
u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
2024-05-28 16:55:02,328:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:963: RuntimeWarning: divide by zero encountered in log
u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
2024-05-28 16:55:02,328:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: invalid value encountered in multiply
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,328:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:963: RuntimeWarning: divide by zero encountered in log
u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
2024-05-28 16:55:02,328:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:963: RuntimeWarning: divide by zero encountered in log
u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
2024-05-28 16:55:02,330:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: divide by zero encountered in power
X2 = np.dot(Xm, R * (S ** (-0.5)))
in self.scalings_])
2024-05-28 16:55:02,330:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: invalid value encountered in multiply
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,330:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: invalid value encountered in multiply
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,330:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:963: RuntimeWarning: divide by zero encountered in log
u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
2024-05-28 16:55:02,330:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:963: RuntimeWarning: divide by zero encountered in log
u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
2024-05-28 16:55:02,331:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: divide by zero encountered in power
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,331:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: divide by zero encountered in power
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,332:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: invalid value encountered in multiply
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,332:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:963: RuntimeWarning: divide by zero encountered in log
u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
2024-05-28 16:55:02,332:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:963: RuntimeWarning: divide by zero encountered in log
u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
2024-05-28 16:55:02,332:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: divide by zero encountered in power
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,332:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: invalid value encountered in multiply
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,332:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: divide by zero encountered in power
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,332:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:963: RuntimeWarning: divide by zero encountered in log
u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
2024-05-28 16:55:02,332:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: divide by zero encountered in power
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,332:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: divide by zero encountered in power
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,333:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: invalid value encountered in multiply
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,333:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: invalid value encountered in multiply
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,333:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: invalid value encountered in multiply
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,333:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: invalid value encountered in multiply
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,333:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: invalid value encountered in multiply
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,333:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:963: RuntimeWarning: divide by zero encountered in log
u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
2024-05-28 16:55:02,333:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:963: RuntimeWarning: divide by zero encountered in log
u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
2024-05-28 16:55:02,333:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:963: RuntimeWarning: divide by zero encountered in log
u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
2024-05-28 16:55:02,333:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: divide by zero encountered in power
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,333:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:960: RuntimeWarning: invalid value encountered in multiply
X2 = np.dot(Xm, R * (S ** (-0.5)))
2024-05-28 16:55:02,334:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:963: RuntimeWarning: divide by zero encountered in log
u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
2024-05-28 16:55:02,334:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\discriminant_analysis.py:963: RuntimeWarning: divide by zero encountered in log
u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])
2024-05-28 16:55:02,350:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score
return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__
return self.score_func(y_true, y_pred, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_ranking.py", line 619, in roc_auc_score
y_score = check_array(y_score, ensure_2d=False)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array
_assert_all_finite(
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\validation.py", line 126, in _assert_all_finite
_assert_all_finite_element_wise(
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\validation.py", line 175, in _assert_all_finite_element_wise
raise ValueError(msg_err)
ValueError: Input contains NaN.
warnings.warn(
2024-05-28 16:55:02,350:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score
return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__
return self.score_func(y_true, y_pred, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_ranking.py", line 619, in roc_auc_score
y_score = check_array(y_score, ensure_2d=False)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array
_assert_all_finite(
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\validation.py", line 126, in _assert_all_finite
_assert_all_finite_element_wise(
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\validation.py", line 175, in _assert_all_finite_element_wise
raise ValueError(msg_err)
ValueError: Input contains NaN.
warnings.warn(
2024-05-28 16:55:02,350:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score
return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__
return self.score_func(y_true, y_pred, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_ranking.py", line 619, in roc_auc_score
y_score = check_array(y_score, ensure_2d=False)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array
_assert_all_finite(
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\validation.py", line 126, in _assert_all_finite
_assert_all_finite_element_wise(
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\validation.py", line 175, in _assert_all_finite_element_wise
raise ValueError(msg_err)
ValueError: Input contains NaN.
warnings.warn(
2024-05-28 16:55:02,351:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score
return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__
return self.score_func(y_true, y_pred, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_ranking.py", line 619, in roc_auc_score
y_score = check_array(y_score, ensure_2d=False)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array
_assert_all_finite(
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\validation.py", line 126, in _assert_all_finite
_assert_all_finite_element_wise(
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\validation.py", line 175, in _assert_all_finite_element_wise
raise ValueError(msg_err)
ValueError: Input contains NaN.
warnings.warn(
2024-05-28 16:55:02,351:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\pycaret\internal\metrics.py:204: FitFailedWarning: Metric 'make_scorer(roc_auc_score, response_method=('decision_function', 'predict_proba'), average=weighted, multi_class=ovr)' failed and error score 0.0 has been returned instead. If this is a custom metric, this usually means that the error is in the metric code. Full exception below:
Traceback (most recent call last):
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\pycaret\internal\metrics.py", line 196, in _score
return super()._score(
^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_scorer.py", line 350, in _score
return self._sign * self._score_func(y_true, y_pred, **scoring_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\pycaret\internal\metrics.py", line 144, in __call__
return self.score_func(y_true, y_pred, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\_param_validation.py", line 213, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_ranking.py", line 619, in roc_auc_score
y_score = check_array(y_score, ensure_2d=False)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\validation.py", line 1049, in check_array
_assert_all_finite(
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\validation.py", line 126, in _assert_all_finite
_assert_all_finite_element_wise(
File "c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\utils\validation.py", line 175, in _assert_all_finite_element_wise
raise ValueError(msg_err)
ValueError: Input contains NaN.
warnings.warn(
2024-05-28 16:55:02,359:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-05-28 16:55:02,359:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-05-28 16:55:02,359:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-05-28 16:55:02,359:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-05-28 16:55:02,360:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-05-28 16:55:02,360:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-05-28 16:55:02,361:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-05-28 16:55:02,361:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-05-28 16:55:02,361:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\metrics\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
2024-05-28 16:55:02,381:INFO:Calculating mean and std
2024-05-28 16:55:02,382:INFO:Creating metrics dataframe
2024-05-28 16:55:02,383:INFO:Uploading results into container
2024-05-28 16:55:02,384:INFO:Uploading model into container now
2024-05-28 16:55:02,384:INFO:_master_model_container: 8
2024-05-28 16:55:02,384:INFO:_display_container: 2
2024-05-28 16:55:02,384:INFO:QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,
store_covariance=False, tol=0.0001)
2024-05-28 16:55:02,384:INFO:create_model() successfully completed......................................
2024-05-28 16:55:02,442:INFO:SubProcess create_model() end ==================================
2024-05-28 16:55:02,442:INFO:Creating metrics dataframe
2024-05-28 16:55:02,450:INFO:Initializing Ada Boost Classifier
2024-05-28 16:55:02,450:INFO:Total runtime is 0.15891352891921995 minutes
2024-05-28 16:55:02,454:INFO:SubProcess create_model() called ==================================
2024-05-28 16:55:02,454:INFO:Initializing create_model()
2024-05-28 16:55:02,454:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x0000018BFA8FD010>, estimator=ada, 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 0x0000018BFD2A52D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-05-28 16:55:02,454:INFO:Checking exceptions
2024-05-28 16:55:02,455:INFO:Importing libraries
2024-05-28 16:55:02,455:INFO:Copying training dataset
2024-05-28 16:55:02,461:INFO:Defining folds
2024-05-28 16:55:02,461:INFO:Declaring metric variables
2024-05-28 16:55:02,464:INFO:Importing untrained model
2024-05-28 16:55:02,467:INFO:Ada Boost Classifier Imported successfully
2024-05-28 16:55:02,473:INFO:Starting cross validation
2024-05-28 16:55:02,476:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-05-28 16:55:02,523:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
warnings.warn(
2024-05-28 16:55:02,523:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
warnings.warn(
2024-05-28 16:55:02,523:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
warnings.warn(
2024-05-28 16:55:02,525:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
warnings.warn(
2024-05-28 16:55:02,526:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
warnings.warn(
2024-05-28 16:55:02,529:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
warnings.warn(
2024-05-28 16:55:02,530:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
warnings.warn(
2024-05-28 16:55:02,531:WARNING:c:\ProgramData\miniconda3\envs\kaggle_ML_env\Lib\site-packages\sklearn\ensemble\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
warnings.warn(
2024-05-28 16:55:02,744:INFO:Calculating mean and std
2024-05-28 16:55:02,745:INFO:Creating metrics dataframe
2024-05-28 16:55:02,748:INFO:Uploading results into container
2024-05-28 16:55:02,748:INFO:Uploading model into container now
2024-05-28 16:55:02,749:INFO:_master_model_container: 9
2024-05-28 16:55:02,749:INFO:_display_container: 2
2024-05-28 16:55:02,749:INFO:AdaBoostClassifier(algorithm='SAMME.R', estimator=None, learning_rate=1.0,
n_estimators=50, random_state=123)
2024-05-28 16:55:02,749:INFO:create_model() successfully completed......................................
2024-05-28 16:55:02,814:INFO:SubProcess create_model() end ==================================
2024-05-28 16:55:02,814:INFO:Creating metrics dataframe
2024-05-28 16:55:02,823:INFO:Initializing Gradient Boosting Classifier
2024-05-28 16:55:02,823:INFO:Total runtime is 0.16513894001642862 minutes
2024-05-28 16:55:02,827:INFO:SubProcess create_model() called ==================================
2024-05-28 16:55:02,827:INFO:Initializing create_model()
2024-05-28 16:55:02,827:INFO:create_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x0000018BFA8FD010>, estimator=gbc, 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 0x0000018BFD2A52D0>, model_only=True, return_train_score=False, error_score=0.0, kwargs={})
2024-05-28 16:55:02,827:INFO:Checking exceptions
2024-05-28 16:55:02,827:INFO:Importing libraries
2024-05-28 16:55:02,827:INFO:Copying training dataset
2024-05-28 16:55:02,833:INFO:Defining folds
2024-05-28 16:55:02,833:INFO:Declaring metric variables
2024-05-28 16:55:02,836:INFO:Importing untrained model
2024-05-28 16:55:02,840:INFO:Gradient Boosting Classifier Imported successfully
2024-05-28 16:55:02,848:INFO:Starting cross validation
2024-05-28 16:55:02,849:INFO:Cross validating with StratifiedKFold(n_splits=10, random_state=None, shuffle=False), n_jobs=-1
2024-05-28 16:55:03,171:INFO:Calculating mean and std
2024-05-28 16:55:03,171:INFO:Creating metrics dataframe
2024-05-28 16:55:03,173:INFO:Uploading results into container
2024-05-28 16:55:03,173:INFO:Uploading model into container now
2024-05-28 16:55:03,174:INFO:_master_model_container: 10
2024-05-28 16:55:03,174:INFO:_display_container: 2
2024-05-28 16:55:03,175:INFO:GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='log_loss', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=100, n_iter_no_change=None,
random_state=123, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False)
2024-05-28 16:55:03,175:INFO:create_model() successfully completed......................................
2024-05-28 16:55:03,238:INFO:SubProcess create_model() end ==================================
2024-05-28 16:55:03,238:INFO:Creating metrics dataframe
2024-05-28 16:55:03,246:INFO:Initializing Linear Discriminant Analysis
2024-05-28 16:55:03,246:INFO:Total runtime is 0.17218267917633057 minutes
2024-05-28 16:55:03,250:INFO:SubProcess create_model() called ==================================
2024-05-28 16:55:03,250:INFO:Initializing create_model()